Smarter risk scoring with AI that sees the full picture
Move beyond traditional credit scores with AI agents that analyze alternative data, behavioral signals, and market conditions to deliver more accurate risk assessments — expanding your addressable market while reducing defaults.
Built for Risk Officers, Credit Analysts & Portfolio Managers
The Problem
Why manual triage doesn't scale
Traditional Scores Miss the Full Picture
FICO and bureau scores rely on limited data, excluding thin-file and no-file customers and missing important risk signals.
Risk Reviews Take Too Long
Manual portfolio risk reviews are quarterly at best, missing rapid changes in borrower circumstances or market conditions.
Most portfolio reviews happen only quarterly
Default Prediction is Inaccurate
Legacy models have high miss rates for defaults, especially during economic shifts when historical patterns break down.
Thin-File Customers are Excluded
Millions of creditworthy individuals lack traditional credit history, forcing institutions to either reject them or take unquantified risk.
Results
Measurable impact from day one
40%
Better Default Prediction
AI models with alternative data significantly outperform traditional scoring for default prediction.
30%
More Approvals
Expanded data inputs allow safe approval of previously unscorable applicants.
50%
Faster Risk Decisions
Automated scoring and real-time monitoring accelerate risk assessment across the portfolio.
25%
Portfolio Loss Reduction
Better prediction and early warning systems reduce overall portfolio losses.
Capabilities
Everything you need for intelligent triage
Alternative Data Analysis
AI agents ingest and analyze alternative data sources — bank transactions, employment data, rental history — for a complete risk picture.
- Transaction behavior analysis
- Income stability modeling
- Spending pattern insights
Dynamic Risk Models
Self-calibrating models that adjust to economic conditions, portfolio performance, and regulatory changes in real time.
- Macro-economic sensitivity
- Sector-specific adjustments
- Stress testing scenarios
Real-Time Portfolio Monitoring
Continuous monitoring of portfolio health with early warning signals for deteriorating credits.
- Early warning indicators
- Concentration risk alerts
- Vintage performance tracking
Explainable Scoring
Transparent risk scores with clear reason codes and factor attribution for regulatory compliance and customer communication.
- Adverse action reasoning
- Factor contribution breakdown
- Model performance reporting
How It Works
Three steps to automated triage
Step 1
Collect & Enrich
Aggregate traditional and alternative data sources, clean and normalize for model consumption.
Step 2
Score & Assess
Run multiple risk models, generate composite scores, and flag anomalies for review.
Step 3
Monitor & Adapt
Continuously track portfolio performance, recalibrate models, and generate risk reports.
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Frequently asked questions
What does alternative data add to a credit decision?
Cash-flow patterns, behavioural signals, and market context describe applicants that a thin credit file simply cannot. That is what lets approvals rise by around 30% without raising losses, because the additional approvals are customers who were previously unscoreable rather than genuinely risky.
Can the model explain why an applicant scored as they did?
Yes. Explainable scoring returns the contributing factors and their relative weight for every decision, which is a regulatory requirement in most lending markets. It also lets credit teams challenge and tune the model rather than accept its output purely on trust.
Does risk scoring update after the loan is issued?
Portfolio monitoring re-scores exposures as borrower behaviour and market conditions change, so deterioration becomes visible early rather than at the point of default. Combined with roughly 40% better prediction at origination, portfolio losses across the book typically fall by around 25%.
Related Glossary Terms
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