AI fraud intelligence platform

Inspiration

Financial fraud is evolving faster than traditional detection systems. While machine learning models can classify transactions as fraudulent or safe, they often lack interpretability. Banks and financial institutions do not just need predictions — they need explanations. We were inspired by a simple question: What if fraud detection systems could not only detect risk, but also explain it in human language? That idea became the foundation of our AI Fraud Intelligence Platform — combining supervised machine learning with Generative AI (Gemini) to create an intelligent, explainable fraud monitoring system.

What it does

We built a real-time AI-powered fraud detection dashboard that: Detects fraudulent transactions using a trained ML model Calculates fraud probability Generates AI-powered explanations using Gemini Visualizes risk using live dashboards and charts Maintains transaction history and risk trends This transforms fraud detection from a binary decision system into an intelligent monitoring platform

How we built it

Our architecture consists of three layers: 1️⃣ Machine Learning Layer (Detection Engine) We trained a Random Forest classifier on a real-world credit card fraud dataset. The model predicts fraud probability: Where: � = transaction feature vector � = probability that transaction is fraudulent The model also computes: Using: Since fraud datasets are highly imbalanced: We handled this using resampling techniques to prevent model bias toward non-fraud transactions.

Challenges we ran into

Imbalanced Dataset Fraud cases were extremely rare (~0.2%), causing the model to predict "safe" almost always. We solved this with resampling and threshold adjustment. 2️⃣ Model Interpretability Raw probabilities are not meaningful to non-technical users. We integrated Gemini to convert statistical outputs into actionable insights. 3️⃣ Frontend-Backend Integration CORS issues, API errors, and async handling required careful debugging. 4️⃣ Real-Time Visualization We implemented animated gauges and charts to simulate live monitoring systems

Accomplishments that we're proud of

What we learned

Machine learning models alone are not enough — explainability matters. Class imbalance significantly impacts predictive systems. Full-stack AI systems require integration across multiple technologies. Generative AI can enhance traditional ML systems by adding interpretability. Clean UI dramatically improves perceived product quality.