Predictive Credit Risk AI

Forecast defaults accurately. Reduce losses. Approve creditworthy applicants faster. Scale lending responsibly.

90%+

PD Accuracy

30-50%

Loss Reduction

<1min

Approval Time

The Problem

✗ Traditional credit scores miss key signals (gig income, rent payments, utilities)
✗ Manual underwriting is slow and inconsistent
✗ Bad debt and defaults caught too late
✗ Limited inclusion for thin-file borrowers

Our Solution

✓ Alternative data + ML: 90%+ PD accuracy
✓ Instant scoring: Sub-minute decisions
✓ Early warning: Catch delinquency before it happens
✓ Explainable decisions: SHAP/LIME for transparency

What's Included

Probability of Default (PD)

Model likelihood of 12-month default with 90%+ accuracy

Loss Given Default (LGD)

Estimate loss severity after default; optimize recovery strategies

Instant Credit Decisioning

Real-time scoring at loan origination; <1 minute approval

Early Warning System

Identify delinquency signals 60-90 days before default

Alternative Data Integration

Rent, utilities, gig income, e-commerce, telecom payments

Explainable AI Scoring

Why was this applicant approved/denied? Show the reasoning

How We Deploy

Cloud

AWS/Azure/GCP - scalable, on-demand scoring

On-Premises

Full data control, air-gapped compliance

Hybrid

Data on-prem, models/training in cloud

Key Use Cases

1Loan origination scoring (instant auto-decisioning)

2SME/business lending with alternative data signals

3Early delinquency detection for pre-emptive collections

4Portfolio risk segmentation and capital optimization

5Alternative credit bureau data (rent, utilities, gig income)

6Regulatory compliance (Basel III, IFRS9, CECL, fair lending)

7Credit limit optimization and offer personalization

Implementation Timeline

Week 1

Assessment

Audit credit portfolio, data sources, regulatory requirements

Week 2-3

Pilot

Build PD models on pilot segment; validate accuracy vs. holdout

Week 4+

Production

Deploy scoring APIs; integrate with lending platforms

Ongoing

Optimization

Monitor model drift; retrain quarterly; expand to new segments

Regulatory Framework

Basel III

Regulatory capital aligned with PD/LGD/EAD estimates

CECL / IFRS9

Expected credit loss provisioning at loan origination

Fair Lending

Ensure credit decisions are non-discriminatory; audit for bias

Explainability

Transparent reasoning for approval/denial decisions

Technology Stack

Credit Risk Models

Supervised Learning

• XGBoost, LightGBM - 90%+ PD accuracy
• Random Forests - Feature importance ranking
• Neural Networks - Non-linear patterns

Unsupervised Learning

• Isolation Forest - Detect anomalous borrowers
• DBSCAN, K-means - Borrower segmentation
• Autoencoders - Latent risk patterns

Risk Metrics & Probabilistic Methods

PD/LGD/EAD

• Probability of Default - 12-month default likelihood
• Loss Given Default - Recovery rate estimation
• Exposure at Default - Credit utilization models

Advanced Methods

• Bayesian Networks - Causal credit relationships
• Survival Analysis - Time-to-default modeling
• Monte Carlo Simulations - Portfolio stress testing

Data Integration & Explainability

Data Sources

• Credit bureau data (payment history, inquiries)
• Alternative data (rent, utilities, telecom, gig)
• Bank transaction data (cash flows, stability)
• Public records (liens, judgments, bankruptcies)

Explainability

• SHAP - Global + local explanations
• LIME - Local interpretable approximations
• Feature Importance - Why was decision made?

Infrastructure & APIs

Real-Time Scoring

• FastAPI (sub-second inference)
• Redis (caching for quick scores)
• PostgreSQL (credit history storage)

Integration

• Loan origination APIs
• CRM & decisioning engine connectors
• Regulatory reporting automation

Business Impact

→Loss Reduction: 30-50% reduction in default rates through better risk selection
→Speed & Scale: <1 minute approvals vs. hours for manual underwriting
→Financial Inclusion: Approve creditworthy thin-file borrowers previously denied
→Compliance: Meets Basel III, CECL/IFRS9, fair lending requirements with audit trails

Reduce Losses. Scale Lending Responsibly.

Predict defaults with 90%+ accuracy. Approve faster. Comply with confidence.

Schedule a Demo →

Quick Questions

What accuracy can we achieve for default prediction?

Our models achieve 90%+ accuracy for 12-month default prediction on standard portfolios. Accuracy depends on your historical data quality and portfolio type (consumer vs. SME).

How fast can we score loan applications?

Real-time scoring in <1 minute from application submission. Can integrate into your LOS for instant auto-decisioning.

Can we use alternative data?

Yes. Rent payments, utilities, telecom, gig income, e-commerce signals significantly improve predictive power especially for thin-file borrowers.

How do we comply with fair lending laws?

We perform comprehensive bias audits; use explainable AI (SHAP/LIME) to show decision reasoning; ensure no protected attributes drive decisions.

Does this meet Basel III and CECL requirements?

Yes. Our PD/LGD/EAD models are designed to align with regulatory capital and provisioning frameworks. We provide full documentation for audits.

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What's Included

Probability of Default (PD)

Model likelihood of 12-month default with 90%+ accuracy

Loss Given Default (LGD)

Estimate loss severity after default; optimize recovery strategies

Instant Credit Decisioning

Real-time scoring at loan origination; <1 minute approval

Early Warning System

Identify delinquency signals 60-90 days before default

Alternative Data Integration

Rent, utilities, gig income, e-commerce, telecom payments

Explainable AI Scoring

Why was this applicant approved/denied? Show the reasoning

Key Use Cases

1Loan origination scoring (instant auto-decisioning)

2SME/business lending with alternative data signals

3Early delinquency detection for pre-emptive collections

4Portfolio risk segmentation and capital optimization

5Alternative credit bureau data (rent, utilities, gig income)

6Regulatory compliance (Basel III, IFRS9, CECL, fair lending)

7Credit limit optimization and offer personalization

Implementation Timeline

Week 1

Assessment

Audit credit portfolio, data sources, regulatory requirements

Week 2-3

Pilot

Build PD models on pilot segment; validate accuracy vs. holdout

Week 4+

Production

Deploy scoring APIs; integrate with lending platforms

Ongoing

Optimization

Monitor model drift; retrain quarterly; expand to new segments

Technology Stack

Credit Risk Models

Supervised Learning

• XGBoost, LightGBM - 90%+ PD accuracy
• Random Forests - Feature importance ranking
• Neural Networks - Non-linear patterns

Unsupervised Learning

• Isolation Forest - Detect anomalous borrowers
• DBSCAN, K-means - Borrower segmentation
• Autoencoders - Latent risk patterns

Risk Metrics & Probabilistic Methods

PD/LGD/EAD

• Probability of Default - 12-month default likelihood
• Loss Given Default - Recovery rate estimation
• Exposure at Default - Credit utilization models

Advanced Methods

• Bayesian Networks - Causal credit relationships
• Survival Analysis - Time-to-default modeling
• Monte Carlo Simulations - Portfolio stress testing

Data Integration & Explainability

Data Sources

• Credit bureau data (payment history, inquiries)
• Alternative data (rent, utilities, telecom, gig)
• Bank transaction data (cash flows, stability)
• Public records (liens, judgments, bankruptcies)

Explainability

• SHAP - Global + local explanations
• LIME - Local interpretable approximations
• Feature Importance - Why was decision made?

Infrastructure & APIs

Real-Time Scoring

• FastAPI (sub-second inference)
• Redis (caching for quick scores)
• PostgreSQL (credit history storage)

Integration

• Loan origination APIs
• CRM & decisioning engine connectors
• Regulatory reporting automation

Business Impact

→Loss Reduction: 30-50% reduction in default rates through better risk selection

→Speed & Scale: <1 minute approvals vs. hours for manual underwriting

→Financial Inclusion: Approve creditworthy thin-file borrowers previously denied

→Compliance: Meets Basel III, CECL/IFRS9, fair lending requirements with audit trails

Quick Questions

What accuracy can we achieve for default prediction?

Our models achieve 90%+ accuracy for 12-month default prediction on standard portfolios. Accuracy depends on your historical data quality and portfolio type (consumer vs. SME).

How fast can we score loan applications?

Real-time scoring in <1 minute from application submission. Can integrate into your LOS for instant auto-decisioning.

Can we use alternative data?

Yes. Rent payments, utilities, telecom, gig income, e-commerce signals significantly improve predictive power especially for thin-file borrowers.

How do we comply with fair lending laws?

We perform comprehensive bias audits; use explainable AI (SHAP/LIME) to show decision reasoning; ensure no protected attributes drive decisions.

Does this meet Basel III and CECL requirements?

Yes. Our PD/LGD/EAD models are designed to align with regulatory capital and provisioning frameworks. We provide full documentation for audits.