AI-Powered Credit Scoring & Risk Evaluation

Our credit risk models typically deliver 30-40% improvement in predictive accuracy compared to conventional credit scores alone. This enhanced performance comes from three key advantages: 1) incorporation of alternative data sources that capture financial behaviors not reflected in traditional credit files, 2) advanced mathematical techniques that identify subtle patterns and relationships traditional regression models miss, and 3) customization to your specific portfolio and customer segments.

For thin-file and credit-invisible applicants, the performance difference becomes even more significant, with our models providing valid risk assessment for segments where traditional scores offer limited or no predictive value. Financial institutions consistently report 25-35% approval rate increases for qualified applicants while maintaining or improving portfolio performance metrics.

Regulatory compliance forms the foundation of our credit modeling approach, with explainability engineered into every stage of development and deployment. Our methodology uses transparency-compatible algorithms that maintain full interpretability while delivering advanced predictive performance. Each model includes comprehensive documentation identifying all factors influencing credit decisions and their relative importance.

For individual lending decisions, the system generates detailed reason statements explaining the primary factors that determined the outcome. This documentation satisfies regulatory requirements for adverse action notices while providing clear explanation of decision rationale. Our models undergo rigorous fair lending analysis before deployment, with ongoing monitoring to ensure consistent performance across all protected classes.

Yes, our platform connects with all major loan origination systems, including Fiserv, Finastra, nCino, MeridianLink, Temenos, and proprietary platforms through secure API integration. The solution can operate within your existing decisioning workflow, enabling phased implementation that maintains business continuity while delivering progressive performance improvements.

We’ve successfully integrated with over 200 different lending technology environments across the financial sector, including both modern API-based systems and legacy platforms with limited integration capabilities. Our flexible architecture adapts to your specific technical environment while preserving your existing lending infrastructure investments.

Our platform incorporates both traditional credit data and alternative sources based on your specific lending needs and available information. Beyond conventional bureau data, the system can utilize banking transaction patterns, utility and telecom payment history, rental data, public records, business financial information, and specialized industry datasets relevant to your lending categories.

Data quality validation forms a critical component of our implementation process, with automated monitoring that identifies accuracy issues, missing information, and potential biases. The system implements sophisticated handling for missing or inconsistent data, ensuring reliable risk assessment despite common data limitations. All data acquisition and usage complies with applicable privacy regulations and consumer protection requirements

Success metrics align with your specific business objectives, typically including approval rate increases, loss rate performance, operational efficiency, and regulatory compliance. Financial institutions generally experience 25-40% increases in approval rates for qualified applicants, 65-75% reduction in manual review requirements, and 15-25% improvement in portfolio performance through more precise risk segmentation.

We establish baseline measurements during initial assessment and track improvements against these metrics following implementation. Most clients achieve full ROI within 6-12 months, with specific returns depending on your current lending volume, target segments, and operational model. Our performance guarantees provide financial assurance of these improvements.

No, our implementation approach minimizes resource requirements from your organization. Implementation typically requires limited involvement from your team—usually 3-5 hours weekly from lending stakeholders and IT resources during the deployment phase. Our data scientists, credit specialists, and integration experts handle the technical aspects of model development, validation, and deployment.

After implementation, the solution operates with minimal maintenance requirements, with Aspagnul providing ongoing model monitoring, performance reporting, and refinement. This approach delivers enterprise-grade credit analytics without requiring you to build and maintain specialized data science capabilities internally, making advanced risk assessment accessible regardless of your organization’s size or technical resources.