How AI Is Helping Lenders Prevent The Next Wave Of Credit Card, Loan Frauds

Artificial intelligence (AI) is rapidly changing how lenders are detecting and preventing frauds related to credit cards and loans with scams getting more sophisticated for traditional verification systems. From stolen identities and forged documents to synthetic borrowers and AI-generated manipulation, fraudsters are using technology to exploit gaps in the digital lending space. At the same time, lenders are also turning to AI to analyse the large volumes of customer and transaction data.

Nevertheless, the question remains whether AI can stay ahead of fraudsters as lending becomes more relied on digital services.

Biggest Forms Of Credit And Loan frauds Today

Lenders are dealing with identity theft, synthetic identities, document forgery, application fraud and account takeover. Fraudsters may use stolen personal information to apply for loans, manipulate income documents, or create synthetic identities by combining genuine and fabricated information. Another growing concern is first-party fraud, where borrowers deliberately misrepresent their financial position or intent to repay. Digital lending has also created opportunities for organised fraud rings that submit multiple applications using interconnected identities, devices and bank accounts.

Says Joydip Gupta, head, APAC region, Scienaptic, a software company: “The fraud that makes headlines is not always the fraud that creates the biggest losses for lenders. RBI data for FY26 makes that distinction quite stark. Banks reported 8,640 fraud cases in the advances category involving Rs 40,774 crore, compared to just 293 card, Internet and digital-payment fraud cases involving Rs 29 crore. The Reserve Bank of India (RBI) has also cautioned that frauds reported in a particular year may have originated in earlier years, so these numbers need to be read in that context.”

How Has Fraud Changed?

The rapid digitisation of lending has given fraudsters more channels to exploit. Generative AI has further raised the stakes by making it easier for fraudsters to create convincing documents, alter images, and impersonate individuals. Fraudsters can now also exploit stolen credentials and compromised devices at scale. As a result, lenders increasingly need to assess not just whether individual information appears genuine, but whether the overall application behaviour is consistent with a legitimate borrower.

“Earlier, fraudsters often needed stolen documents or access to a real victim’s identity. Today, generative AI can help create convincing employment records, income documents, photographs, and other supporting material in minutes. Synthetic-identity exposure is already at record levels in markets where it is closely tracked. The second shift is from individual fraud to networks. One fraudulent application may look completely normal. The pattern becomes visible only when you discover that dozens of applications share devices, addresses, bank accounts, employers or other digital signals. India’s own experience with mule accounts show the scale of this problem. By January 31, 2026, I4C's Suspect Registry had shared information on 2.73 million Layer-1 mule accounts with participating institutions,” Gupta adds.

Is AI More Accurate To Detect Fraud?

The strongest AI models combine multiple data points rather than relying solely on traditional credit scores. These can include repayment history, income and transaction patterns, loan application details, device information, IP addresses, geolocation signals, and behavioural patterns.

Network-level information can be particularly valuable. If multiple applications are connected through the same device, phone number, bank account or other indicators, AI can identify relationships that may not be visible through conventional underwriting.

Adds Gupta: “A sophisticated fraudulent application can look perfect in isolation. What is more difficult to fake is consistency across multiple independent data sources over time. That is where AI becomes particularly powerful.”

Can AI Help Prevent Identity Theft?

According to Gupta, AI cannot prevent someone’s identity from being stolen. What it can increasingly do is prevent a stolen or fabricated identity from turning into a sanctioned and disbursed loan.

“That distinction is important. At origination, AI can compare identity information across sources, analyse device and behavioural signals, detect unusual application velocity, identify connections to previously suspicious identities or accounts, and flag inconsistencies that a traditional checklist may miss. Liveness and deepfake detection are becoming increasingly important as well, because identity verification itself is now an attack surface,” he adds.

According to Gupta, the bigger opportunity lies in combining technologies and assessment systems. Instead of treating fraud detection as a final checkpoint, lenders can use AI throughout the process, he further says