Case Study
Credit decisions
at machine speed
How GoLabs replaced a 72-hour manual scoring pipeline with an AI agent system that delivers credit decisions in under 3 seconds — with 35% better accuracy and twice the market reach.
Project overview
Industry
Fintech
Year
2024
Services
AI Agents · ML Engineering
Stack
Python · FastAPI · LightGBM · AWS
The Challenge
A scoring system built for a world that no longer exists
Traditional credit scoring methods were failing to serve a fast-growing fintech client. Manual reviews, thin data, and rigid models were blocking growth, creating risk, and leaving creditworthy applicants underserved.
Slow manual decisions
Credit assessments took 48–72 hours, frustrating applicants and capping the volume of loans the team could process each week.
Inaccurate risk models
Legacy scoring models relied on thin credit bureau data, achieving only 65% accuracy — driving higher default rates and missed prime applicants.
Limited market reach
Without alternative data sources, just 40% of the target demographic could be assessed, leaving a massive underserved segment untouched.
System architecture overview
The Solution
An agentic AI system that rewrote the rules of lending
Behavioral data ingestion
Ingested 200+ alternative signals — app usage, transaction patterns, device metadata — beyond thin bureau files.
Ensemble ML scoring engine
LightGBM + neural net ensemble replaced a single logistic regression model, trained continuously on new outcomes.
Agentic decision orchestration
AI agents route edge cases, request additional data, and escalate only truly ambiguous applications to human review.
Real-time API with explainability
Sub-3-second decisions served via FastAPI, with SHAP-based reasoning surfaced to loan officers in plain language.
Execution
Discovery
Mapped existing pipeline, data sources, and decision logic
Data
Ingested 200+ signals; built feature store on AWS S3 + Glue
Build
Trained ensemble model; built agent orchestration layer
Deploy
Canary rollout — 5% → 25% → 100% traffic over 6 weeks
Optimize
Continuous retraining loop; SHAP explainability dashboard
How we rebuilt trust in the decision pipeline
The team began with a deep audit of the legacy scoring logic and a 12-month dataset of loan outcomes. This revealed that 60% of defaults could have been predicted using signals already available — but never captured. We rebuilt the feature extraction layer first, piping transactional and behavioral data into a centralized feature store before touching the model.
Model development ran as three parallel tracks: a gradient-boosted tree for interpretability, a neural net for complex signal interaction, and an ensemble layer combining both. The final agent layer — built with LangChain orchestration — handles exception routing without human bottlenecks.
"We were losing potential customers every day. GoLabs gave us a system that makes decisions before a customer even finishes reading the confirmation screen."
Technology stack
Results
From 72 hours to 3 seconds — and twice the market
Faster decisions
0%
Average credit decision time dropped from 48–72 hours to under 3 seconds — across all applicant segments.
Better accuracy
0%
Model precision improved from 65% to 88%, reducing defaults and unlocking more creditworthy applicants.
Market reach
0×
Alternative data doubled the addressable population from 40% to 80% of the target demographic.
By replacing a rigid rules engine with an agentic ML system, the client moved from reactive risk management to proactive financial inclusion — processing 10× the monthly volume at half the operational cost.
Volume increase
0×
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