Artificial Intelligence in Banking and Financial Services: Transparency, Auditability, Avoiding Bias

A live 90-minute CLE webinar with interactive Q&A

Thursday, February 21, 2019

1:00pm-2:30pm EST, 10:00am-11:30am PST

Early Registration Discount Deadline, Friday, February 1, 2019

or call 1-800-926-7926

This CLE webinar will examine the regulatory issues associated with financial services companies using artificial intelligence (AI) and machine learning to make loan underwriting decisions. The discussion will include steps that banks and their counsel can take to address cultural biases in AI algorithms, provide better transparency to regulators, and mitigate the risk of civil or regulatory actions.


Right now there is a unique, multibillion dollar opportunity for banks, fintechs and other financial services companies: There is a vast, untapped market of potential borrowers who need loans, but who lack a traditional credit scores. Indeed, according to a 2015 Report by the Consumer Financial Protection Bureau, there are 44 million Americans who lack a traditional credit score.

Banks, fintechs, and other financial services companies are now beginning to reach this virgin market by using Artificial Intelligence (machine learning) and alternative data to assess the creditworthiness of borrowers who lack traditional credit scores. The first to arrive are likely to profit enormously, all while helping underserved borrowers.

But some financial services companies risk missing this opportunity because they are concerned about regulatory risks and public criticism. Those concerns are valid—but not insurmountable. Among other things:

  • AI algorithms can be biased (e.g., disfavoring protected classes when granting loans), risking violation of the Equal Credit Opportunity Act (“ECOA”) and similar state laws;
  • AI algorithms are notorious for being a “black box,” the workings of which are hard to see and understand, yet FCRA and ECOA require that consumers receive adverse action notices explaining reasons why consumers were denied credit (or subject to other adverse credit-related determinations);
  • Many U.S. laws or regulations are designed with the expectation of auditability and transparency, but the underlying complexity of AI and ML-based models raises particular challenges. As algorithms are deployed in increasingly high-value decisionmaking, such as determining who gets access to credit, financial service providers must be able to explain their processes to regulators.

Listen as our authoritative panel discusses the regulatory and operational issues presented by the application of Artificial Intelligence (machine learning) in credit underwriting.



  1. How machine learning and alternative data are being leveraged to reach new borrowers
  2. How bias finds its way into Artificial Intelligence and the legal risks that poses
  3. How to provide adverse action notices based upon Artificial Intelligence decisions
  4. Auditability, transparency and model valuation: concerns of regulators with "black box" algorithms
  5. Steps that lenders and their counsel can take to address cultural biases in AI algorithms, and mitigate the risk of civil or regulatory actions
  6. Steps that lenders can take to provide better transparency to regulators


Our experienced panel will review these and other notable issues:

  • How to minimize the risk of bias and utilize AI with peace of mind
  • How to comply with adverse action requirements
  • How to tackle model validation challenges


Lakatos, Alex
Alex C. Lakatos

Mayer Brown

Mr. Lakatos practices in complex international litigation, particularly on behalf of non-US financial institutions. He...  |  Read More

Additional faculty
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