ML-Powered Fraud Monitoring on Databricks

State of the art AI fraud detection solution on Databricks

MADIS replaced a rule-based fraud monitoring system with an end-to-end machine learning solution on Databricks that scores online payments and card transactions in real time.

At a glance

Sub-second

fraud probability scoring

2 models

dual-model architecture

Near-zero

missed fraud cases

Summary: .MADIS built an end-to-end machine learning solution with a dual-model architecture that continuously assesses the probability of fraud for online payments and card transactions – significantly reducing false positives while keeping missed fraud cases near zero.

The client

The client is a bank that processes online payments and card transactions.

The challenge

The bank’s fraud monitoring was based on predefined rules that fire when fixed conditions are met. The aim was to move from these rigid triggers to a continuous assessment of fraud probability – while keeping every decision explainable and auditable for regulatory compliance. The goal was to provide a new solution that is statistically higher than the rule based.

Our approach

Dual-model architecture

Two machine learning models deliver real-time fraud probability scores, covering online payments and card transactions.

Real-time scoring

Inference runs in under a second, so transactions can be assessed as they happen.

Explainability and auditability

Explainable AI and a full audit trail make every score traceable for regulatory compliance.

The solution

The delivered solution is an end-to-end machine learning system built on Azure and Databricks that replaces the legacy rule-based monitoring. Instead of rigid triggers, it continuously assesses the probability of fraud for each transaction.

Technologies: Azure Cloud · Databricks · Apache Spark · MLflow · Python

Results

• Fewer false alarms: significantly fewer false positives than the rule-based system.
• Fraud stays caught: missed fraud cases kept near zero.
• Proven improvement: a proven gain in effectiveness over the legacy rule-based system.
• Compliance-ready: full auditability through explainable AI.

Facing a similar challenge?

Thinking about moving from rule-based monitoring to machine learning? Our AI team can help you get there safely.