Table of Contents hide Top 5 Effective Ways to Prevent Loan Application Fraud What is loan application fraud? Top 5 Effective Ways to Detect and Prevent Loan Application Fraud Top 5 Effective Ways to Prevent Loan Application Fraud Banks and financial institutions lose billions of dollars every year to credit fraud or loan application fraud. It is estimated that the US faces an average of 300,000 cases of credit fraud annually. According to the 2021 report by the Federal Trade Commission (FTC), almost 30% of all financial fraud complaints in the US involved identity theft. Synthetic identity fraud is also on the rise and has resulted in over six billion dollars of credit losses. The data from FTC revealed a sharp increase in loan application fraud, with auto-loan and lease fraud increasing by 105% between 2018 and 2019. Business and personal-loan fraud rose by 116% over the same time period. With this kind of fraud, financial institutions are almost guaranteed to have a loss with the average ranging around 6,000 USD in damages from a single instance of loan fraud. This makes it imperative for banks and financial institutions to find a way to mitigate the impact of fraudulent payments. What is loan application fraud? Credit fraud or loan application fraud involves malicious actors taking out a loan in their name or, by using the name of an unsuspecting victim which they never pay off. If they take the loan out in their own name, it is called first-party fraud. Most often, these criminals tend to use fake or stolen identities of third parties to take out the loan and this is called third-party fraud. Managed IT Services can help your organization stay safe from fraudulent loan applications. First-party and third-party frauds First-party fraud involves fraudsters maxing out their own credit lines (through loans or credit cards) using their own details. Once the credit is approved, they immediately convert it to cash by writing checks or maxing out their credit cards and promptly disappear. With most financial institutions focusing on speed inconvenience for taking out loans, first-party fraud has become quite easy to implement. Even though banks typically require identity verification with Social Security numbers, actually tracking down loan applicants requires time and effort to a much greater degree than the approval of the loan. Third-party frauds are typically much harder to detect than first-party frauds and much more common. Recent data indicates that nearly 40% of all application frauds involve third-party frauds. Third-party fraud involves identity verification with stolen or fabricated identities. With fraudsters using fresh identities or synthetic identities for a series of loan applications, it becomes challenging for banks to detect fraud. In fact, most third-party frauds are only detected when the victims directly contact the bank for clarification regarding the loan amounts. Due to the time lag between the processing of the loan application and the complaint, the fraudsters have quite a long window to disappear in leaving the bank to absorb the losses. Synthetic identities are created using a composite of disparate elements of real identities. For instance, they could merge the Social Security number of Joe with the physical address or bank account of Mary. This makes synthetic identities very hard to track because there is no single, individual victim who can contact the bank and inform them of the fraud. Third-party frauds make it necessary for financial institutions to implement advanced anti-fraud methods to mitigate the significant losses. Managed Security Services can help you implement machine learning-powered tools to prevent the use of synthetic identities. Top 5 Effective Ways to Detect and Prevent Loan Application Fraud ID verification and facial recognition ID verification and facial recognition are critical elements of the verification process. Most experts recommend taking a picture to be a routine element of the onboarding process with the device camera. However, it is up to the organization to ensure that the picture has sufficient image quality required for automatic analysis and verification. Many security vendors now offer SDKs that can be integrated into your mobile applications and some also offer web-based solutions. New methods of document verification now compare the face image extracted from the document with a “selfie”. The selfie is further subjected to a liveliness test to make sure that it was indeed taken from an alive individual. As part of your routine identity check, you should implement both image capture and the selfie at the time of the validation. Identity data validation Identity data validation consists of a verification match between the submitted authentication information against public and private databases. There are tools available for organizations to extract authentication data from an authorized document and compare it against provided information, and public and private databases. Bank Account Ownership Having a bank account is the minimum eligibility for almost all loan applications. The bank account ownership test verifies if the applicant actually has access to the bank account s/he claims to have. Establishing this is fairly easy as all the client has to do is to provide credential details of the bank account. The organization can also choose to authenticate via micro-deposits to the account. Phone and social media verification In this type of verification, you send a push notification through your app for out-of-band verification. This verifies that the phone being used for authentication is not just a VoIP number, but actually, a physical device registered to a mobile network. Social media verification helps verify a user’s identity based on social media activity and connections. With fraudulent identities, social media presence may be minimal to nil. Identity risk scoring Identity risk scoring tests employ a comparison between all submitted information with databases of reported stolen attributes and heuristics. This provides you with an overall risk score that judges the likelihood of the submitted identity being forged. This kind of testing is made possible through the evolution of machine learning techniques that now make scoring on identity attributes accurate and feasible. This kind of supervised machine learning solution studies multiple data points and matches that information with different databases. This quickly flags stolen identities and can even detect synthetic identities thanks to data points being referenced with multiple stolen identities. ML solutions also analyze past account activities, study past loans or credit lines that could have been defaulted on, and leverages geolocation data for fraud detection. About Nora: Nora Erspamer is the Director of Digital Marketing at New Charter Technologies, a group of companies specializing in 24/7 IT support services. She is an experienced marketer and sales strategist with a demonstrated history of working in various technology industries. Skilled in strategic campaign development, lead generation, and marketing automation software. Her blog can be found at https://newchartertech.com/blog/.