Financial services Usage scenario

Real-time fraud detection with graph analysis and machine learning

Detecting payment fraud in real time with a graph database and machine learning, with fewer false positives.

Describe your project See the solution

Project overview

This scenario illustrates what we build; it is not a client reference.

This scenario shows how a payment platform can replace static rules with real-time analysis. Machine learning and a graph database spot organised fraud patterns, while blocking fewer legitimate transactions.

Client challenges & requirements

A payment fintech sees a rise in elaborate fraud attempts (card fraud, account takeover).

Its static rules generate many false positives: legitimate transactions are blocked and users pay the price.

Main objectives

Detect complex and coordinated fraud patterns in real time

Reduce financial losses due to fraud

Decrease false positives so legitimate customers are not penalised

Provide analysts with tools to efficiently investigate suspicious cases

Project solution

The solution relies on a streaming architecture able to score each transaction in milliseconds. Events are ingested through Kafka and processed with Spark streaming.
Transactions are modelled in a graph database (Neo4j) to quickly spot suspicious links between accounts, devices and cards. A machine learning model computes a risk score for each transaction.

Indicators to track

Fraud-related losses
False positive rate
New fraud patterns detected
Average investigation time per case

Tech stack

Streaming: Apache Kafka Spark streaming
Graph database: Neo4j
Data Science: Python Scikit-learn XGBoost
Cache: Redis
Infrastructure: Kubernetes Docker

Frequently asked questions

Graph databases like Neo4j are built to find hidden connections between entities (accounts, devices, cards). They quickly identify coordinated fraud networks that traditional relational databases struggle to detect.

The system is designed to evaluate each transaction before it is approved, in milliseconds. Fraudulent transactions are blocked in real time, without slowing down legitimate customers.

The machine learning model is retrained regularly with the latest data. The graph approach also spots abnormal patterns never seen before, which protects against new fraud techniques.