FintechRisk Assessment & AI Lending20246 months
Neural Credit Risk Engine
94.2%
Default Prediction Accuracy
Up from 61% with the legacy rule-based system
8 seconds
Processing Time
Down from 18+ hours for full underwriting decision
31%
Portfolio Loss Reduction
Annualized reduction in net charge-offs
2.7×
Loan Volume Growth
Increase in monthly application throughput
The Challenge
A regional lending institution was processing over 2,400 loan applications monthly using a legacy rule-based system with a 34% false-positive rate on defaults. Manual underwriting consumed 18+ hours per application, limiting growth and creating inconsistent credit decisions.
Our Solution
We engineered a multi-layer neural credit risk model trained on 7 years of proprietary transaction data, behavioral signals, and alternative data sources including rent payment history and utility records. The system delivers real-time risk scores with interpretable AI explanations (SHAP values) for regulatory compliance, integrated into their existing loan origination workflow.
Key Outcomes
94.2%
Default Prediction Accuracy
Up from 61% with the legacy rule-based system
8 seconds
Processing Time
Down from 18+ hours for full underwriting decision
31%
Portfolio Loss Reduction
Annualized reduction in net charge-offs
2.7×
Loan Volume Growth
Increase in monthly application throughput
"AIBrigade delivered a credit risk system that our regulators actually praised. The interpretability layer was a game changer — we can now explain every decision to examiners with confidence."
S
Sarah Chen
Chief Risk Officer · MidWest Capital Partners
Project Details
ClientMidWest Capital Partners
IndustryFintech
Duration6 months
Year2024
Tech Stack
PythonTensorFlowApache KafkaAWS SageMakerPostgreSQLFastAPIDockerGrafana
Tags
Machine LearningRisk ModelingPythonAWSReal-time Analytics
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