Atlas Financial Saves $3.2M Annually With AI Fraud Detection

Results at a glance

The Challenge

Atlas Financial was experiencing a 3.2% fraud rate on their personal loan applications — significantly above the 0.8% industry average. Fraud losses reached $3.8M in 2024. Their rule-based fraud detection was generating 12% false positives, blocking legitimate applications and increasing manual review overhead.

Our Solution

We built a machine learning fraud detection model trained on 3 years of Atlas's application data, incorporating 180+ behavioral, financial, and device signals. The model operates in real-time during application, providing fraud probability scores with explainable reasons — enabling automated decisions on clear cases and intelligent routing of edge cases.

How We Built It

1. 1

   Analyzed 3 years of historical applications with fraud labels to identify predictive signals

2. 2

   Built feature engineering pipeline processing 180+ variables per application in < 200ms

3. 3

   Trained gradient boosting model with custom loss function penalizing false negatives 4× false positives

4. 4

   Implemented model monitoring for data drift and performance degradation

5. 5

   Built explainability layer surfacing top reasons for each fraud score

6. 6

   Created tiered decision system: auto-approve, auto-decline, manual review