AI-driven fraud detection leverages artificial intelligence (AI) and machine learning to identify and prevent fraudulent activity in real-time. AI-powered fraud detection systems analyze large volumes of data, looking for anomalous patterns or unusual behaviors that might be otherwise missed by traditional, rule-based methods. Mitek's AI-powered tools for fraud detection help organizations to proactively detect and stop threats.
Use case / examples of AI-driven fraud detection
Payment fraud: Identifying fraudulent payments in real-time, monitoring for unexpected transaction patterns and any activity that doesn't match the customer's typical behavior (for example, including parameters like geographic location, time of day, merchant type, or large transaction amounts).
New account fraud: Using AI-powered tools and identity verification to prevent the opening of fraudulent accounts by analyzing an applicant's personal information and identity documents, their behavior, and their device used. This process can catch inconsistencies or red flags that suggest the application may not be legitimate, and highlight it for further review
Insurance fraud: Detecting fraudulent insurance claims by analyzing the claim details, their consistency with customer behavior, and past fraudulent claim data, as well as stopping account takeovers by verifying that the claimant is who they claim to be. This helps the system spot patterns that suggest a claim might be fraudulent.
Loan application fraud: Flagging suspicious loan applications with automated tools by checking applicant data against known fraud indicators. Techniques that assist in loan application fraud include verifying document authenticity, ensuring the applicant's biometric data is consistent, and using real-time risk scoring.
