AI-based Tax Fraud Detection
The Challenge
Refund advance loans are short-term loans that allow taxpayers to receive their tax refunds faster. They are offered by some financial institutions that partner with tax preparation services. The loan amount is based on the expected tax refund amount and is repaid when the refund is received from the IRS.
However, refund advance loans also pose a high risk of fraud. Some taxpayers may provide false or inaccurate information on their tax returns to inflate their refund amount and get a larger loan. If the IRS rejects or reduces the refund, the financial institution may not be able to recover the loan amount.
One of our clients, a major financial institution, needed a fraud detection model to identify and prevent fraudulent applications for refund advance loans. They wanted to use various data sources such as tax variables, credit behavior, and identity validation to detect suspicious patterns and anomalies.
The Solution
We worked with our client to develop a custom fraud detection model for their refund advance products. We used historical data from their previous loan applications and IRS acknowledgments to train and validate the model. We also applied advanced machine learning techniques to optimize the model performance and accuracy.
The fraud detection model we developed generated an application-level fraud score based on various risk factors. The higher the score, the more likely the application was fraudulent. The model also provided the reasons for the score, such as mismatched information, inconsistent behavior, or invalid identity.
We implemented the fraud detection model on our client’s platform and integrated it with their loan approval process. The model flagged applications with high fraud scores and challenged them with additional identity verification questions. The applicants had two days to respond to the questions or their applications were denied.
The Results
The fraud detection model we developed for our client was very effective in reducing fraud losses and improving loan quality. Within one tax season, our client saw a 20% decrease in fraudulent application approvals and a 35% savings in fraud dollar losses. This means that they avoided lending money to applicants who were unlikely to repay their loans.
Moreover, the quality of the loan applications also improved significantly. Since the fraud detection model screened out the applicants who provided false or inaccurate information, our client received more applications from genuine and eligible taxpayers. This increased their customer satisfaction and loyalty.