Artificial Intelligence (AI) is the sought-after technology intervention that financial organizations are fast adopting in mini packets of automation, machine learning, robotics, analytics and more. These efforts are mainly for chatbots, repetitive task automation, credit scoring, consistent customer services, quantitative asset management, deep-dive behavior analysis besides effective fraud identification.
Our client, a leading financial firm in the US, struggles with the fact that digital financial fraud is a continuously evolving battle with few deterrents or interventions in place. Like most financial institutions today, it faces several technical challenges in implementing a fool-proof financial fraud detection framework.
We designed an AI-based Analytics tool suite to detect fraudulent transactions, collect evidence, and analyze data necessary for conviction. It incorporated Artificial Intelligence tools to learn and monitor users’ behavioral patterns, identify exceptions using warning signs of fraud attempts. Our fintech domain experts identified precision models with Machine Learning (ML) techniques to adapt claims management at various stages of the claim-handling procedure. The below visual captures four key methods in this context.
The highlight of our AI-integrated customized solution framework is the usage of a standard online payment environment to detect fraudulent transactions in real-time. This unique design focuses on an effective supervised learning engine with critical data analytics that enables high-performance fraud detection, thus improving the original data’s predictive value. The framework’s features exploit the discriminant properties of customer data by finding hidden patterns. Across all our successful AI executions, this AI framework significantly improves fraud detection rate and performance stability compared with a rule-based solution.
Business Impact/ Outcome
This advanced AI-driven fraud detection solution falls under the category of explainable AI unlocks significant benefits:
85% – 90% Accuracy in fraud detection rates in claims raised
Proportionate 80% – 85% increase in claims amount saved due to fraud
Reduction in the fraud alert rate (the percentage of daily transactions investigated manually) from 40% to 10%