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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The security of the 468 billion payment card transactions made annually rests on the competency of fraud detection software. Leading card payment processing companies rely on different approaches to minimize significant losses from fraud. On top of the $29 billion lost to fraud in 2019, regulators fined companies billions for non-compliance with AML and other directives.

As a result, many businesses invest in complex, costly fraud management systems that run on hand-coded rules, making them difficult to customize and update to changes in fraud patterns. Consequently, valid transactions are dismissed as fraudulent, expensive fraud reviews grow in number, and opportunities to reduce fraud are lost to inaccuracy. Once a company reaches a fraud rate greater than one percent, card networks can go as far as canceling the permission to accept and process credit card payments, a detrimental result for any business.

Card fraud worldwide

Source: Nilson report

The importance of preventing the unauthorized use of a credit or debit card to obtain money or property fraudulently is hard to overstate. With identity theft and transaction laundering being the most common forms of fraudulent schemes involving payment cards, businesses need to ensure that they and their customers are as safe as possible to prevent costly and lengthy investigations and losses. 

What is credit card fraud detection?

Credit card fraud detection identifies suspicious transactions, events, and behaviors for further investigation. Each and every operation generates hundreds of data points that are evaluated for signs of fraud derived from past data. Modern machine learning powered fraud detection systems consider the tiniest changes in a customer’s behavior patterns in milliseconds with a high degree of precision. So how do credit card companies detect fraud? 

Unlike the outdated rule-based systems that rely on stationary rule sets, machine learning approaches are much more dynamic and proactive. For years, payment providers have been building their risk management strategies based on how credit card fraud works with an invaluable machine learning component. 

Perceptions about the protection of online transactions

Source: The real cost of online fraud

Such evolving systems reduce the number of false declines of legitimate transactions, identify new patterns non-stop, and adapt to changes in a constantly changing environment and financial conditions payment service providers and merchants operate in. Some large service providers processing millions of transactions every day share their fraud detection experiences with the industry, and some even provide open access to their solutions to the general public. 

Let’s explore how the biggest names in business tackle the challenge of credit card fraud detection and what businesses can learn from them. 

Paypal fraud detection

Research commissioned by PayPal outlines that companies are losing an average of $4.5 million per year due to online fraudulent transactions. PayPal’s fraud detection system needs to sustain constant attacks to prevent sizable losses for a company with hundreds of millions of customers worldwide. By taking a deep learning approach that leverages a massive amount of fraud data accumulated over the years, PayPal has kept its fraud loss rate to just under 0.3%. 

Starting with logistic regression machine learning over a decade ago, PayPal has implemented more advanced techniques in recent years. Gradient Boosted Trees and neural networks enable PayPal’s fraud detection system to evaluate risky transactions with a high degree of accuracy in real-time. 

For PayPal, the largest jump in online spending to 21.3% of total retail sales in the U.S. in 2020 came with a significant increase in online scams and sophisticated fraud attempts by malicious actors. To respond to pandemic-fueled changes, PayPal rolled out the Fraud Protection Advanced service to help merchants identify, investigate, resolve, and mitigate fraud in the increasingly complex digital landscape. The new solution leverages custom filters, risk scores, block and allow lists, and custom options that use a merchant’s historical data to detect and prevent fraud. 

Fraud reports by fraud method

Source: FTC

Amazon fraud detection system

Strong fraud detection is absolutely necessary for the world’s largest online retailer to reduce consumer friction and prevent losses. Amazon invests heavily in sophisticated machine learning techniques to combat fraudulent activity and stay a step ahead of the cybercriminals. Besides the tons of data Amazon has generated over decades in business, it also uses AWS customers’ datasets to train its fraud detection systems. 

Amazon leveraged its internal developments and experience in combating scams to roll out a public version of its fraud detection system as a fully managed service in 2020. Amazon Fraud Detector powered by machine learning integrates via API and combines customers’ historical data with its own to create customized models that detect suspicious behaviors indicative of identity theft or transaction laundering. 

The Amazon fraud detection service works in real-time and can automatically identify potentially fraudulent transactions in milliseconds. Customers can fine-tune their machine learning models by creating decision logic to assign outcomes to predictions. Depending on the risk score, customers can predetermine the right course of action to prevent needless losses and time-consuming investigations.

eBay fraudsters

As the largest auction site in the world, eBay is an attractive platform for scammers because they can exploit the necessary trust between buyers and sellers. As eBay pushes back with stronger safeguards, fraudsters come up with new ways to cheat the system. 

The most common eBay buyer scams range from receiving empty boxes and counterfeit goods to asking for payments outside of eBay or through gift cards. All of these methods aim to create a veneer of legitimate behaviors so that eBay sides with fraudsters in case of a dispute. When scammers pose as buyers, they exploit eBay’s consumer protection measures to defraud honest sellers by overpaying, changing addresses, claiming that packages arrived empty, and many more.

Unlike automatic fraud detection systems that look for fraudulent behaviors, eBay has turned the process on its head. As fraudsters keep on coming up with new scams and patterns to circumvent the system, eBay chose to look for good behavior patterns that do not change with time instead. A report published by eBay executives describes how the auction site’s new AI algorithm can identify credit card fraud transactions with high precision by identifying outliers using a clustering method to formulate a score for consistency and good behavior. 

Visa fraud monitoring program

In 2020, Visa’s AI fraud monitoring program prevented $25 billion worth of losses by partnering with financial institutions and merchants to combat illegitimate transactions. The Visa Advanced Authorization system processes and evaluates more than 500 transaction parameters to estimate the risk of fraud in about a millisecond. Time, geo-location, amount, spending patterns, transaction type, circumstances, and many more attributes are analyzed to generate a risk score that is sent to a cardholder’s bank for the final decision. 

Visa chargeback and fraud monitoring programs have achieved a fraud rate of less than 0.06% by building a multi-layered security infrastructure with an AI fraud detection system at its core. Visa has reduced latency for its 3.5 billion cards and 210 billion annual transactions by layering AI and ML tools in systems outside its main transaction processing network. Visa leverages recurrent neural networks and gradient boosted trees to lower customer friction and faster fraud detection with a 20-30% lift in model performance. 

Mastercard fraud monitoring program

As one of Visa’s closest competitors, Mastercard fraud prevention also relies on identity verification. With the Mastercard Identity Check program and its EMV 3D-Secure 2.0 technology, the financial services company helps merchants and card issuers authenticate card-not-present transactions quickly and securely. 

The Mastercard fraud prevention program leverages AI and machine learning to check 150+ transaction parameters to assess risks and filter legitimate transactions from illegitimate ones in real-time. Depending on a transaction’s risk score, card issuers can decide whether they want to authenticate an operation or not. 

Besides time, amount, location, and other standard variables, Mastercard checks screen brightness, customer gestures, history, and merchant-specific parameters to calculate the probability of a transaction being fraudulent. Mastercard fraud prevention can require additional authentication with biometrics or a one-time password for suspicious transactions. Additional checks are better than blocking operations outright as they reduce customer friction without impeding the purchasing journey.

Source: Mastercard

Apple Pay fraud detection and prevention

Through a wholly-owned subsidiary called Apple Payments Inc., created to prevent the rest of Apple from interacting with customer information, the company verifies the identity of each Apple Pay user. Customers may be asked to provide their name, address, social security number, and government ID before making transactions through Apple Pay. While Apple cannot read this information, they minimize the chances of bad actors committing fraud on their platform by requiring comprehensive identity verification in the very beginning. 

When verified users use Apple Pay for adding or transferring money to another person or bank account, Apple fraud prevention checks their approximate use patterns on their Apple devices. This can include how frequently a payer communicates with the payee by phone, email, or text messages. 

Apple does not collect the context of communication, such information is stored for a limited time, and it cannot be linked to the payer unless a transaction requires further investigation due to suspicious activity. By leveraging platform-specific device parameters, Apple Pay fraud protection strives to add another layer of security to everyday transactions.

Source: Apple 

Google Pay fraud protection

100 million users are making payments using Google Pay and the number is growing steadily, driven by the pandemic induced shift towards touch-free payment methods, digital wallets, and mobile payment apps. To protect customers and thousands of merchants, reduce chargebacks, and reduce customer friction, Google Pay fraud protection assesses customer data whenever a new card is connected to the system for risk criteria using an identity and verification (ID&V) process. 

The ID&V process is complemented by the Google Pay fraud department and their use of the latest security protocols to protect consumer data from bad actors, scammers, and even fraudulent merchants. Google encrypts and stores customer payment credentials on their servers to prevent unauthorized access.  Lastly, users need to unlock their devices for each transaction and authorize the operation with a password or biometric authentication. 

Furthermore, Google Pay creates single-use virtual account numbers for purchases at points of sale, preventing merchants from seeing a customer’s actual payment credentials. Even if a terminal is hacked, the payment information cannot be used to make further unauthorized transactions or clone payment cards. 

Let’s take a look at SDK.finance’s demo video to see how SDK.finance offers a complete overview and management of client transactions, as well as advanced AML and fraud prevention capabilities, enabling institutions to stay proactive in combating financial crime:

Conclusion

It is implausible that fraud and scams will stop anytime soon. Bad actors will continue looking for ways to exploit weaknesses in payment systems, and companies will patch their security in an ever-evolving cycle. Businesses that decide not to leverage the latest technologies will see their fraud rates grow as scammers flock towards companies with weaker security and avoid those with AI-powered fraud detection. 

The post Credit Card Fraud Detection. Big Players’ Experience appeared first on SDK.finance - White-Label Digital Banking Software.

It’s a lender’s nightmare. You approve a home loan or a line of credit. A few months later, the account holder tells you someone else had taken out the loan in their name using a stolen ID. You try to track down the money, but it is long done. You contact the authorities but there’s nothing they can do to help you. So your business has to eat the loss.

Loan application fraud is no boogeyman tale. It’s a real issue faced by thousands of lenders all over the world. 

It is estimated that financial institutions lose billions of dollars yearly to this type of scheme, with synthetic identity fraud alone being responsible for over six billion dollars of credit losses. In the US alone, close to 300,000 people fall victim to credit fraud every year. And the average amount a bank can expect to lose due to a single loan fraud case is estimated to be around 6,000 USD.

With the amount of consumer debt increasing steadily over the last five years, it looks like the situation will only get worse. So lessening the negative impact of fraudulent payments should be a top priority for financial institutions.

Read this article to find out more about fraud on loan applications and how the latest trends in lending software development can help shield you and your customers from it.

What Is Loan Application Fraud? 

Loan Application Fraud Definition

Credit fraud or loan application fraud is a type of financial fraud during which the criminal takes out an illicit loan they have no intentions of ever paying off.

They can take out the loan in their own name, in which case we would label it first-party fraud. 

More commonly, however, the line of credit will be taken out in the name of a third party using faked or stolen identification documents. In this case, we will label it as third-party fraud.

Loan Application Fraud Statistics

According to the 2021 report by the Federal Trade Commission (FTC), almost 30% of all financial fraud complaints in the US involved identity theft. This represents a 50% increase from the year before. And loan application fraud was one of the main sources of these increased complaints.

It is currently estimated that a whopping 10% of all “bad debt” held by banks in the United States is a product of credit fraud. And it’s not just payday lenders with lax identity check procedures that get defrauded. Criminals target all types of institutions, with experts estimating that first-party loan fraud accounts for 0.75%-1.50% of AAA prime bankcard and demand deposit portfolios.

Which Loan Types Are Most Susceptible to Financial Fraud in 2021?

According to the FTC, federal student loans, personal loans, and auto loans experienced the biggest rises in financial fraud last year. That being said, this year’s report revealed a general sharp increase in loan application fraud, with all types of lenders suffering more damage than the year before.

#1 Federal Student Loan (188% Increase)

Federal student loan fraud rose by a whopping 188% between 2018 and 2019. In the vast majority of cases, this was first-party fraud.

#2 Personal Loans (116% Increase)

Business and personal loan fraud rose by 116% over the same time period.

#3 Auto Loans (105% Increase)

The data also revealed a sharp increase in car loan application fraud, with auto-loan and lease fraud increasing by 105% in 12 months.

Types of Loan Application Fraud

There are two main types of loan application fraud: first-party and third-party fraud. 

First-Party Loan Application Fraud

First-party fraud involves the criminal applying for a line of credit or a personal loan using their own legal name and documents. The criminal will then withdraw all of the money from the account and disappear without a trace.

Naturally, this method represents a high degree of risk for the fraudster as they have to voluntarily hand over their own personal data to the lender. While an unscrupulous actor could simply cross a state border and start a new life a century ago, this is borderline impossible in today’s digital world.

As a result, this method is becoming less and less popular every single year.

Third-Party Loan Application Fraud

Third-party loan fraud, on the other hand, involves receiving loans in the name of another person. To do this, the criminals can use either stolen or faked identity documents. If you want to know more about how they do this, read our article on the document fraud. 

As we discussed in our article about payment fraud, by the time the identity theft victim notices that something is amiss, the criminal (and the money) is usually long gone. And the financial institution that issued the loan has to suffer the loss.

And because the criminals can switch to a new identity after every loan application spree, third-party application fraud can inflict a lot more damage to your business.

Synthetic Identities in Third-Party Loan Fraud

The last few years have seen a sharp increase in the use of so-called synthetic identities in loan fraud. Explained simply, a synthetic identity is a legitimate-looking persona that is created via a combination of real and fictitious information.

SAS calls synthetic identities the “gold standard” of banking application fraud. And for a good reason. Artificial identity loan applications are notoriously difficult to spot and prevent.

Why Synthetic Identities Are Hard For Financial Institutions to Deal With?

Loan fraud using synthetic identities is harder for financial institutions to detect for a variety of reasons.

First of all, most fraud models were not created with detecting synthetic identities in mind. As a result, a reported 85-95% of all cases of synthetic identity fraud cases are not being flagged as even potentially fraudulent by traditional loan application fraud detection systems.

Secondly, as there is no actual identity theft victim, no one will alert your team about it. The account will be dealt with like a legitimate one until your team realizes what’s going on. Which can take weeks or months. Further increasing the criminal’s chances of avoiding punishment.

How Big of a Problem Is Synthetic Identity Fraud?

A report by Auriemma Consulting attributed an astonishing one fifth of all credit losses suffered by financial institutions to synthetic identity fraud. 

How to Prevent Loan Application Fraud

Banks can battle fraud on loan applications in a wide variety of ways. By integrating these methods into your company’s lending software development efforts, you will make your financial institution much more resilient to credit line fraud.

In-Depth Monitoring of New Account Application Data

By maintaining a corpus of existing and closed accounts, your financial institution can look out for device fingerprints and data reuse.

Implemented as part of a rule system, this information can serve as an effective tool for loan application fraud prevention at the earliest stages.

Monitoring of Existing Accounts For Suspicious Activity Patterns

A financial institution’s loan application fraud detection efforts should not just focus on new applications. Identifying cases of fraud in already-issued loans is key when it comes to minimizing your fraud losses.

Here are a few suspicious patterns you need to look out for:

• An account that uses up its credit lines shortly after it is created.
• Accounts with a dormancy period followed by a sudden increase in transaction frequency.
• Several accounts making payments to the same merchant from one device (via device fingerprinting.
• An account whose data points match those seen on high-risk accounts.
• Payments to merchants that seemingly have no plausible connection to the account holder (e.g. 18-year-old male from a small town in Kansas paying for ESL English lessons in Curaçao).

Identity Verification Tests to Prevent Loan Account Fraud

There are a variety of third-party identity tools and methods that can greatly improve your security systems. Using a combination of them can help your financial institution lower its risks of getting defrauded. Read this article to get more information about credit card fraud detection.

Identity Verification Testing

The most common, widely used loan fraud detection method is identity verification testing. This can come in a wide variety of forms.

You may ask the user to verify their identity via a real-time selfie or have one of your representatives call them.

The most common way of identity verification testing, however, are personal questions the answers to which only the client can know.

The main downside to identity verification testing is that it invariably introduces friction into the banking process and a frustrated client may elect to take their business elsewhere.

To develop a reliable money transfer app, check this article. 

Identity Verification with Enriched Data Collection

As we mentioned in our article on CNP fraud prevention, data enrichment is one of the most effective tools for detecting fraudulent financial activity. Data enrichment solutions can take the information you already have about your client and use sophisticated algorithms to gain additional data about the account holder.

If your client is a real person, the service will find a lot of additional information about them (no matter how “off-grid” they are). Your security system can then use this enriched information to better assess the risk associated with the client. If the client’s name appears in registers of known payment dodgers or databases of stolen IDs, your team can decide on a course of further action.

On the other hand, if the system can’t find any (or can only find minimal) information about the account holder, you are likely dealing with a case of synthetic identity fraud.

Machine Learning-Based Document Verification

One of the most exciting new developments in the field of preventing finfraud is AI-powered document verification. While criminals have found clever ways to bypass traditional identity document verification methods, AI-powered tools are much more difficult to fool. 

A properly configured AI-powered document verification tool can:

• Recognize ID documents from various countries of territories.
• Run a near-instant check for document inconsistencies.
• Accurately recognize biometric data using state-of-the-art facial recognition.
• Detect the use of deep fakes, masks, and other face alteration methods.

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:

Final Words

Fraudulent loans are responsible for an astonishing 20% of all bad credit held by banks and other types of financial institutions. A single fraudulent loan costs the lender an average of 6,000 USD. Today’s fraudsters are much more technologically sophisticated than those of yesteryear and routinely use deep fakes and advanced image editing techniques.

Having quality loan application fraud detection measures in place is paramount for every financial institution.

Proper in-house security techniques and third-party AI-based solutions can protect financial institutions and their clients from the negative impact of fraud on loan applications.

The post Detecting and Preventing Loan Application Fraud with AI-Powered Online Document Verification appeared first on SDK.finance - White-Label Digital Banking Software.

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.

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