Page

AI in Financial Services


Overview

Vestibulum id ligula porta felis euismod semper. Curabitur blandit tempus porttitor. Donec ullamcorper nulla non metus auctor fringilla. Nullam quis risus eget urna mollis ornare vel eu leo. Aenean eu leo quam. Pellentesque ornare sem lacinia quam venenatis vestibulum. Donec ullamcorper nulla non metus auctor fringilla.


Research



Related Publications


Events


  • AI Symposium 2025

    The FinRegLab AI Symposium 2025 offers a special forum for collaborative dialogue at the nexus of financial innovation, public policy, the market, and society. Attendees include policymakers, technology leaders, banking and fintech executives, consumer advocates, and nonprofits to explore how we can, and should, deploy AI across the financial system to reap its powerful capabilities…

    Learn More: AI Symposium 2025
  • Federal Reserve Bank of St. Louis Conference: “Crossing the Credit Barrier”

    Join us to explore research and analysis on the landscape around affordable credit and banking for lower-income communities.

    Learn More: Federal Reserve Bank of St. Louis Conference: “Crossing the Credit Barrier”

About FinRegLab

FinRegLab is an independent, nonprofit organization that conducts research and experiments with new technologies and data to drive the financial sector toward a safe and responsible marketplace. The organization also facilitates discourse across the financial ecosystem to inform public policy and market practices. To receive periodic updates on the latest research, subscribe to FRL’s newsletter and visit www.finreglab.org. Follow FinRegLab on LinkedIn.

FinRegLab.org | 1701 K Street Northwest, Suite 1150, Washington, DC 20006

AI in Financial Services


This report examines broad implications of using AI in financial services. While recognizing the potentially significant benefits of AI for the financial system, the report argues that four types of challenges increase the importance of model transparency: data quality issues; model opacity; increased complexity in technology supply chains; and the scale of AI systems’ effects. The report suggests that model transparency has two distinct components: system transparency, where stakeholders have access to information about an AI system’s logic; and process transparency, where stakeholders have information about an AI system’s design, development, and deployment.