Episode
EP 44: Human-Centered Credit - Building Explainable AI for Lending in an Agentic World
- Podcast
- Data Science With Sam
- Published
- Jul 4, 2026
- Duration seconds
- 1306
- Processing state
not_requested- Canonical source
- https://rss.com/podcasts/data-science-with-sam/3006404
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Summary
We are letting AI make life-changing financial decisions without requiring it to explain itself. The CFPB says 'insufficient information' is no longer an acceptable reason to deny a loan. The EU AI Act classifies credit scoring as high-risk. And more than half the US workforce - gig workers, fractional leaders, solopreneurs - is still scored by models built for a 1950s economy. Tamara Laine is the Founder and CEO of MPWR AI and an Emmy Award-winning investigative journalist turned fintech executive. Her reporting on ethical AI at Amazon and Northrop Grumman earned her an Emmy. Her work at MPWR - building explainable, policy-bound AI for the lending lifecycle - has earned her a seat in the Fintech Sandbox. IN THIS EPISODE: ▪ How Tamara's path from ballet to journalism to fintech all follows the same thread: spot a problem, find the expertise to solve it, create change ▪ Why half the US workforce is now a gig worker, creator, Gen Z worker, or thin-filed borrower - and why FICO models were never built to see them fairly ▪ What MPWR AI actually does: six policy-bound agents across the full loan lifecycle, pulling hundreds of data points to help lenders say yes more often ▪ The core architectural principle: AI does the work, deterministic models make the decisions - because LLMs can be biased and engineered to produce answers that are simply not true ▪ The two-button audit system: a decision audit and a bias audit, immediately available to any compliance team ▪ Patent-pending architecture: agents operate in defined information buckets — if the information isn't there, the system cannot answer and cannot hallucinate ▪ How MPWR measures whether it's actually expanding access: higher acquisition rates, lower defaults, reduced manual work - all benchmarked against lenders' own…