Make better underwriting decisions
Your models should be as personal as your customers. With Chalk, your credit analysis is customized to your requirements, and can change as fast as your business. Empower your teams with the freedom to solve the particular problems that are most important to your business.
Understanding credit-worthiness is a complex task. Data scientists need to explore and test new ideas, without worrying about breaking the existing features. With Preview Deployments, chalk allows developers and data scientists to test out their theories alongside your deployed application without polluting your on-going underwriting.
Use Chalk's time-travel functionality to backfill new and updated features so that you can see how they would have impacted past decisions. When you like what you see, launch the changes to production immediately.
Chalk makes it easy to fetch credit data only when it’s needed. Each model specifies exactly the data staleness that it can tolerate to give you fresh data cheaply. By employing a layered approach to credit, you can reject bad candidates without fetching expensive data.
Great credit models rely on dozens of data vendors and constant iteration to stay profitable. Credit reports and FICO scores are the tip of the iceberg. The best teams incorporate alternative data like Plaid transactions, Rutter accounting data, merchant data, and product usage to build a comprehensive risk profile. With Chalk, engineering teams can add robust integrations as fast as your data science teams propose them.
Credit doesn’t end with issuance. After a loan is made, analysts often interrogate defaults, lawyers must answer regulators, and compliance teams periodically confirm that underwriting decisions were not based on protected data classes. Chalk perfectly tracks decisions from raw data sources to the feature values that power models, empowering teams to deeply understand the provenance of each decision.
The feature distribution of your target population will change over time. Easily differentiate between organic shifts and unexpected format changes in upstream data sources, or development mistakes. Get alerted automatically before issues cause defaults.