FINTECH

ML Fraud Detection Engine

Real-time fraud detection system using ensemble ML models, preventing $5M+ in fraudulent transactions monthly.

10 months6 engineers13 min read

Key Results

Fraud Prevented$5M+/yr

Detection Speed<50ms

False Positive Rate0.3%

Model Accuracy99.4%

Client

Digital Banking Platform

Industry

Financial Technology

Location

Singapore

Overview

A digital banking platform processing 2M+ daily transactions was experiencing growing losses from sophisticated fraud attacks. Their rule-based detection system had a high false-positive rate (8%), causing legitimate customers to be blocked, and was unable to detect emerging fraud patterns fast enough.

We built a real-time ML fraud detection engine using ensemble models that analyze transactions in under 50ms. The system combines gradient boosting, neural networks, and graph-based analysis to detect both known fraud patterns and anomalous behavior, while keeping false positives under 0.5%.

The Challenge

Real-Time Inference

Score every transaction in under 50ms using complex ensemble models without adding noticeable latency to the payment flow or degrading user experience.

Class Imbalance

Fraud represents only 0.1% of transactions. The model must achieve extremely high recall on fraud while maintaining false positive rates below 0.5%.

Evolving Attack Vectors

Fraudsters continuously adapt their strategies. The system needs to detect novel fraud patterns (zero-day fraud) that have never been seen in training data.

Graph-Based Detection

Detect organized fraud rings through network analysis — identifying suspicious connections between accounts, devices, and transactions.

Our Solution

Architecture Overview

Streaming Layer

Kafka + Feature Store

ML Inference

TensorFlow Serving + Ensemble

Decision Engine

Rules + ML Orchestrator

Real-Time Feature Engineering

Built a streaming feature store using Kafka and Redis that computes 200+ features per transaction in real-time, including velocity checks, behavioral patterns, device fingerprints, and geographic anomalies.

KafkaFeature StoreRedis

Ensemble Model Architecture

Designed an ensemble combining XGBoost (for structured features), a deep neural network (for sequential patterns), and a graph neural network (for relationship patterns). Achieves 99.4% accuracy.

XGBoostDNNGNN

Low-Latency Inference Pipeline

Optimized the inference pipeline with TensorFlow Serving, model quantization, and feature pre-computation. Achieved P99 inference latency of 42ms, well within the 50ms budget.

TF ServingQuantizationOptimization

Adaptive Learning System

Implemented continuous model retraining with human-in-the-loop feedback from the fraud investigation team. New fraud patterns are incorporated within 24 hours.

MLOpsHITLContinuous Training

Performance Metrics

Transaction Throughput

Response Time Distribution

$5M+

Annual Savings

<50ms

Detection Time

99.4%

Accuracy

0.3%

False Positive Rate

Technology Stack

ML & AI

Python 3.11
TensorFlow 2.15
XGBoost

Streaming & Data

Apache Kafka
Redis (Feature Store)
PostgreSQL 15

Infrastructure

Kubernetes (GPU)
TF Serving
MLflow

Outcomes & Impact

Financial Impact

$5M+ in annual fraud losses prevented (95% detection rate)
False positive rate reduced from 8% to 0.3%, unblocking $12M in legitimate transactions
ROI of 15x on the project investment within first year

Detection Performance

99.4% model accuracy on production transactions
P99 inference latency of 42ms (well within 50ms budget)
Detected 3 novel fraud rings within the first month using graph analysis

Operational Efficiency

Fraud investigation team efficiency improved by 200% with AI-prioritized case queue
Automated retraining pipeline incorporates new fraud patterns within 24 hours
Dashboard provides real-time visibility into fraud trends and model performance

Regulatory Compliance

Model explainability reports satisfy regulatory requirements for AI in financial decisions
Complete audit trail for every fraud decision with feature attribution
Bias monitoring ensures fair treatment across demographic segments

“The results speak for themselves — $5M+ in prevented fraud, and our legitimate customers are no longer being blocked. The ensemble approach catches sophisticated attacks that our old rule-based system completely missed. BeluMind’ ML expertise is world-class.”