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May 7, 2024
Impact of Face Match Technique on Credit Risk Management
Explore the role of face match technology in the lending industry and examine its effectiveness in credit risk management.
Explore the role of face match technology in the lending industry and examine its effectiveness in credit risk management.
According to forecasts, facial recognition market size is expected to reach $4.94 billion by the end of 2024. Over the following six years, it will demonstrate a CAGR of 9.34%, nearly doubling the market to $8.44 billion by 2030.
This trend is explained by the fact that face recognition apps, based on innovative technologies such as Artificial Intelligence and Machine Learning, help combat crime and fraud in many industries.
Credit organizations, in particular, use a face matching algorithm to enhance the efficiency of credit scoring.
Face match is a technology that compares multiple photos to determine whether the same individual is depicted in each image. Face matching algorithms analyze facial features in the images and check for similarities.
Face match search is utilized in many modern industries, including law, e-commerce, education, marketing, security, and more.
For example, face recognition attendance systems are widely employed in educational institutions, airports, and other crowded places.
In this article, we focus on the role of face match in the online lending industry, as credit organizations often employ this technology to verify the identities of potential borrowers.
Synthetic identities are a special form of fraud, involving the creation of a fake identity by combining genuine and false information about a person for malicious purposes.
For example, a fraudster may steal a real social security number to create a new, non-existent identity. These identities are then used to deceive government agencies, trading companies, and lending organizations.
Security Magazine reports that 46% of major global companies encounter synthetic identities in their operations.
The financial sector is most vulnerable to this type of fraud. 92% of surveyed organizations in this sector consider synthetic identities a real problem, with 49% having already encountered them firsthand.
Data from TransUnion shows that the amount of credit obtained in the US by fraudsters using synthetic identities is constantly increasing. In 2021, criminals obtained auto loans, credit cards, and unsecured consumer loans totaling $3.9 billion; in 2022, this amount increased to $5.6 billion.
Using face matching software, lending organizations can minimize risks associated with synthetic identities. This technology enables identity verification during the loan application process, helping to detect fraud and reduce defaults on issued loans.
Face match is based on facial biometrics—the identification and measurement of multiple data points obtained from a person's face image.
These can include distances between lips and nose, the spacing of the eyes, the shape of the cheekbones, and more.
The process of identity verification occurs as follows:
1. The specified data points are analyzed across multiple images of the applicant.
2. The system evaluates the likelihood of a match.
3. Based on the analysis, a conclusion is drawn whether the same individual is depicted in all the images.
Biometric facial recognition is complicated because several pictures may contain different elements. Images may appear different due to factors such as glasses, facial hair, hairstyles, and lighting.
However, face matching algorithms are trained to ignore these differences, focusing solely on consistent data points.
Additionally, the most advanced facial recognition systems can adapt to poor image quality.
Lending organizations employ face match for two primary purposes:
This technology plays a crucial role in fraud prevention thanks to reliable identity verification mechanisms. It ensures that the applicant is indeed the person they claim to be.
As discussed earlier, the face match technique enhances identity verification processes and reduces the risk of personal data theft and fraud. This enables more informed and sound credit decision-making, lowering the likelihood of defaults.
Face match is one of the essential services provided by RiskSeal. It greatly assists our clients – lending organizations – in combating synthetic identities.
In our practice, there are multiple cases where the face matching algorithm helped us identify a high probability of default.
Here's how it works:
1. A potential borrower submits a loan application.
2. RiskSeal analyzes all possible profile pictures from the applicant’s social media accounts and messengers. If the client provides us with a borrower's selfie, we also compare it to images found online.
3. If the analysis reveals inconsistencies in the user's photos, the borrower is flagged as suspicious and assigned a high default risk.
4. Conversely, if the potential borrower has multiple different photos that all match, their Trust Score increases, enhancing their chances of loan approval.
Face match is one of the many checks we perform within the RiskSeal solution. Its significance cannot be underestimated – it holds immense value for credit organizations and delivers exceptional results.
RiskSeal searches for all possible images of a potential borrower on the web, for example, profile pictures on social networks, and compares them based on various data points. If the client provides a selfie, that photo is also included in the comparison.
When the verification confirms a match, the applicant is deemed reliable, increasing their chances of obtaining credit. If the photos don't match, the borrower is considered suspicious and is unlikely to get credit.
Both technologies are used for verifying a person's identity, but they differ slightly.
Face match is a technology that allows you to compare multiple photos to determine whether the same person is depicted in them.
Face recognition is the process of comparing a photo from a person's document with their selfie during the loan application process or other actions requiring identification.
Face match technologies optimize identity verification, increasing the likelihood of detecting synthetic identity fraud. This technology makes it harder for fraudsters to create and use fake IDs.
Facial match technology compares multiple photos of a person and determines whether these images match based on numerous nodal points. If the photos don't match, fraud may be suspected.
The technology's advantage is that modern algorithms are trained to adapt to poor image quality and additional elements in photos, such as facial hair and glasses.
Face match takes into account facial landmarks, which display unique characteristics of facial features. These include distances between the eyes, width and height of the eyes, width and length of the nose, length and width of the chin line, height of the forehead, width of the cheekbones, and so on.
In the lending industry, a face match can indicate potential fraud based on discrepancies between a potential borrower's photos in various online accounts. This additional identity check helps make more informed decisions on loan applications and reduces the risk of default on issued loans.
Several signs may indicate synthetic identity fraud, such as discrepancies between the SSN and the applicant's name, inconsistencies between the information provided in the application and data from open sources (such as employment or education), and a limited set of provided documents.
If all of the above signs are absent, suspicion of fraud may arise from a negative result of identity verification using face match technology.
It's the process of automatic identification or verification of people based on their facial features, using computer vision and pattern recognition methods. It specifically focuses on analyzing and interpreting facial images for recognition and comparison.