CBA Webinars

CBA Team Contact

Jake Fowler

What Risk and Compliance need to know for the new world of AI and Machine Learning in Consumer Banking



Nick Kiritz, Director and Lead Expert for Model Risk Management
Aaron Johnson, Director


Financial firms continue to increase their investments in AI and machine learning to improve service offerings and increase efficiency, even as risk managers and regulators struggle to keep up.  This webinar will focus on the most popular emerging use cases for AI and machine learning in consumer banking and their implications for risk and compliance managers tasked with controlling the incremental financial, reputational, and compliance risks. Risk and compliance stripes covered will include model risk, data governance, technology risk, operational risk, fair lending, and privacy.


  1. Definition of AI and machine learning and trends in its use in consumer banking
  2. Overview of potential risk and compliance implications of growing AI use in consumer banking
  3. Deep dive into several key applications of AI and machine learning, their potential and realized risks and steps consumer banks can take to mitigate those risks.


  1. Increased understanding of current AI and machine learning applications in consumer banking
  2. Greater understanding of the implications of increased AI use across risk and compliance areas, including deep dives into the areas with which attendees should be most concerned.
  3. Discussion of illustrative examples of recent AI and machine learning issues, and specific examples of risk and compliance framework enhancements attendees should consider.

Have you created an account yet? If you haven't logged in to the CBA registration site before, please click the Create Account button to set up your profile.  This allows the site to recommend programs of interest to you.

Nick Kiritz

Director, Promontory Financial Group, an IBM Company

Nick is Director and Lead Expert for Model Risk Management at Promontory Financial Group, an IBM company. He works with clients across a variety of regulatory and jurisdictional environments to enhance the efficiency and effectiveness of their model risk management programs. He provides clients with guidance on model risk management policies, procedures, compliance; standards-of-practice; and design and implementation of model risk management systems to meet regulatory and risk management objectives, including significant work in model risk management for AI and machine learning. Nick has also advised vendors of model risk management systems, including providing advice to IBM on development of its the Model Risk Governance module for OpenPages and development of the OpenScale platform for AI risk management.

Prior to joining Promontory, Nick served as a risk manager at Constellation Energy and Exelon, where he led market risk management for a large merchant generation fleet; helped manage risks related to electricity, fuel, interest rates, and foreign exchange; developed an economic-capital model; and shaped policy for market risk, trader control, and liquidity risk. Before his work in energy, he led development and management of Fannie Mae’s model governance and validation program and later led its economic-capital modeling group. While at the OCC, Nick led the quantitative portion of reviews of interest-rate risk, mortgage banking, and securitization. He also led development of the OCC’s first model for interest-rate risk and designed and oversaw the one-week program to train examiners in the model’s use. At McKinsey, he focused on financial services and worked on subprime credit, bank stress tests, financial crisis management, and strategic reviews for stock brokers and financial-infrastructure firms.

Nick earned a M.B.A. in finance and a B.A. in Economics and Development Studies from the University of California, Berkeley.  He is a CFA® charterholder and a certified Financial Risk Manager under the Global Association of Risk Professionals (GARP). He has published thought leadership in the area of model risk management in the Journal of Risk Model Validation, the RMA Journal, the ABA Journal  and Law360.

Aaron Johnson


Aaron draws on his extensive experience developing and validating models for pre-provision net revenue, stress testing, and consumer credit scoring to help Promontory clients manage risk.

He previously led validation of PPNR models at PNC and was a specialist in data analysis for the Public Company Accounting Oversight Board’s Center for Economic Analysis. During his earlier tenure at Promontory, Aaron developed and validated a variety of statistical models used in stress testing, provided analytical support for a large independent foreclosure engagement, served as a technical writer, and helped manage engagements in the quantitative group. Prior to that, he was a statistical analyst for TransUnion’s custom modeling group, where he developed consumer credit-default models for domestic and international clients, including credit unions, banks, and personal-finance companies. In this role, Aaron also validated statistical models, acquired a deep understanding of consumer credit data, audited the implementation of scoring models, and provided other custom analytics. Before TransUnion, Aaron developed databases for the University of Illinois at Chicago and implemented other initiatives that streamlined administrative functions.

Aaron earned an M.B.A. in finance and statistics from University of Illinois at Chicago, an M.A. in teaching English as a second language from Pennsylvania State University, and a B.A. in English literature from Washington University. He is a CFA® charterholder and a certified Financial Risk Manager under the Global Association of Risk Professionals (GARP).


12/12/2019 at 11:30 AM (EST)  |  60 minutes
12/12/2019 at 11:30 AM (EST)  |  60 minutes Please click the button to the right to join the webinar.