Credit Scoring

Use AI to deliver fast, accurate, and inclusive credit scoring for better lending decisions.

Business impact

• Credit approval rate — More accurate scoring increases approvals for creditworthy but previously excluded borrowers
• Loan processing time — Automated AI scoring reduces manual review and accelerates loan decisions
• Default rate — Better risk prediction lowers loan defaults and associated losses
• Customer acquisition — Inclusive scoring expands customer base by reaching underbanked populations
• Operational efficiency — AI reduces manual effort and errors, improving productivity and cost savings

Data requirements

• Credit bureau data (Structured) — Provides historical credit and repayment records for model training
• Transaction and cash flow data (Numeric) — Captures real-time financial behavior to enhance creditworthiness assessment
• Alternative data (e.g., mobile usage, social media) (Text) — Includes nontraditional signals to score thin-file or unbanked consumers
• Identity verification data (e.g., facial recognition) (Image) — Ensures borrower identity and reduces fraud risk during application
• Loan application metadata (Structured) — Supports contextual understanding of borrower intent and risk factors

AI methods and techniques

• Predictive AI — Forecasts borrower default risk using historical and real-time data patterns
• Generative AI — Analyzes unstructured data and generates explainable credit risk insights
• Agentic AI — Automates credit decision workflows and customer advisory interactions

AI models and model families

GPT-4o, Claude, Llama, TabPFN, Gradient-Boosted Decision Trees

Industries

Real-world evidence

11 documented case studies on record.

Companies using this: Affirm Holdings Inc, Bank Rakyat Indonesia, Credit Sesame, Empower Finance, Experian, GFT, MYBank, Petal, Surfin, Temenos, Upstart Holdings Inc.