FraudShield AI — Real-Time Transaction Risk Engine

FraudShield AI is a real-time transaction fraud detection engine built for fintech companies, neobanks, payment processors, and lending platforms. Traditional rule-based fraud systems catch old patterns but miss novel attacks. FraudShield combines gradient-boosted ML models with LLM-powered reasoning to detect fraud it has never seen before.

The Problem:

Global payment fraud exceeds $40B annually and grows 20% per year

Rule-based systems generate 60–90% false positives, blocking legitimate customers

New fraud patterns (synthetic identity, account takeover, bust-out) bypass static rules

Analysts spend hours reviewing flagged transactions manually

The AI Solution:

FraudShield runs a two-layer analysis on every transaction:

1. ML Layer — XGBoost model scores transaction velocity, device fingerprint, behavioral biometrics, and graph relationships between accounts in real-time

2. LLM Reasoning Layer — Claude analyzes the flagged transaction in context, explains the risk in plain English, and suggests the appropriate action (approve/deny/step-up verification)

Key features:

Sub-50ms scoring via optimized inference pipeline

Explainable AI — every decision has a plain-English reason

Adaptive learning — new fraud patterns update the model weekly without full retraining

Graph network analysis to detect fraud rings

Integrates with Stripe, Plaid, Marqeta, and custom payment stacks

How It Helps FinTech:

One digital lending platform reduced fraud losses by 58% in Q1 after deployment. False positive rate dropped from 8.2% to 1.1%, meaning fewer legitimate customers were blocked. The analyst team reviewing flagged transactions shrunk from 8 people to 3 — with better accuracy.

Real Use Case:

A neobank with 200,000 users was losing $180K/month to ACH fraud. After deploying FraudShield, losses dropped to $22K/month within 60 days. The plain-English explanations helped the 2-person fraud team quickly review edge cases, and adaptive learning caught a new bust-out fraud ring that hit competitors that same quarter.

Tech Stack: Python, XGBoost, Claude 3.5, Redis, Kafka, PostgreSQL, FastAPI, React dashboard.