AI Credit Scoring and Risk Assessment with Amazon SageMaker

Credit risk decisions are critical for portfolio health, regulatory compliance, and growth. Traditional underwriting methods - relying on static rules and manual judgment - create variability, delays, and higher operational risk. Financial enterprises need a faster, more consistent way to assess creditworthiness at scale - without compromising control.

Rysun's AI Credit Scoring and Risk Assessment, built using Amazon SageMaker, is a ready-to-deploy, AWS-native accelerator that strengthens credit decisions across products, geos, channels, and customer segments while maintaining governance, auditability, and predictability. Built for risk, finance, and technology teams, this solution delivers a proven framework that aligns with regulatory requirements and modern data environments while remaining configurable to enterprise risk policies.

What This Solution Does At its core, the solution provides a predictive credit risk intelligence layer that evaluates creditworthiness using machine learning-driven insights derived from internal financial data and external credit signals from sources like Dun & Bradstreet, Experian, and Euler Hermes. Standardized risk scores and decision indicators support automated approval, decline, or review workflow - while preserving transparency and auditability.

Key Capabilities • Predictive credit scoring aligned with enterprise risk policies • Automated decisioning workflows with clear approval logic • Consistent risk evaluation across products and segments • Continuous model refinement with evolving data • Governance and auditability baked into the decisioning process

How It Works The solution applies machine learning models to assess credit risk using internal and third-party data. Standardized risk scores and decision indicators automate approval, decline, or review workflows, ensuring transparency and consistent decision-making. Models are configurable based on risk appetite and regulatory needs, providing continuous optimization as new data is fed into the system.

AWS-Native Architecture Built using Amazon SageMaker, AWS Lambda, Amazon S3, and Amazon CloudWatch, this solution ensures scalable, secure, and reliable credit risk management. Access control is handled via AWS IAM and Amazon Cognito, with architectures aligning to AWS best practices for security, scalability, and operational monitoring.

Proven Impact • 99.9% Availability: The solution is designed for high availability, ensuring reliable and uninterrupted service uptime. • 85%+ Model Accuracy: The machine learning model, powered by Amazon SageMaker, has achieved over 85% AUC-ROC on held-out test sets, ensuring reliable and consistent risk predictions.

Rysun’s credit risk accelerator enables organizations to standardize decisioning, extend existing risk frameworks, or validate model robustness - while strengthening governance and control.