Finance
ML models that evaluate creditworthiness using bureau data, alternative signals, and cash flow patterns — producing risk scores that outperform traditional scorecards.
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Industry overview
Credit risk models that combine bureau data, alternative data sources, and transactional cash flow patterns to produce borrower risk scores for automated or assisted lending decisions.
At a glance
Traditional credit scorecards exclude a large portion of creditworthy borrowers — particularly thin-file individuals and SMEs with non-standard income. ArrayMatic builds ML-based credit models that incorporate bank statement cash flows, payment history, and alternative data to assess creditworthiness more accurately and fairly.
We develop and validate credit scoring models against lender historical portfolios, integrate them via decisioning APIs, and provide explainable score breakdowns that satisfy regulatory requirements. Portfolio-level monitoring alerts when model performance drifts — ensuring accuracy holds as economic conditions change.
Key capabilities
Engagements are scoped to your business context — these are the core capabilities we bring to finance clients.
Alternative data credit models for thin-file borrowers
Cash flow and bank statement income analysis
Real-time risk decisioning APIs for origination systems
Explainable score breakdowns for regulatory compliance
Bureau data and open banking integration
Portfolio-level risk monitoring and model drift alerting
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