Credit Scoring
ML models using traditional and alternative data for better predictions.
Credit Risk Agent
Smarter Credit Risk Decisions
Evaluate creditworthiness with 25% better predictions using AI that analyses traditional and alternative data for comprehensive risk assessment.
Free expert consultation
The Challenge
Credit Risk in a Changing World
Traditional credit models miss risks and opportunities. Economic volatility demands faster, more nuanced risk assessment.
Thin File Borrowers
Traditional scores miss creditworthy customers lacking credit history.
Economic Volatility
Static models cannot adapt to rapidly changing economic conditions.
Portfolio Monitoring
Early warning signals are often detected too late for intervention.
Regulatory Pressure
Fair lending requirements demand explainable, unbiased decisions.
Traditional Approaches Fall Short
Credit scores miss 30%+ of creditworthy borrowers
Models lag economic conditions by months
Portfolio problems detected after delinquency
Limited ability to explain credit decisions
Inconsistent risk assessment across products
Manual portfolio reviews are slow and incomplete
The Solution
AI-Powered Credit Intelligence
Our Credit Risk Agent uses machine learning to evaluate creditworthiness more accurately, monitor portfolios continuously, and provide early warning of emerging risks.
Alternative Data
Incorporate cash flow, rent payments, and behavioural data for fuller picture.
Dynamic Scoring
Models adapt to changing economic conditions in real-time.
Portfolio Monitoring
Continuous risk assessment with early warning signals.
Explainable AI
Clear reasons for every decision to support fair lending compliance.
Key Features
Comprehensive Credit Risk Capabilities
Income Estimation
Estimate income from cash flow patterns when documentation unavailable.
Portfolio Analytics
Real-time portfolio risk metrics with segment analysis.
Early Warning System
Detect deteriorating credits before delinquency occurs.
Stress Testing
Scenario analysis for economic downturns and rate changes.
Fair Lending Analysis
Bias detection and disparate impact analysis for compliance.
How It Works
How Credit Risk Works
Data Integration
Gather bureau, alternative, and behavioural data for comprehensive view.
Risk Assessment
ML models evaluate creditworthiness with explainable factors.
Continuous Monitoring
Track portfolio health with early warning indicators.
Action Recommendations
Suggest interventions for at-risk accounts before problems escalate.
Better Risk Decisions
25%Better PredictionsDefault prediction accuracy
30%More ApprovalsFor creditworthy applicants
40%Earlier WarningDays before traditional signals
15%Loss ReductionThrough early intervention
Integrations
Credit Data Integration
Credit Bureaus
Local credit bureausInternal credit history
Alternative Data
Account transaction dataPayroll dataUtility and telecom data
Lending Systems
Loan origination systemsCore bankingCollections
Use Cases
Credit Risk Use Cases
Origination Scoring
Better credit decisions at point of application.
25% improvement in default prediction
Portfolio Monitoring
Continuous tracking of credit quality across the portfolio.
40% earlier detection of deterioration
Credit Line Management
Optimise credit limits based on real-time risk assessment.
20% increase in profitable exposure
Collections Optimisation
Prioritise collections efforts based on recovery likelihood.
30% improvement in recovery rates
Technical Specifications
Technical Specifications
Scoring
- Data Sources
- 50+ integrated
- Features
- 1,000+ analysed
- Scoring Time
- < 1 second
Monitoring
- Refresh Frequency
- Daily to real-time
- Alert Latency
- < 24 hours
- Portfolio Size
- Unlimited
Compliance
- Explainability
- Full adverse action
- Fair Lending
- Bias testing and adverse action reasons
- Model Governance
- SR 11-7 aligned
FAQ
Frequently Asked Questions
Our models are tested for disparate impact and include bias mitigation. Every decision includes adverse action reasons compliant with ECOA and FCRA.
Yes. Alternative data including cash flow, rent payments, and behavioural signals help score applicants with limited traditional credit history.
ML models detect behavioural changes that predict future delinquency, often 30-60 days before traditional warning signs appear.
Integration typically takes 6-10 weeks. The agent connects to credit bureaus, your core banking platform, and alternative data sources through pre-built connectors and APIs.
Yes. Models are trained on your historical loan performance data and can be tuned to reflect your risk appetite, product mix, and target market segments.
Most institutions see ROI within six months through higher approval rates on good borrowers and lower losses from earlier intervention on at-risk accounts.
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Ready for Better Credit Decisions?
See how our Credit Risk Agent can improve predictions by 25% while expanding access to credit.
Free expert consultation - Takes 15 minutes