The concept of fake news has always been a staple of culture, but it became part of the popular lexicon during the Trump administration in the United States. 

Media giants came under criticism for skewing the narrative to meet a specific agenda. As a result, news agencies and media companies had to invest in fact-checking technology to authenticate every news item, including images, statistics, and video content.

Since most news companies use images to report the news, they need AI-powered fake photo detection tools to see if someone has doctored the photo or not.

This article explores how to detect a photoshopped image and also highlights how a fake picture can fuel fake news reporting.

How do fake photos fuel the spread of fake news?

In a DIY social experiment conducted during the pandemic, photographers from Ritzau Scanpix showed ways images can mislead news consumers. 

Although the photographers captured the same subjects from multiple angles using different lenses, the resulting pictures told contrasting stories.

A photographer takes pics of people in public from 2 perspectives and it shows how easily the media can manipulate reality. There is no social distance during the coronavirus pandemic.

Let’s take a look at the situation from the other side:

This experiment exposed one of the ways that news sites and yellow pages can manipulate their audience. 

But that’s not even half of it; let’s explore other ways a fake image can propel fake news stories.

• Evoke emotions

News websites rely on words to convey their narratives, but a striking image drives home the point faster because humans are visual beings. 

If you see an image of a mother sitting in a dilapidated house with a crying baby in her arms, the last thing on your mind would be to verify if it is a fake image. 

Such fake photos force readers to empathize with the people in the picture and invest in their story. Since the reader is now emotionally invested, the veracity of the facts becomes secondary.

• Reinforces prejudices

During the BLM protests in 2020, fake news portals worked overtime trying to implicate the protestors in the looting. Sometimes, they photoshopped gang-related tattoos on the body of protesters and labeled them “marauders.” 

• Exploit the consumer’s POV

Most people are suspicious of stock images and high-res photos because they look staged. But when a picture comes from a low-res camera, like an iPhone or Android, it feels like a regular citizen captured it. And since the fake image looks natural and ‘unstaged’, it helps drive the fake news agenda to the reading audience.

• Fosters microtargeting

Fake news agencies often target conspiracy theorists — most of whom are on Reddit and 4Chan. These niche consumers peddle in rumors without spending ample time to research their sources. As a result, it is easier to target them with a photoshopped image or a fake photo.

• Provides ammo for pseudo-experts

In the age of social media, many self-proclaimed experts share their content with unsuspecting consumers. If the reader trusts the so-called expert, they won’t bother to fact-check their images. After all, why would an expert need to manipulate images?

Viral photos that were fake

Sometimes, you see a photo and think: “Is this image real or fake?”

We have all pondered on this question several times. To help you understand how fake images impact the spread of fake news, let’s check out some viral photos that were actually fake.

This picture of a Frozen Venice is actually an edit of the Lake Baikal in Russia.

The “Frozen Venice” picture went viral because it showed a part of Venice that most tourists could not recognize — mainly because the image is fake. The artist superimposed a picture of the frozen Lake Baikal on Venice street.

To be fair, this image adds an extra layer of beauty to the already enchanting city of Venice. Unfortunately, this image can cause irreparable harm in the hands of fake news merchants.

Climate change deniers could use this photo as propaganda to repel the fact that the globe is getting warmer. After all, Venice now looks like a town in the ice-filled Baikal region.

Sharks don’t hang out in hotel lobbies!

Even though sharks don’t hang out in hotels (obviously), pseudo-experts can use this image to spread fake news.

For instance, climate change advocates can stir people into action by claiming that temperatures have risen so high that sharks now have to swim into hotels to shelter from hurricanes.

In 2020, Trump ran a Facebook ad using a 2014 protest photo from Ukraine.

During the protests in 2020, President Donald Trump’s Facebook account posted a campaign ad that depicted the anti-police violence from protesters. However, the image from the ad came from the 2014 Euromaidan protests in Ukraine. Whether this fake news content was a mistake or not, such a reputable institution could have benefited from a fake photo detection software.

Why should news media websites check photo authenticity before publication?

Journalistic integrity demands that publishers should detect fake photos online before sharing them with their consumers.

Beyond journalistic integrity, here are other reasons why news sites should avoid fake photos like the plague.

• Avoid litigation

According to Reuters, a young man from Kentucky sued CNN for defamation of character after the world-renowned news network posted an image of him allegedly confronting a native-American activist. The 275 million USD lawsuit is still pending.

If you want to avoid these massive lawsuits, learn how to detect a fake photo.

• Protect your company’s reputation

According to Statista, people are losing trust in news websites because of deep fakes and manipulated images. Consumers now consider most news channels as fake news or biased.

Source: Statista

The “fake news” label will besmirch your reputation, no matter how trivial the case. Once consumers discover that you have reported the news using manipulated fake images, they will approach every piece of reporting with skepticism.

• Curb mass misinformation

Experts from Cambridge Analytica claim that fake photos and news materials swayed the results of the 2016 election. Also, the COVID-19 pandemic caused mass hysteria because news media websites spread unverified images and information. 

Source: Statista

Besides, data from Statista shows that confidence in online news sites is waning due to misinformation. 

Nevertheless, news agencies can maintain a stellar reputation by conducting additional image and fact checks before reporting.

How to check if this image is real or fake

Differentiating a fake photo from a real one has become a herculean task as advanced online photo manipulation tools are now available online. But news agencies that know how to tell if an image is fake can protect their reputation and maintain readers’ trust.

So, let’s check out how to see if a photo is fake or real.

• The eye test — review the images for irregularities and skewed perspectives. A microscope can help you spot rough edges and non-matching color schemes.
• Tin Eye — searches images on multiple sources and uses the metadata to find the original.
• Google Reverse Image Search — allows users to upload images to verify their authenticity.
• AI-powered tools — like Jigsaw’s Project Assembler — allows users to fact-check images using machine learning algorithms. This experiment is no longer available, but similar products exist on the market today. 

Fake photo detection using AI, ML technologies 

As we mentioned in the previous paragraph, modern tools for photo verification use artificial intelligence and machine learning technologies to provide accurate image detection.

Here is how to tell if a picture is fake using a ML-based fake photo detection tool:

1.Upload the image.

2.Photo validation process using AI, ML technologies:

• Testing pixels for authenticity. In other words, the solution detects any changes in a pdf/jpg file. This product answers the question “does this file was photoshopped or not
• File metadata checking. Extract metadata recorded behind the files, ranging from file size, data, geolocation and modification history to the software tools used to create them

3.The results show if the image is fake or real.

Metadata checking result example

Use ML-based fake photo detection together with other tools to authenticate images for your news websites.

If you run a news organization, you realize all too well that fake photos can be prominent drivers of fake news. But there are too may consequences – from distorted narrative to the risk of million-dollar lawsuits.

Using a fake photo detector to authenticate pictures before posting them, you will protect your company’s reputation and avoid numerous issues.

References 

• Perceived objectivity of mass media in the US 2020
• Confidence in ability to recognize made-up news US 2019 
• The Big Viral Moments of 2020 That Were Totally Fake 
• Explained: What is Fake news? | Social Media and Filter Bubbles 
• Stopping the spread of fake news through photographs 
• In the age of fake news, these digital watermarks could stop the spread of fake images 
• How false information spreads 
• Six Fake News Techniques and Simple Tools to Vet Them 
• How is Fake News Spread? Bots, People like You, Trolls, and Microtargeting | Center for Information Technology and Society – UC Santa Barbara 

The post Fake Photo Detection for News Media Websites appeared first on SDK.finance - White-Label Digital Banking Software.

If you’ve used any digital payment service, you’ve probably needed to verify your identity and provide proof of address.

However, criminals have figured out ways to fake proof of address documents for a low fee. These counterfeits are so similar to the original that only advanced AI-powered tools can detect them. 

So, this article discusses proof of address, why it is needed, types of documents used for address verification and how to fight fake proof of address documents.

What is proof of address?

Proof of address is a document that verifies where you live — a proof of residence. The document must show the following:

• Your full name similar to other government-issued documents
• The date of issue (and expiry, if applicable) 
• The issuing authority with the corresponding logo
• The specific address of residency.

Banks, payment systems, credit unions and other financial institutions request proof of address as a security measure to confirm that you are not lying about where you live.

Proof of address differs from “proof of identity” in the sense that one confirms your residency while the other proves that you are an actual living entity. Besides, proof of identity documents must have your picture, while a proof of address document mustn’t contain your image. 

Why is proof of address a necessity?

Now that you know what qualifies as proof of address, we can explore why it is necessary. But before we dive into the topic, let’s go through some numbers.

A report by the Alte Group estimates potential losses of around 721.3 billion USD from fake proof of address and other identity fraud cases. 

According to the U.S. Census Bureau, 14% of the people living in the U.S. move within the country annually. This constant movement of people makes it difficult to track every citizen’s location or residency.

Source: U.S. Census Bureau

Similarly, the Federal Trade Commission received over 2.1 million reports of fraud in 2020 alone. Although most of these fraud cases were related to COVID-19, 53% of respondents still complained about fake proof of address bills.

And this brings us back to the necessity of fake proof of address verification. 

• Mitigating risks

The digital world is fraught with fraudulent activity and various forms of identity crimes. As a result, every financial institution, government agency, or payment management platform should mitigate these risks by using proof of address verification.

• Regulations 

US citizens must apply for a change of address within 30 days of moving to a different locale. However, countries like Hong Kong don’t mandate that citizens provide proof of address to access services. So, find out the law of the land and abide by them.

What industries need proof of address verification?

As mentioned earlier, financial institutions and government agencies require verification for proof of address documents. But other industries also ask for these documents. 

Here they are:

• Banking 

When opening an account, no banker will “take your word for it” when it comes to your address. You need to provide a government-issued document that proves your residency. 

• Online payment platforms 

Payoneer requests documents from users to verify their identity, address, source of income, and business. Without a valid proof of address, you cannot access the services of such online payment platforms.

• Loan platforms

When applying for FHA, VA, USDA, and conventional loans, you need to verify your identity and provide proof of address. These documents help the lending agencies to stop fraud and protect people’s identities.

• Travel 

Companies like Airbnb request proof of address documents from customers to verify their identity. These documents help them keep track of homeowners who list fake addresses for their property. 

• Government agencies 

Visa applicants need to provide bank statements and proof of address to process their applications. 

Types of documents used for proof of address

To prove your address, you need a document that verifies your residency and is recognized by the authorities. However, documents like handwritten bills and payment receipts are not proof of address documents.

So, let’s discuss these proof of address documents.

National ID or International passport

National IDs verify your identity and residency in one fell swoop. Both documents are internationally recognized because they are government-issued. 

But often, the registered place of residence on the national ID and the bearer’s actual residential addresses are different. That is why companies require other documents for proof of address.

Bank statement

Bank statements are also excellent proof of address documents because they show your registered address on the receipt. Visa applicants and loanees need bank statements to prove their residency.

Utility bill

When you pay for gas, electricity, water, and other utilities, the receipt always shows your address. Even if you own multiple properties, paying for these amenities means that you are the legal occupant.

You can also present your council or municipal tax bill as proof of address for banking services. Ultimately, online payment platforms accept insurance receipts for your car and home since they specify your address.

Driver’s license

The driver’s license is quite tricky. In some countries, you can only prove your identity with a driver’s license and not your address. But since most adults have driver’s licenses, the document is easy to counterfeit.

Residence permit

Just like international passports, resident permits can serve as proof of address since they contain your official residence. Besides, some countries demand residence permits from foreigners since not all international passports contain residential addresses.

Government-issued rental agreement

Homeowners and rental agencies often issue rental agreements. But not every company or government agency accepts these documents without a government stamp or logo.

Problems with fake utility bills and fake bank statements 

Of all the proof of address verification methods mentioned above, utility bills and bank statements are the most widely accepted. 

However, these documents are now easy to manipulate using advanced digital tools, commonly known as fake utility bill generators. The Internet is full of websites that offer “fake utility bill generator” or “fake utility bill for proof of address”.

How does this fake utility bill generator work? 

The utility bill maker generates a document with all the necessary stamps and logos to make it look legit. To the naked eye, these forgeries look legit. But advanced fake utility bill detection engine can spot the discrepancies between genuine and counterfeit documents.

And this same principle applies to using fake bank statements as proof of address. Any fake bank statement generator online can falsify your document instantly and deliver the output in any desired format.

Fake utility bills and bank statements are a great problem for banks and financial institutions. Since the verification process is complicated and no comprehensive data exists on these forgeries, it is very difficult to assess financial losses due to fake proof of address documents.

How does a fake proof of address detector work?

The central focus of the verification exercise is to check if the address on the utility bill is authentic and if the owner actually lives there. So, let’s go through how a typical utility bill verification process works.

Allow your customers to submit their utility bills, bank statements, rent agreements and other types of proof of address documents by scanning or taking a photo of these documents. It is a convenient route:

1. For the proof-of-address, the user first captures a picture of a valid document. 
2. The system checks if the address on the document is not photoshopped or forged (our AI-powered Fake Utility Bill Detector checks popular file formats (PNG, JPEG, PDF, and others) for signs of manipulation).
3. The results appear instantly: real or fake.
4. No extra steps for you and your customer.

As we explore the rise of fake proof of address verification and the innovative AI-powered and ML-based tools combating it, it’s evident that robust transaction management and stringent financial compliance measures are crucial. Now, let’s check out the SDK.finance’s demo video to explore how SDK.finance provides a comprehensive view and control over client transactions, along with advanced AML and fraud prevention features, empowering institutions to stay ahead in the fight against financial crime:

References:

• How to Nail Proof of Address in Your Address Verification Process 
• Anyone Can Forge Utility Bills… This is How You Safely and Accurately Verify Residency – Evident | Automated Verification to Reduce Risk
• Know your customers, and now, know where they are with Veriff – Veriff
• Quick Address Verification services with a free trial
• Direct-From-Source Data Tightens Security and Compliance for Online Gambling ID Verification 
• This Type Of Identity Fraud Is Surging: How To Stay Protected 
• Another Epidemic: Identity Theft 

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Identity fraud is one of the most frustrating things banks and payment processors have to deal with.

According to Javelin, US businesses suffered almost $17 billion worth of losses due to identity fraud. By mistaking a criminal for a genuine client, a bank or payment processor might unwittingly aid money laundering, tax evasion, and terrorist activities.

The traditional document validation process was slow, unreliable, and difficult to scale. It introduced a lot of friction into the registration process while not guaranteeing perfect security. And as your business grew, you had no choice but to hire more and more security specialists to keep up. Which could get incredibly costly. 

Thankfully, modern advances in machine learning (ML) and artificial intelligence (AI) technologies can help solve all these issues.

Read on to find out everything you need to know about document fraud and how to detect fake documents with modern AI-based document verification systems.

What is document fraud?

Identity document fraud is the criminal process of buying, selling, or manufacturing counterfeit IDs in order to perform illicit payment, immigration, company registration, or other types of criminal activities (according to the Interpol)

In order to perform identity fraud, the criminals may use both counterfeit documents and genuine documents,

Listed below are the most common types of identity documents used for fraud (source – Interpol):

What are fraudulent documents used for?

Fraudulent documents are used to commit a wide variety of crimes. These can range from money laundering, gaining illicit employment, and other types of financial crimes to human trafficking and terrorism-related activities.

What are top 4 most common types of counterfeit documents?

Document fraud can be carried out in a number of ways. The four most common types of counterfeit documents are pseudo documents, false documents, modified documents, and image fraud.

Pseudo documents

This is the most common type of fake identity document. As we already mentioned above, pseudo documents are completely fake documents. They often have important safety features missing, including watermarks and holograms. Despite this, the ID may look somewhat official and will typically purport to come from a distant land, which could be enough to fool inexperienced workers.

False documents

False documents are not counterfeits or reproductions. Instead, they are genuine documents that were issued by real government institutions. A genuine ID becomes a false document when a third party uses it for illicit activities.

For example, a criminal might attempt to use a stolen passport to take out a series of bank loans in the victim’s name. They will then pocket the money and disappear, leaving the victim and the bank left to deal with the losses.

Modified documents

Modified documents are a cross between pseudo documents and false documents. The base of a genuine personal identification document, such as a passport or an ID card, is taken and then altered to display other details. The base can come from either a document issued in the name of another person or from blanks stolen or leaked from the government-contracted printer. 

Image fraud

If you work at a bank or payment processing company that allows users to register remotely, then you know that having to deal with image fraud is an unfortunate consequence of providing services over the internet. 

Digital authentication of documents is difficult because the end user only has to provide an image of their ID, rather than the physical document itself. 

To commit fraud, criminals will either take pictures of modified documents in a light that hides their most noticeable flaws or use a piece of image manipulation software, such as Adobe Photoshop, to make alterations to a photo of a genuine document.

Check this article to get more information about fintech software development challenges and solutions.

Document fraud statistics

The world of document fraud is a rapidly changing one. Advances in the realms of document fraud detection and prevention make previously popular methods outmoded overnight.

This forces criminals to invent new avenues of attack.

To which government agencies have to reach in turn.

And so on.

Most commonly faked documents

According to TrustID, the most commonly faked type of documents are passports and ID cards. Fake visas and residence permits are much rarer.

And despite all the myths, driving licenses are faked much less frequently than other types of documents.

In terms of trends, ID card fraud became 33% more prevalent last year and fake visas became twice more common (possibly due to Brexit).

Top 10 countries with most faked documents

The following countries’ documents are faked most often:

1. France (15.1%)
2. Portugal (15.0%)
3. Nigeria (11.1%)
4. Spain (8.4%)
5. Great Britain (7.1%)
6. India (6.9%)
7. Italy (6.8%)
8. Belgium (6.3%)
9. Netherlands (5.0%)
10. Germany (1.9%)

As can be seen from the list, the vast majority of documents are faked in the European Union. The only non-EU countries in our top 10 are Nigeria (#3) and India (#6).

This means that financial service providers who work in Europe must pay special care when dealing with identity checking.

How ML and AI are improving document verification?

Machine learning and artificial intelligence is improving document verification in a variety of ways.

Traditionally, document verification had to be carried out by hand, with trained security specialists looking over every application. This method has, of course, very slow, inaccurate, and expensive.

No matter how big your security team, processing thousands of applications per day can never be a fast and convenient process for the user.

Humans are human, and even the most experienced security experts will make mistakes. And that is before we even mention the fact that scaling manual document verification is an incredibly costly and unsustainable endeavor.

Thankfully, machine learning and artificial intelligence technologies are coming in to help financial institutions by combatting document fraud in a much more efficient way. A well-trained ML identity fraud prevention algorithm can process thousands of documents per second, filtering out the cases which truly require the attention of your team.

Thanks to these AI-based algorithms, you can reduce staff costs while improving both processing speeds and security.

How AI document verification works

Integration

The first step to document verification using AI systems is integrating the software into your payment processing systems.

Low-friction document verification

When document verification is required, the new client will be asked to upload a picture of their government-issued document and a selfie taken directly via the app.

AI document scanning

The fraud detection software then uses OCR (Optical Character Recognition) algorithms to read the data from the document and identify any discrepancies with the typography that could indicate that the document has been modified.

Simultaneously, the AI system compares the document against a database of known real documents and checks for all visible forgery marks. 

Facial data analysis

Once the document is verified as genuine, the algorithm will use a sophisticated facial recognition system to make sure that the customer’s face is the same exact one that appears on the document.

Verdict

A few seconds after the data has been submitted for review, the system will either automatically confirm the verification attempt as genuine, block it as fraudulent, or send it to your security team for further review.

Benefits of AI document verification vs. manual

AI-Powered online document verification solutions

Want to take advantage of the latest developments in AI document fraud verification? AI-powered online document verification solutions can help lower risks of suffering negative consequences related to document fraud.

Key features of a great image forgery tool

• Document authentic and genuine verification.
  AI, ML models ensure that the document submitted by the potential customer is authentic.
• Identifying the extracted format of the documents.
  AI-backed document validation solutions are capable of identifying the extracted format of the documents.
• MRZ (Machine-Readable Zone) code validation.
  Performs the evaluations to check if the field is edited or tampered with.
• Government microprint verification.
  AI-powered documentation verification solution checks the microprint for validating the authenticity of the document.
• Testing pixels for authenticity.
  Detection of signs of forgery, even if it is capable of detecting minor changes of a single-pixel.
• File metadata checking.
  Extract metadata recorded behind your files, ranging from file size and modification history to the software tools used to create them.

Read this article to get information about key players in credit card fraud detection.

Discover how SDK.finance’s system simplifies KYC checks (like document verification) for new and existing users, and helps to manage all aspects of client management within one centralized platform:

Most popular image forgery detection tool use cases

• Finance (banks, payment processors and credit companies)
• Government institutions
• Human Resources Departments
• Travel (identity and health documents verification for airlines, airports, hotels, travel agencies and other travel partners the means to perform automatic identification and COVID-19 document verification)
• Real estate agencies (verification of the authenticity of passports, documents of ownership of real estate in the process of sale or lease
• Photo fake detection (detect fake photos in everyday life).

Employing an automated tool you get the chance to decrease manual effort in document verification and minimize the number of fraudulent documents in your business everyday operations.

The post How AI Document Verification Technology Can Help Combat Document Fraud appeared first on SDK.finance - White-Label Digital Banking Software.

While machine learning may seem incomprehensible, it is much easier to understand than you might think. In our previous article ‘All You Need to Know About Machine Learning Based Fraud Detection Systems‘ we talked about machine learning vs. rule-based systems in fraud detection and the benefits of using machine learning in fraud detection. In this article, we will take a look at what goes into building a machine learning-based system and the most common types of ML models that are used in payment fraud detection.

Creating a Machine Learning Payment Fraud Model

Dataset Preparation

Before you can do anything else, you need to prepare a dataset. In most cases, the data points you use will need to be manually labelled as either genuine or fraudulent.

Your archive of past payments, which has already been labelled by your security team, will make for a perfect dataset.

The bigger (and higher quality) the pool of data you will train your neural network on, the more accurate and efficient your system will be.

Introduction of Features

Next, you will need to introduce features, which are data points describing customer behavior and giving you a clear signal that something is wrong with the transaction.

The most common features in payment processing are:

• Customer identity
• Order information
• Payment method
• Location information

Having a large, pre-prepared corpus of fraudulent payment features will help your system identify fraudulent payments with ease from the very start.

Algorithm Training

After features are introduced, you need to train the algorithm on a training set of historical data. Once the training phase is complete, you will have a finished model that can start identifying fraudulent payments.

The higher quality the training set, the better and more accurate the system will be.

Continuous Improvement

During the first stage of its operation, your security team will need to monitor the system and make sure that it is performing the way it should.

A great security team will also be able to log all of the algorithms’ mistakes and errors. These will be labelled and added to the dataset that will be used to train a new version of the model.

As a result of these actions, the system will become better and better as time goes on.

As you can see, training up an ML algorithm is by no means easy and can be very expensive in terms of both man-hours and funds. Thankfully, third-party solutions exist to help businesses take advantage of the latest developments in AI technologies at a minimal cost.

Top 5 Most Popular Machine Learning Models

The five most popular ML models are random forest, support vector machine, k-nearest neighbors, neural networks, and deep neural networks. Let’s take a bird’s-eye view at them all.

Random forest

One of the best examples of intuitive naming in machine learning, a random forest is essentially a collection of separate decision trees that are “grown” using the training set.

When a piece of data needs to be classified, each of the decision trees will tell the forest how close it is to its class. The forest then picks the tree that gave the new piece of data the most votes.

In the context of payment fraud, you can create a forest filled with various common and not so common types of transactions. When a new transaction occurs, the random forest will immediately tell your team what type of transaction it is.

Support vector machine

Another popular classification method is the support vector machine (SVM). In this method, each feature is presented as a coordinate point. Each data item is plotted as a point on an axis of features. 

If we wanted to create a classification of all transactions based on two variables, such as account age and payment sum, we’d plot the two variables in a 2D space where each piece of data would have two coordinates. 

Large payments from a new account would be labeled as high-risk. Smaller payments from older accounts would be labeled as being safer.

K-nearest neighbors

Another simple, yet effective algorithm is K-nearest neighbors. It stores all available cases and then classifies all new cases via a majority vote from its K neighbors. The case assigned to a new class will be the most common among its K nearest neighbors as measured by a distance function.

This system is very similar to the way we intuitively classify things as people. Let’s say your bank gets a new corporate client. This client is a member of various industry organizations, has accounts in a wide variety of other banks, and regularly does business with many of your best clients. Birds of a feather flock together. You and your security team instinctively know that this client is legitimate, because their “neighbors” are trustworthy.

Neural networks

Neural networks are based on a model of the human brain. They are designed to recognize patterns in raw data. Thanks to this, they can help businesses classify and cluster vast amounts of information quickly and efficiently.

Simply supply a labeled data set to a neural network and it will be able to group future data according to similarities without the need for you to add any additional features (although features can still be used to reinforce the model). 

Deep neural networks

White normal neural networks are great for a lot of classifying work, they can never be creative. That’s where deep neural networks come in.

Being a much more complicated and many-layered system than regular neural networks, deep neural networks can also do a lot more. Companies use them for such things as analytics, predicting future outcomes, solving creative-thinking tasks, and even creating art.

What’s more, deep neural networks do not need as much guidance as their regular neural networks and can even function with completely unlabeled data sets. Which means that you can use deep neutral networks to solve problems you yourself don’t know how to solve.

Examples of deep neural networks include the Deep Dream Generator, YouTube and Tik-Tok content-serving algorithms, as well as Sony CSL’s music-creation algorithm.

Daddy’s Car, a Beatles-style song generated by the Sony CSL AI

Deep learning fraud prevention systems are still somewhat rare, but the technology has a lot of potential to revolutionize the way financial systems work and create a much safer operating environment for financial institutions and their clients.

Watch the SDK.finance’s demo video to explore how SDK.finance provides a comprehensive view and control over client transactions, along with advanced AML and fraud prevention features, empowering institutions to stay ahead in the fight against financial crime:

Final Words

Machine learning-based fraud prevention is an exciting new development in the prevention of illicit payments.

By replacing outdated rule-based systems with modern machine learning solutions, banks and payment processors can reduce the losses they incur due to fraud, lower their security system-related expenses, and reduce payment friction for their clients.

As for the companies that are hesitant to switch, the costs associated with maintaining their legacy payment fraud systems will eventually outweigh the investment necessary to introduce the more modern system. It is predicted that all major financial industry players will eventually transition to machine learning-based payment fraud prevention systems.

The post How to Detect Payment Fraud Using Machine Learning? appeared first on SDK.finance - White-Label Digital Banking Software.

Fraud detection algorithms are an integral part of all modern financial systems.

They are an invaluable tool that protects financial institutions from chargebacks, investigation fees, government fines, and reputational damage. A good prevention and detection system can help your business in a variety of ways. It can filter out the vast majority of fraudulent transactions, freeing up the resources of your security team.  

But not all fraud detection systems are created equal. Credit card fraud detection using machine learning is an exciting new development in the sphere of identifying payment anomalies. It allows financial institutions to block fraudulent transactions with never before seen accuracy. It helps them reduce the number of false positives for genuine transactions. And it does so while reducing overall IT costs.

So what exactly is machine learning-based payment fraud prevention? Is it right for your financial institution? How expensive is it? And is there a future for rule-based systems?

Read on to find out.

Fraud Statistics

As we’ve already discussed in our article on payment fraud, illegitimate payments are becoming an ever-increasing concern for financial institutions. The total amount of losses incurred by businesses due to payment fraud have more than tripled in just the last decade. In 2011, $10 billion was lost due to illicit payments. In 2020, this figure climbed to more than $32 billion, with 6.83 cents out of every $100 being lost to fraud.

And, according to industry analysts, the costs of payment fraud will only continue to increase. It is projected that fraud losses will rise a further 25% and exceed $40 billion by 2027. And that is not even mentioning the ever-present chargeback fees, investigation fees, and fines from governments and credit card issuers.

One of the most popular and effective ways banks and payment processors deal with payment fraud is through fraud detection algorithms. In 2020, detection algorithms were implemented by more than 60% of all banks.

The two principal routes a business can go about transaction monitoring is by implementing either a rule-based or a machine learning-based system. Let’s look at what these systems are and which option is best for your business.

Machine Learning vs. Rule-Based Systems in Fraud Detection

Rule-based systems and fraud detection machine learning algorithms are two completely different approaches to combating illicit payments.

Rule-based systems are more traditional. They are configured by internal security teams to help automate procedures and checks that a human expert would typically handle. This is by far the most common system out there today. Read this article to get more information about credit card fraud detection. 

Machine learning (ML) systems are a much more modern and efficient way of dealing with safety procedure automation. A lot of major industry players have had outstanding success with ML algorithms, but they are still much less common than rule-based systems.

Rule-Based Systems

Rule-based systems use pre-programmed behavior scripts to identify suspicious transactions. This is similar to the way classic antivirus software functions.

Once a behavior linked to a fraudulent payment becomes known by the bank’s security team, they implement a rule to combat it. The criminals will then try to outsmart the rule.

Banks and payment processors using rule-based systems can only react to an exploit once their security team detects it. Naturally, by that point, at least some fraudulent payments have been committed and at least some losses have been incurred.

On top of that, rule-based systems become more and more expensive to maintain as they grow larger and more complex. Their proper functioning requires the hiring of a large team of IT specialists.

Machine Learning-Based Fraud Prevention Systems

The love child of time-tested fraud detection and modern machine learning technologies, ML systems offer a much more proactive and flexible approach to identifying fraudulent payments. 

Machine learning systems enable financial institutions to detect irregularities and identify subtle changes in large data sets in a much more precise and granular way than was previously possible.

Rather than becoming more expensive to maintain with time, machine learning systems tend to become more effective and efficient without requiring any additional specialist labor.

What Are the Benefits of Using Machine Learning in Fraud Detection?

In order to understand the key benefits of ML fraud detection, let’s look at the most common frustrations the security departments of the world’s leading financial institutions have when dealing with rule-based approaches and how they are solved by machine learning.

Effortless Scaling

As criminals work their way around old rules, security teams must continue to introduce new, more sophisticated rule sets. As a result of this, rule-based systems keep getting larger, heavier, and slower as time goes on.

Additionally, as there is only so much granularity you can introduce into a rule, human-run reviews become needed more often, putting added stress on your security team.

High-quality financial fraud detection machine learning systems, on the other hand, tend to age like fine wine.

As the system is exposed to more data and new instances of fraud, it only becomes better at separating fraudulent payments from genuine transactions. This means that it can potentially require fewer manual reviews per million transactions as time goes on.

Reducing the Number of False Positives

As rule-based systems become more and more complex, another important problem surfaces. Using heaps of rules layered upon rules results in more and more genuine transactions being blocked by the system.

While these rules may save you some money on chargebacks, they will introduce added payment friction to your clients. As a result of this, some of them may turn to one of your competitors for their payment needs.

Being much more granular and precise, machine learning algorithms do not suffer from this problem.

Flexible Outcomes

As rules are based on yes/no answers, they necessitate fixed outcomes. If you’ve spent any length of time in the financial services industry, you know that it is anything but static.

Let’s say your fraud team analyzes all fraud cases for the last year and imposes a rule that scrutinizes all orders above $300.

Years pass. You acquire new business clients. Many of them serve an upmarket audience. Now, orders of over $750 are the norm. Unless you want to alienate your new clients and their wealthy customer base with increased payment friction, you will need to amend the rule or make a set of exceptions. 

At the same time, you receive an influx of customers from a less developed region who like your services due to their low transaction costs. Now low-value transactions are more common. As a result, fraudsters use smaller payments of $20 to $50 to blend in with them. The $300 rule won’t do you any service here, either.

Payment fraud detection machine learning systems, on the other hand, are much more flexible in nature. They can understand data sets in a way no human ever could.

Check out the SDK.finance’s demo video to explore how SDK.finance provides a comprehensive view and control over client transactions, along with advanced AML and fraud prevention features, empowering institutions to stay ahead in the fight against financial crime:

Final Words

Machine learning-based fraud prevention is an exciting new development in the prevention of illicit payments.

By replacing outdated rule-based systems with modern machine learning solutions, banks and payment processors can reduce the losses they incur due to fraud, lower their security system-related expenses, and reduce payment friction for their clients.

As for the companies that are hesitant to switch, the costs associated with maintaining their legacy payment fraud systems will eventually outweigh the investment necessary to introduce the more modern system. It is predicted that all major financial industry players will eventually transition to machine learning-based payment fraud prevention systems.

The post Machine Learning Based Fraud Detection Systems in Finance: All You Need to Know appeared first on SDK.finance - White-Label Digital Banking Software.

Machine learning use cases in banking are propelling the financial services industry forward. New innovative tools enable financial institutions to transform the endless stream of data they continuously generate into actionable insights for everyone, from C-suite and operations to marketing and business development. 

Companies are turning to machine learning use cases in finance for stronger security, slicker user experience, faster support, and nearly instant gapless processing. The benefits add up so much so that the potential annual value of AI and analytics for global banking could reach as high as $1 trillion, according to McKinsey. 

High regulatory and compliance barriers often dampen the adoption of new technologies in the financial services industry, as security is paramount. This article explores why machine learning is different and how the world’s leading financial institutions are leveraging it with great success today. 

Source: McKinsey

Machine learning fraud prevention

Financial fraud costs customers and businesses billions every year. In 2020 alone, businesses lost a record $56 billion. Banks with outdated technology and lax security measures oppose sophisticated cyber attacks and exploits. Companies need to ensure that they know how to prevent fraud to stay at least a step ahead of the fraudsters.  

Machine learning fraud prevention is among the most effective applications of technology to date. Dedicated innovative algorithms analyze millions of data points, transaction parameters, and consumer behavior patterns in real-time to detect security threats and potential fraud cases. Unlike traditional static rule-based systems that apply the same logic to most consumers, machine learning fraud prevention is personalized on a customer-by-customer basis. 

As a result, machine learning enables financial companies to detect fraudulent operations quickly and precisely to prevent any unnecessary losses. As a recent fraud detection case study pointed out, machine learning can be the key to solving the problem of false positives. The net revenue lost from merchants incorrectly identifying legitimate transactions as fraudulent, the so-called false positives, is estimated to reach $443 billion in 2021.

Source: Aite Group

Manual fraud prevention with a human monitoring a dashboard with a few KPIs is not scalable to millions of transactions consumers make every day and millions more metrics associated with them. Machine learning fraud prevention takes the guesswork out of the equation with a constant cycle of monitoring, detecting, and learning that makes better predictions with every new piece of data. 

Machine learning in anomaly detection  

Since the first applications of machine learning anomaly detection in financial services in the 1990s, the technology has matured to simultaneously track and process transaction size, location, time, device, purchase data, consumer behavior, and many other variables. Modern anomaly detection is successfully applied to anti money laundering (AML) and know your client (KYC) processes as well as financial fraud because it provides clear binary answers to complex inputs.

In 2020, regulators fined financial institutions a record-breaking $10.4 billion for AML and KYC violations, an increase of 81% from the year before. As governments continue to crack down on fraud, financial institutions need to ensure they are compliant with the strict regulations to prevent significant losses. 

Machine learning in anti money laundering enables banks to accurately find the very subtle and usually hidden events and correlations in user behavior that may signal fraud. By automating the complex anomaly detection process, financial institutions can process much more data much faster than human rule-based systems. 

Source: Fenergo report

The machine learning KYC process relies on anomaly detection to automatically find irregularities in documents customers submit for verification at the early stages of onboarding, saving companies from taking on unnecessary risk. It also helps to drastically improve user experience by reducing the number of verification steps that impede the consumer purchasing journey.

Real-time anomaly detection enables companies to immediately respond to deviations from the norm, potentially saving millions that would have been lost to fraudulent behavior otherwise. By eliminating the delay between spotting the problem and resolving it, payments and finance companies can maximize the efficiency of their anti-fraud strategies with machine learning.

Machine learning in onboarding & document processing  

The benefits of machine learning use cases in FinTech go beyond transactions and fraud detection. The technology can be applied to back-office tasks with great success as well. Document classification, for one, is a vital but traditionally labor-intensive process that requires considerable time and resources. Machine learning can significantly reduce the processing time for labeling, classifying, and organizing documents for later retrieval. 

By first running documents through the Optical Character Recognition (OCR) process, machine learning algorithms can then digitize the text on scanned documents to read, process, and analyze their context. Using that information, the machine learning model can classify the document and index it for future search for ready access by company employees.   

Machine learning powered document processing is as useful to traditional banks that still rely on paper forms to onboard customers as it is to neobanks that require customers to submit documents electronically. Whether it’s a photo of an invoice to prove the source of funds or a scan of an ID, machine learning is an efficient and highly scalable tool for onboarding. 

As machine learning use cases in payments demonstrate, the algorithms play an important role in the design of the onboarding process. Machine learning can be used to determine what effect the smallest changes in the consumer’s decision journey will have on conversion rates. By crunching through millions of user actions, machine learning can enable financial companies to perfect how consumers interact with their systems. 

When machine learning is a part of the onboarding and document recognition processes, customers can complete complex operations such as opening a new bank account in minutes from anywhere and on any device. All checks can be carried out in real-time while the business effectively captures the data consumer inputs into the system. Such use cases for machine learning help companies build long-lasting and valuable relationships with their customers.  

To get more information about fintech software development challenges and solutions, read this article.

Machine Learning in credit scoring 

Although most financial institutions collect valuable data with every transaction their customers make, most cannot use the information to its full potential. Machine learning in credit scoring can crunch through millions of data points, transaction details, and behavior patterns to determine hidden features about consumers. As a result, machine learning models can generate highly personalized offers that boost revenue by catering to more customers, such as those thought to be credit invisible. 

Unlike human credit scorers, machine learning algorithms can evaluate borrowers without emotion or biases. According to Harvard Business Review, financial companies can make lending fairer by removing racial, gender, and other biases from the models when they are being built. Understanding the unbiased risks enables banks to make better decisions and serve a wider audience. 

With the currently available computational capabilities, machine learning in the credit card industry and beyond can process vast amounts of customer data to generate accurate credit scores almost instantaneously and automatically. With processed data and comprehensive risk profiles, banks can enable their customers to customize their loans on their own and receive money in just a few clicks from the comfort of their homes. 

Source: Business Insider Intelligence

Machine learning in payments

The payments industry has a lot to gain from leveraging machine learning in payment processing. The technology enables payment providers to drive down transaction costs and attract more business when applied to costs, conversion, connectivity, billing, and payouts. 

Machine learning can help PSPs optimize payment routing based on multiple parameters, such as performance, functionality, or pricing. By processing multiple data sources in real-time, machine learning algorithms can dynamically allocate traffic to the best performing combination of variables at any point in time. This unlocks a whole new level of service personalization as the provider can provide the best results for merchants based on their individual goals, whether it’s performance, pricing, or functionality. 

The versatility of machine learning applications in finance allows companies to generate deep value by solving everyday challenges. For example, the recently enacted mandatory two-step verification measures like Verified by Visa or 3DSecure from Mastercard have led to a high decline rate in payments, resulting in many dissatisfied customers. By applying machine learning in payment processing, payment providers could determine if a transaction should be routed to a two-step verification page or if it will go through on its own. 

Machine learning in process automation 

Process automation is another successful application that resulted in many machine learning use cases in FinTech. From automating repetitive tasks through robotic process automation, such as document processing and employee training gamification to customer request tracking, finance companies can completely replace time-consuming manual work and generate more value in the process. 

With every online action leaving a footprint, machine learning algorithms have a lot of data to work with that they can use to interpret behaviors and recognize patterns. This is especially useful for customer support systems that help classify, narrow down, and potentially solve client problems without any human input. Finance companies that use this to their advantage can improve their customer experience, reduce costs, and scale up their services efficiently. 

Source: McKinsey

Given the amount of structurally diverse data businesses generate daily, and even more data available generated in the financial markets, processing it manually would barely scratch the surface of the insights that lie within. As machine learning use cases in finance demonstrate, integrating the technology to crunch through large volumes of data can produce valuable intelligence that improves business decision making, forecasting, and highlights profitable opportunities. 

Machine learning in investing 

Much like machine learning use cases in banking are applied to solving current problems, the technology has also been used to optimize how investment companies operate. For example, algorithmic trading that makes automatic purchases based on predetermined parameters has been around since the 80s, but its recent fusion with machine learning elevated it to a whole new level.  

Trading companies use machine learning powered algorithmic trading to closely monitor financial news, trade results, prices, and hundreds of other data sources simultaneously to detect patterns that move the prices of financial instruments. The algorithms can execute trades at the best possible prices using that information, eliminating fat finger errors that sometimes result in millions in losses. 

Similarly, machine learning has increased the accessibility of financial markets with automated robo-advisors that make investment decisions automatically based on a customer’s risk profile and preferences. Robo-advisors create personalized portfolios to help consumers achieve their investment goals, be it everyday savings, retirement funds, or protection from inflation.  

Source: PwC

Machine learning in customer retention 

Quality customer support is a vital component of any successful financial business. Machine learning in the payment industry and financial services helps companies understand and cater to their customers’ needs with personalized services and offers. The technology enables businesses to extract nuanced insights from how their clients interact with their products and services. This intelligence can be used to determine areas for improvement and highlight opportunities for expansion with a huge scope of implementation that spans the whole marketing funnel.

Perhaps more importantly, the data can help companies monitor and forecast customer churn based on changes in behavior. As acquiring new customers is much more expensive than retaining existing ones, machine learning helps companies identify customers they are at risk of losing and act quickly to retain them. Whether it’s a customer who had a bad experience or someone who forgot about the service and stopped using it, machine learning can help build loyalty and keep customers interested longer. 

Source: McKinsey 

Machine learning in support & chatbots 

Chatbots unlock new useful functions for customers much in the same way as machine learning use cases in payments do for companies. When connected to a payment system, machine learning powered chatbots let customers ask questions and receive insightful answers about their accounts and transactions. Sophisticated algorithms powering chatbots can solve many day-to-day queries that would otherwise be passed on to traditional customer support. Unlike most call centres, chatbots are available 24/7/365 and provide nearly instant responses.  

If a chatbot cannot resolve an issue, it would have to pass it on to a human customer support representative. But before that happens, the algorithm would automatically classify the query and pull up all of the relevant details so that the human operator can resolve the issue much faster. Any conversation with a chatbot is an opportunity for companies to offer personalized offers, be it a lending product, a new subscription plan, or a special insurance offer. 

Conclusion  

Machine learning use cases in payments, finance, and banking challenge competition to develop faster, cheaper, and better propositions. Financial institutions must build loyalty with highly personalized, timely, and competitively priced products and services to remain competitive. The core technology and data infrastructure need to leverage the many advantages of machine learning in decisions across the customer life cycle to achieve this. For more and more financial companies, machine learning isn’t just the future. It’s the present. 

The post Machine Learning in Banking: Top Use Cases appeared first on SDK.finance - White-Label Digital Banking Software.

Payment fraud is one of the most common forms of identity theft in the world. In 2020, the total amount of losses incurred by businesses due to financial fraud rose to a record $56 billion, with over 2,800,000 cases of credit card fraud being reported in the UK alone.

A large portion of these losses was borne by banks and payment processing service providers. These institutions are often responsible for covering chargeback costs. In the case of money laundering, they may also be subjected to million dollar fines from government authorities. Not to mention the costs of dealing with the PR nightmare that seems to always follow suit.

With more and more consumers taking their spending habits online, investing into better fraud prevention systems is one of the best decisions a financial institution can make. It will help your business avoid hefty penalties. It will safeguard your clients from unnecessary stress and headaches. And it will help you maintain a stellar reputation.

In this article, you will find out what payment fraud is, its main types and forms, as well as the most effective payment fraud detection tools available.

Source: PwC. 2020 Global Economic Crime and Fraud Survey. 

What Is Payment Fraud?

Payment fraud is an umbrella term for illegal financial transactions. The European Union Agency for Law Enforcement Cooperation (Europol) categorizes payment fraud as a low-risk, high-profit criminal activity. 

The combination of low stakes and high rewards makes this type of fraud very popular with criminal outfits of all sizes.

Source: European Fraud Report – Payments Industry Challenges. Date: 2019

What Are the Main Types of Payment Fraud?

Financial Identity Theft

When most people hear the word payment fraud, they think of financial identity theft. Simply put, this is the purchasing of goods using stolen payment information. Financial identity theft can be divided into two types, card-present fraud and card-not-present fraud.

Card-Present Fraud

As the name implies, this first type of credit card fraud involves the criminals having direct access to a copy of the victim’s physical card.

Fraudsters create these copies with the use of card skimming devices. After the victim inserts their card into a skimmer, the device reads and copies the authentication information exchanged between the credit card and the bank. This can happen, for example, at a compromised ATM or when the victim hands their credit card to an unscrupulous restaurant waiter. Check this article to get more information about credit card fraud detection. 

The criminals then use the credit card to make fraudulent purchases until the victim notices the transactions and blocks their card.

Source: European Central Bank Sixth Report on Card Fraud Date: 2020

Card-Not-Present Fraud

Unlike the method we just discussed, card-not-present fraud does not require any direct contact with the victim or the purchase of expensive skimming and card creation equipment. All the criminals need are the victim’s payment details. 

Criminals can gain this data in several ways, ranging from phishing attacks and social engineering to database breaches. Alternatively, they may simply purchase a database of already-stolen credit card information from the dark web. 

Due to its low barrier to entry, card-not-present is by far the most common type of credit card fraud today.

Traditionally, this type of fraud was very difficult to deal with. However, modern software can help banks and payment processors successfully deal with most cases of card-not-present fraud instantly. Usually, it is trained on a database of billions of both legitimate and fraudulent transactions. So it’s like having a team of experienced employees instantly review every single payment.

Source: Experian

Money Laundering

There are many ways in which criminals can use credit card transactions for money laundering. Modern security software made old-fashioned schemes of using money mules to make deposits at dozens of different bank branches per day obsolete. So criminals have devised new ways to launder their ill-gotten money. 

Merchant Identity Fraud

One of these ways is merchant identity fraud. The criminals register a business and create a legitimate-looking website for it. They then set up a merchant account and make payments to that account using stolen credit cards or the cards of their mules.

These fake businesses can pretend to sell anything from digital marketing services and personal coaching to foreign language courses. It actually does not matter what they do. It’s just a facade. No actual services are provided.

By the time most AML teams discover the fraud, the fake company is already nowhere to be found.

The best ways to protect yourself from digital fraud in banking is by investing in state-of-the-art Know Your Customer (KYC) and Anti-Money Laundering software.

Additional tools, such as anomaly detection, can act as an additional layer of security by highlighting which of your clients show signs of engaging in fraudulent activities and which credit card may have been stolen or is being used by money mules.

Modern Payment Fraud Prevention Tools

When it comes to combating payment fraud effectively, having good software on your side is a must. Bad actors are constantly figuring out new ways to outsmart payment providers and banks. By investing into the best credit card and retail banking fraud detection tools you can afford, you ensure that your business stays several steps ahead of them.

AML Fraud Prevention Tools

There are hundreds of AML fraud prevention tools on the market. Anti-Money Laundering software ranges from solutions that help your in-house team collect and process AML documents more efficiently to automated checkers that assign a reliability score to each client. There are also software suites that offer all-in-one packages.

Here are a few popular AML options:

• ComplyAdvantage
• NorthRow
• Sumsub
• Folio Digital Identity
• AML Manager

None of these solutions is universally better than the rest. Whether a particular piece of software is the right choice for you depends on the unique needs and wants of your business, as well as the AML requirements of the regulators you work with.

Source: Getid.ee

Fraud Protection with KYC Systems 

Investing in a good Know Your Customer solution allows you to block bad actors before they have a chance to use your platform. Modern KYC solutions can instantly check your new clients against a list of persons and businesses under financial sanctions and known bad actors. They can also flag new or recently bought companies that aren’t in those lists but are of potentially high risk.

KYC solutions are often found bundled in AML software packages, but stand-alone options also exist.

Many KYC tools are tailored to meet the specific Know Your Customer requirements of government regulators. This helps ensure that your KYC procedure meets all the necessary guidelines.

Here are a few options that are popular with specialists across the globe:

• Mitek
• Comply Advantage
• Identity Mind Global
• Trulioo
• Fenergo
• Equiniti KYC Solutions
• Accuity
• Opus
• Simple KYC

Source: ShuftiPro

Digital banking and payment fraud is a serious problem that no player in the financial services market can afford to ignore.

In 2024, the losses incurred by businesses due to illicit transactions rose to a record $56 billion. With more and more inexperienced customers taking their shopping online, it is very likely that this figure will continue to grow year after year.

The best way for banks and payment processors to safeguard themselves and their clients from payment fraud is by investing into high-quality Know Your Customer (KYC), Anti-Money Laundering (AML), and anomaly detection software.

Watch SDK.finance Platform’s demo video to explore how to manage your users, ensure KYC compliance, and prevent fraud with the robust system back office. This video showcases real-life scenarios demonstrating the power of the Clients section:

Anomaly Detection Systems for Payment Fraud Prevention

AI-driven anomaly detection tools are the latest development in digital banking fraud prevention that can be used for payment processors as well. With the help of big data, tools for anomaly detection in finance can easily handle cyclical, seasonal, and even idiosyncratic payment pattern variations while still being effective at filtering out fraudulent payments.

Why is this important? Because it provides great protection for you and your clients while not introducing any sales friction into the process. Your clients can enjoy a seamless payment process while the anomaly detection software does all the heavy lifting in the background.

Users whose transactions fall into this category are asked to complete an additional verification procedure. This helps you separate criminals from genuine customers who are making a highly unexpected purchase.

The post How to Prevent Payment Fraud: Solutions for Banks and Payment Processors appeared first on SDK.finance - White-Label Digital Banking Software.

Over the last decade, the use of AI has exploded. Machine learning and deep learning algorithms and models process an immense amount of data to enable faster, smarter, and better business decisions. As such, machine learning forecasting for the financial industry holds incredible potential for banks, the historical custodians of vast stores of data. However, the technology’s direct impact is still marginal as only a few institutions have capitalized on the technology’s extensive potential. 

BCG estimates that businesses and banks that embrace ambitious AI strategies can add 15-20% to their bottom line in one to two years. McKinsey expects that machine learning technologies in banking could potentially deliver up to $1 trillion of additional value for the global banking industry each year.

To help business leaders capture the value machine learning in banking technologies holds for them, we propose answers to the following questions: 

1. What is the difference between machine learning and deep learning? 
2. How are machine learning and deep learning used for forecasting and prediction? 
3. How do banks use machine learning and deep learning for forecasting and prediction?

What is the difference between machine learning and deep learning?

Artificial intelligence, machine learning, neural networks, and deep learning are often misleadingly used interchangeably in media, creating ambiguity about them. When, in fact, they are essentially subsets or progressions of each prior term. As such, deep learning is a subset of machine learning, and both are subfields of artificial intelligence. 

Source: NVIDIA

Although deep learning and machine learning function in a similar fashion, their capabilities are different. Machine learning models can become progressively better with iterations, but humans must correct inaccurate predictions generated by the algorithms. On the other hand, a deep learning model can determine whether its predictions are accurate or not on its own.

For example, a machine learning algorithm can be taught to spot a suspicious transaction and flag it as fraudulent by feeding it a usually structured dataset to learn from. It depends on human intervention to determine the differences between data inputs and their characteristics, such as time, date, amount, and location of a transaction. As it continues to learn, it will flag any transaction when it spots certain suspicious behaviors it recognizes. 

Now, a deep learning model automates much of the fraudulent behavior extraction process, eliminating some of the required human input. As deep learning models have multiple neural networks, large unstructured datasets can be processed more efficiently and faster than with a machine learning algorithm. This is an important point because unstructured data comprises 80-90% of data found in companies worldwide, according to IBM. 

Besides preventing fraud, machine learning and deep learning enable banks to improve user experience, optimize services, automate processes, and predict customer churn. Leading financial institutions are already employing data science tools that help them drive sales and revenue. 

Source: KPMG

How are machine learning and deep learning used for forecasting and prediction in banking?

Using machine learning in banking and deep learning for banking forecasting and predictions are powerful tools that help to make better-informed business decisions. As the most recent pandemic demonstrated, consumer behavior can change drastically over the course of just a few months. With stores closed under lockdown, consumer purchasing surged online. Datasets used to train static banking anomaly detection systems did not have any remotely similar patterns. As a result, many financial institutions worldwide saw their anomaly detection anti-fraud systems fail.

Adopting machine learning in banking circumvents the deficiencies of static systems by continuously learning from fresh incoming data. Forecasting for banking using deep learning can generate even better results with less human input. Both technologies can generate valuable insights from vast amounts of data that can be used to forecast and predict consumer and business behavior.

It is important to note that forecasting and prediction mean different things, much for the same reason weather forecasting is not called weather prediction. A prediction model uses a training dataset to estimate the outcomes for new data points. On the other hand, forecasting adds a temporal dimension into the equation to make estimates based on time-series data. Forecasting models depend on previous, most recent observations to make future estimates instead of using only the dataset.

For machine learning in banking, the challenge with forecasting models is to find the optimal number of previous events and variables that should be considered when making future estimates. Too many or too few can result in inaccurate results. For predictions, it is important to select datasets that accurately represent the business. If the dataset contains transaction data going back 30 years, the algorithm is unlikely to generate the best results.

How do banks use machine learning and deep learning for forecasting and prediction?

Machine learning for financial forecasting can be applied to many administrative, operational, and client areas of the banking industry. Since financial institutions can collect vast amounts of data, machine learning algorithms can be applied to almost all banking business operations with great success. 

Anomaly detection and fraud prevention

Cybercrime is costing consumers and businesses billions of dollars every year. Financial institutions spend billions more investigating and recovering the stolen money. There are more cyberattacks now than ever before, and they are becoming more and more sophisticated. Strong fraud prevention mechanisms such as forecasting using deep learning for banking are key to preventing unnecessary losses from fraud. 

Banking forecasting using machine learning allows companies to monitor incoming transaction parameters in real-time. The algorithm examines the time series, evaluates customer actions, and examines other variables to determine how likely a suspicious transaction is to be fraudulent. The best way against costly fraud is early detection because it enables banks to block any questionable behavior, thus saving consumers’ money and preventing losses from compliance fines. 

Using machine learning for forecasting in banking drastically reduces the amount of time it takes to spot suspicious transactions as these models can sift millions of data points every second. 

Watch the SDK.finance demo video to explore how to simplify transaction management and ensure financial compliance with our powerful FinTech Platform. This video highlights how SDK.finance provides a comprehensive view and control over your client transactions, as well as the AML & fraud prevention functionality:

Risk assessment

Machine learning forecasting for banking enables more accurate reporting by automating credit risk testing for both banks and customers. By evaluating a consumer’s financial history, recent transactions, and purchasing patterns, machine learning can make accurate forecasts of future spending and income. Automated risk assessment enables banks to automatically offer the best possible terms for loans and credit products to customers based on their risk. 

Credit risk forecasting for banking using deep learning takes minutes and eliminates human errors that can create unnecessary problems down the line. The same algorithm applies to investment risks as well so that banks can evaluate their assets to make better financial decisions. SDK.finance understands how important risk management is for financial companies. To help banks optimize their risk exposure and maximize revenue, SDK.finance offers a risk management feature. 

Customer churn

It is much more expensive to win over a new customer than to retain an existing one. Customer churn is a vital metric that helps to identify and convince clients before they decide to switch services or products. Using deep learning for banking predictions, companies can determine behavior patterns exhibited by clients before they leave. Whether it’s less frequent visits to the platform’s application or lower transaction volume, banks that can spot that behavior are more likely to retain their customers. 

The same metrics can be used to tailor the user experience to maximize retention. The Tech Giants have been using machine learning to tweak their website to maximize the time people spend there. Now financial institutions can do it as well with SDK.finance’s white-label platform that uses deep learning for banking predictions. 

Business performance

Businesses generate an immense amount of data about their performance every day. Making sense of that data is a complex task that can be very rewarding when done right. Instead of letting valuable data go to waste, machine learning forecasting allows companies to forecast their business performance and account for seasonality, consumer sentiment, background, and external factors. 

With accurate revenue forecasts, banks can plan investments and expenditures accordingly. If a branch or division is underperforming, machine learning can help to identify the cause and ways to correct it quickly. Planning infrastructure needs based on future forecasts can prevent unexpected downtimes and additional expenses. SDK.finance leverages machine learning to help companies break down the neverending raw data stream into actionable insights about their business performance. 

The post Machine Learning & Deep Learning Forecasting for Banking Industry appeared first on SDK.finance - White-Label Digital Banking Software.

Businesses today can relatively easily gather and generate a lot of data about their customers, operations, and performance. However, abundant information from CRM systems, ERP platforms, and marketing campaigns does not directly lead to better sales figures and higher profits. 

Data science is the catalyst that turns raw data collected from multiple sources into actionable insights that can improve the bottom line. With access to better data-backed intelligence, companies can adapt their business strategies to capture the most value in their market. By improving the quality of information and business decisions stemming from it, data science enables inbound and outbound growth. 

Besides evaluating past performance, data science is a powerful tool for creating predictive models that can highlight changes in customer behavior, market trends, and potential opportunities. Machine Learning (ML) and Artificial Intelligence (AI) powered systems can monitor and process incoming data in real-time to anticipate outcomes based on historical patterns. 

72 percent of the fastest-growing B2Bs say their analytics are effective in helping them with sales planning, compared to 50 percent of the slowest-growers. Source: Mckinsey

Inbound growth from data-backed insights

Data science tools are instrumental in improving interactions with existing customers. By evaluating previous sales, predictive analytics can help trigger responses and suggest products to guide clients on a personalized purchasing journey. Specific steps can be programmed to automate lead nurturing and qualification that save time and create new sources of revenue. Behavioral insights can be used to determine what products and services customers are more likely to be interested in.  

Predictive metrics can be used to monitor and identify customers who are almost ready to purchase, giving sales departments a head start. With more information to work with, sales teams can adapt their approaches and sales pitches to maximize chances of successful conversions. Data science can crunch information about client companies, their size, budget, and needs to create the best possible offers. 

Willingness to pay is an important metric that is vital for setting the optimal price for a product or service based on customers’ needs, motivations, and preferences. Data science analytics take the guesswork out of the equation by identifying price tiers and how they can be adapted to improve profitability. This data can be used for A/B experiments learning to determine the optimal prices or fees for consumers. 

Offering existing clients more well-priced products and services not only helps to take advantage of recurring revenue but to improve customer satisfaction and retention. By using customer behavior as an indicator, data science tools enable companies to identify customers who are ready to buy more and those who are close to switching to a competitor. In both cases, businesses can capture more value having identified them. In the end, retaining customers is a lot cheaper than acquiring new ones. 

Source: McKinsey

Data-driven outbound growth

When combined with data science, sales prospecting generates higher quality leads as the process incorporates multiple data variables in the search. By identifying points in customer characteristics that are more likely to result in a sale, data science tools can generate well-defined and comprehensive buyer personas. Better targeting improves the effectiveness of marketing and advertising efforts as they can be aimed at the right segments at the right times. 

With access to data science insights, sales and marketing teams can vastly improve their decision-making, structure, and forecasting. Better data means sales quotas can be much more aggressive while remaining precise and achievable. Incorporating external factors, seasonality, new products, and other metrics helps teams see the bigger picture and forecast sales demands accordingly. 

Data science enables companies to align their sales expertise and resources with their covered territories to deliver optimal results. Predictive analytics can point to new selling models, sales policies, and revenue distribution among salespeople. Operations-wise, data science tools can point to more intensive quarters which require additional resources to handle the future increase in demand. By preparing ahead of time, companies don’t miss out on valuable opportunities. 

Data science is a powerful tool that can have a significant positive impact on sales and revenue by driving inbound and outbound growth. Predictive analytics can transform sales, improve operations, and extract the full potential from gathered information. Deep insights generated from business data provide greater accuracy and more control in business decisions that steer future performance. 

With a strong data science toolkit, companies can collect, organize, and analyze their data to attract, convert, and close more customers. 

Experience firsthand the transformative power of SDK.finance’s Platform through our demo video, showcasing real-life scenarios where you can optimize revenue potential with flexible fee structures:

With intuitive customization options driven by data science, SDK.finance empowers financial institutions to maximize revenue streams effortlessly. Contact the SDK.finance team directly to talk about how data science can be useful for your payment business. We are open to discussions.

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Consumers and businesses worldwide are losing billions of dollars every year to neverending attacks from cybercriminals. Financial institutions spend billions more investigating and recovering the stolen money. As attacks become more and more sophisticated, money-handling companies need to incorporate strong fraud-prevention mechanisms into their strategies to protect their customers and themselves from unnecessary expenses. 

The ever-growing amount of data captured by financial institutions makes anomaly detection an invaluable tool for identifying fraudulent transactions and behaviors.

Cybercrime complaints and reported losses 2015-2019 

Source: FBI Internet Crime Complaint Center

What is anomaly detection?

Anomaly detection in financial transactions classifies data into normal distribution and outliers. When a transaction or a data point deviates from a dataset’s normal behavior, it can be considered potentially fraudulent. 

How does anomaly detection work in payments and finance? 

The anomaly detection approach for transaction data is advantageous because it provides simple binary answers. Any unexpected change from normal data patterns or an event that does not conform to model predictions is considered an anomaly. If a transaction looks suspicious and potentially fraudulent, the system may ask the customer to verify details or go through additional verification steps. By analyzing multiple data points, anomaly detection can be applied to flag technical outages, glitches, and potential opportunities such as a positive change in consumer behavior.

However, there are no universal patterns or business as usual when it comes to everyday life. The same unusually large amount of payments expected on Black Friday would stand out on any other day, and vice versa. But even the most well-established peaks in the natural business cycle can shift from time to time. 

The coronavirus pandemic, for example, resulted in a skyrocketing volume of online payments and a fall in in-store purchases. Datasets used to train static anomaly detection systems didn’t have any similar historical patterns, which resulted in countless transactions being flagged as fraudulent when they were not. Many financial institutions worldwide saw their anomaly detection anti-fraud systems fail for this exact reason. 

Machine Learning powered anomaly detection

Incorporating Machine Learning (ML) anti-fraud systems is an advanced approach that reduces uncertainty by automating the complex anomaly detection process. ML algorithms can be used to find the very subtle and usually hidden events and correlations in user behavior that may signal fraud. By comparing numerous variables in real-time, anomaly detection with machine learning can process large datasets to determine the likelihood of fraudulent transactions or actions. 

ML has been used to spot fraudulent transactions since the 1990s. Since then, the technology has matured to track and process transaction size, location, time, device, purchase data, and many other variables simultaneously. ML-enabled anomaly detection can process much more financial data much faster than human rule-based systems. Smart algorithms that monitor consumer behavior help to reduce the number of verification steps that impede the consumer purchasing journey and reduce false positives, drastically improving user experience. 

Real-time anomaly detection in financial transactions enables companies to immediately respond to deviations from the norm, potentially saving millions that would have been lost to fraud otherwise. By eliminating the delay between spotting the problem and resolving it, payments and finance companies maximize the efficiency of their anti-fraud strategies. 

Manual anomaly detection with a human monitoring a dashboard with a few KPIs is not scalable to millions of transactions consumers make every day and millions more metrics associated with them. Maintaining real-life responsiveness requires a sophisticated anomaly detection system powered by machine learning that can monitor and correlate multiple complex metrics with different amounts of variability to sift through millions of data points every second. 

Source: Federal Trade Commission, Consumer Sentinel Network

Anomaly detection: build vs. buy?

The importance of fraud detection for payments and finance companies is hard to overstate. Real-time anomaly detection is already used by leading financial institutions worldwide to prevent losses from occurring in the first place. Businesses aiming to stay a step ahead of cybercriminals can either buy a complete anomaly detection system or build it from scratch. 

To make the right decision that will generate the greatest return on investment, companies need to consider their size and the volume of financial data that must be processed. The budget and time to value tie in with the capacity for development and maintenance of the IT team building it. Lastly, it is essential to factor in future growth and how it will impact all of the previous factors. Real-time anomaly detection for transaction data is a sophisticated tool that requires specialist knowledge and an expert IT team to develop.  Building from scratch enables complete control over the final product but includes a great deal of uncertainty. Partnering with a technology vendor minimizes risk as anomaly detection can be integrated quickly and predictably. 

What is SDK.finance?

A fintech software vendor offering a powerful payment Platform for building retail banking, ewallets, P2P payment apps, marketplace payment solutions, payment acceptance solutions etc.  

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