Lending Strategy and Economics

A one-page AI use-case review template for lending teams to evaluate workflow fit, risk, explainability, controls, and alternatives before moving forward.

A practical guide to separating strong AI use cases from weak ones in lending, with a clearer lens for choosing between AI, rules, and analytics.

AI can create real value in lending. It can also create token cost, governance burden, integration friction, and explainability problems when used where rules, code, or analytics would do better.

Lending teams should stop asking “Can we use AI here?” and start with the workflow problem, decision consequence, and best tool for the job.

Most lending stacks have improved data access and decisioning tools, but outcomes still lag. Carrington Labs’ Chief Executive Officer (CEO) and Chief Product and Commercial Officer explain what’s missing—and how to close the “donut hole” with decision-ready analytics.

If your underwriting stops at spend categories, you may be declining good borrowers and capping safe exposure. Here’s how to measure cash flow impact at the margin.

Data is becoming easier to acquire. Models are becoming easier to build. Neither guarantees better outcomes. Differentiation will come from the ability to translate messy, real-world behavior into explainable, policy-ready inputs.

Decision engine vs risk analytics explained for lenders: what each layer does, when an end-to-end platform makes sense, and how to choose based on execution vs outcomes.

Cash flow underwriting, cash flow servicing, and purposeful AI use led conversations at the world's largest fintech event

A product-led credit risk approach brings risk and product together to raise approval rates, reduce volatility, and improve portfolio margins.

Joining the award-winning program marks a major step in Carrington Labs’ global strategy as it looks to bring its plug-and-play credit risk scoring platform to more lenders, fintechs and financial institutions in the US.