The Problem
On Monday you tested the 3-prompt framework in ChatGPT. You saw how data extraction → risk scoring → decision logic works. But here's the reality: manually running prompts for every loan application doesn't scale past 20-30 apps per day. One underwriter spending 4 hours copy-pasting between systems? That's $120/day in labor costs. Multiply that across a lending team and you're looking at $36,000/year just on manual risk assessment. Plus the inconsistency—different underwriters interpret data differently, leading to approval rate variance of 15-20% for similar applicants.
See It Work
Watch the 3 prompts chain together automatically. This is what you'll build—real-time credit risk scoring from raw application data.
Watch It Work
See the AI automation in action
The Code
Three levels: start simple with API calls, add reliability with error handling, then scale to production with ML pipelines. Pick where you are.
When to Level Up
Simple API Calls
0-100/day
- Basic prompt chaining (extract → score → decide)
- OpenAI/Claude API calls
- Manual review of all decisions
- Local storage or spreadsheets
- No caching or retries
Add Reliability
100-1,000/day
- Retry logic with exponential backoff
- Redis caching (1 hour TTL)
- Error handling and logging
- Database storage (PostgreSQL)
- Async processing for speed
- Basic monitoring (Winston/Sentry)
Production Pipeline
1,000-5,000/day
- ML model integration (XGBoost, neural nets)
- External API integrations (Plaid, Experian)
- Batch processing (50 concurrent)
- Database connection pooling
- Advanced caching strategies
- Real-time monitoring dashboards
- A/B testing for model versions
Enterprise System
5,000+/day
- Multi-agent orchestration (LangGraph)
- Distributed processing (Kafka/RabbitMQ)
- Auto-scaling infrastructure (Kubernetes)
- Advanced ML ops (model versioning, A/B testing)
- Real-time compliance monitoring
- Custom model training pipeline
- Multi-region deployment
- SOC 2 compliance tooling
Fintech-Specific Challenges
Credit risk modeling has unique compliance and data requirements. Here's what you need to handle.
Regulatory Compliance (FCRA, ECOA)
Log every decision factor, generate adverse action reasons, ensure no protected class bias
# Compliance logging
import logging
import json
from datetime import datetime
class ComplianceLogger:
def __init__(self, log_file='fcra_audit.log'):
self.logger = logging.getLogger('compliance')Real-Time Data Freshness
Implement tiered caching: cache demographics (1 week), cache credit data (24 hours), never cache bank balances
# Tiered caching strategy
import redis
import json
from datetime import datetime, timedelta
from typing import Dict, Optional
class TieredCache:
def __init__(self, redis_url: str):Model Explainability
Use SHAP values or attention weights to identify top contributing factors. Map to human-readable reasons.
# Model explainability with SHAP
import shap
import numpy as np
from typing import List, Dict
class ExplainableRiskModel:
def __init__(self, model):
self.model = modelProtected Class Bias Detection
Run bias audits on model outputs. Test approval rates across demographic groups. Use fairness constraints during training.
# Bias detection and mitigation
import pandas as pd
import numpy as np
from scipy import stats
from typing import Dict, List
class BiasAuditor:
def __init__(self, threshold_disparity: float = 0.8):Multi-Bureau Credit Data
Pull primary bureau first. If score is borderline (630-670), pull second bureau for confirmation. Use tri-merge only for high-value loans ($50K+).
# Smart multi-bureau strategy
import asyncio
from typing import Dict, List, Optional
class MultiBureauStrategy:
def __init__(self, primary_bureau: str = 'experian'):
self.primary = primary_bureau
self.costs = {Adjust Your Numbers
❌ Manual Process
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