Finance & Banking
Machine Learning Fraud Detection Saves $2M Annually for Fintech Lender
QuickLend Financial
18 weeks
8 specialists
95%
Fraud Detection Accuracy
$2M
Annual Savings
3x faster
Processing Speed
The Challenge
QuickLend Financial processed over 50,000 loan applications monthly and was losing approximately $4.2M annually to fraudulent applications.
Our Solution
We built a multi-layered ML fraud detection pipeline combining gradient-boosted decision trees with deep learning anomaly detection.
Measurable Results
95%
Fraud Detection Accuracy
The ML model identifies 95% of fraudulent applications.
$2M
Annual Savings
Direct fraud losses decreased by $2M in the first year.
3x faster
Processing Speed
Legitimate applications are processed 3x faster.
“We were skeptical that AI could outperform our seasoned fraud analysts, but the results speak for themselves.”
James Park
VP of Risk Management, QuickLend Financial
Client
QuickLend Financial
Industry
Finance & Banking
Duration
18 weeks
Team Size
8 specialists