AI in the Financial Services Industry Conference Summary
By Guillaume Bolivard and Jean-Marc Charles, Quinlan School of Business, Loyola University Chicago
The second annual AI in the Financial Services Industry conference, organized by the AI Business Consortium, was held on June 27, 2024 at Loyola University Chicago. The event focused on the forefront of artificial intelligence innovations, applications, and execution in the financial services industry. This conference brought together a plethora of industry leaders, AI practitioners, and academic experts who shared their insights on the current advancements, challenges, and future directions of AI technologies.
The keynote address by Agus Sudjianto (former Executive Vice President, Wells Fargo and Head of Corporate Model Risk), titled “Performance is not all you Need: Developing and Validating High-Risk Machine Learning,” set the tone for the event. Sudjianto emphasized the necessity of robustness and governance in AI model development, highlighting that while performance metrics are vital, the models’ ability to maintain reliability under diverse conditions is equally crucial. He advocated for comprehensive model validation processes to ensure long-term reliability and safety, particularly in high-stakes applications.
Panel discussions further enriched the event, addressing critical issues such as risk model management, the regulatory environment and cybersecurity risks, and the strategic deployment of AI in financial services. The panels featured insights from leading experts who shared their experiences and best practices for navigating the complex AI landscape.
Panels and speakers included:
Gen AI for Process Improvement and Related Use Cases
- Gary Class, Industry Strategist for Financial Services, Teradata
- Donald High, Chief Data Scientist, IRS
- Ram Peddu, Chief Data and Analytics Officer for Risk and Finance, BMO Harris Bank
Blueprint for Success: Strategic AI Deployment in Financial Services
- Brent Demar, VP of Decisioning Technology, Discover Financial Services
- Arjun Ravi Kannan, Director of Data Science Research, Discover Financial Services
- Raghu Kulkarni, Chief AI Officer, Equifax
- Yixiu Li, Director of Generative AI Modeling, Discover Financial Services
Regulatory Environment and Cybersecurity Risk
- Loren Bushkar, Innovation Policy Expert, Federal Reserve
- Donna Murphy, Acting Deputy Comptroller for Compliance Risk Policy, Office of the Comptroller of the Currency
- Steven Keith Platt, Director of Analytics and Lecturer of Applied AI, Loyola University Chicago
- Mark Canter, Center for Enhanced Cybersecurity, US Government Accountability Office
Challenges of Deploying AI in a Heavily Regulated Environment
- Arijit Das, SVP of Digital Asset Innovation, Northern Trust
- Doug Evanoff, Director of the Kaufman Center for Financial Policy Studies, Loyola University Chicago
- Jesus Gonzalez, Deputy Global Practice Leader, Aon’s Commercial Risk Group
- Sterling Thomas, Chief Scientist, US Government Accountability Office.
Navigating Model Risk from a Non-Bank and Third-Party Perspective
- Joe Decosmo, Chief Analytics and Technology Officer, Enova International
- Doug Hague, Executive Director and Professor of Practice, School of Data Science, UNC Charlotte
- Kathleen Maley, Vice President of Analytics, Experian
- David McMichael, Vice President and Actuary, Travelers Insurance Company
The event also emphasized ethical and regulatory considerations, with speakers stressing the importance of incorporating ethical principles into AI development and deployment. Regulatory frameworks were discussed as essential tools for ensuring responsible AI use, minimizing potential harms, and maximizing benefits.
New to the conference this year
A series of breakout sessions covering a wide array of topics. These included:
- Raj Sampoornam (Chief Information Officer, Byline Bank) and Stephanie Daugherty (AVP, Business Systems Manager, Byline Bank) discussed the transformative potential of Generative AI in loan origination, highlighting successful implementations and the regulatory challenges encountered.
- Kader Sakkaria (Head of Global Data Technology, Arthur J. Gallagher & Co.) focused on strategies for managing the organizational adoption of AI, aligning AI integration with business goals, and ensuring data quality.
- Sunder Pappu (Senior VP and Head of Technology Strategy, Inland Real Estate Group) delved into methodologies for evaluating and selecting AI projects to maximize success and alignment with strategic business goals.
- Other notable sessions included Dean Haacker’s (CIO, Metropolitan Capital) exploration of workflow automation with AI co-pilots, showcasing productivity improvements through practical use cases and risk assessment methods, and Abol Jalilvand’s (Professor at Quinlan School of Business, Loyola University Chicago) discussion on AI and enterprise risk management provided insights into managing the risks posed by AI technologies, drawing on recommendations from the Artificial Intelligence Risk Management Framework (AI RMF 1.0).
Networking sessions facilitated valuable exchanges among attendees, allowing them to share experiences and insights on AI implementations. Feedback indicated a strong interest in continuing these discussions, particularly around best practices for AI adoption and the challenges posed by regulatory environments.
Key conference takeaways:
Robustness and Governance are Crucial for High-Risk AI Applications
Agus Sudjianto emphasized that performance alone is insufficient for high-risk applications. Robustness and comprehensive governance are critical for ensuring that AI models perform reliably under diverse conditions. Extensive validation processes are essential to achieving long-term reliability and safety in AI deployments, particularly in high-stakes environments where failures can have significant consequences.
Generative AI is Transforming Financial Services
The breakout session highlighted how Generative AI is revolutionizing the loan origination process. Speakers showcased successful implementations, discussing both the benefits and the regulatory challenges that come with integrating AI into financial services. Their insights demonstrated the substantial impact Generative AI can have on improving efficiency and decision-making in financial institutions.
Aligning AI Initiatives with Business Goals is Key
Effective strategies for managing AI adoption within organizations include aligning AI initiatives with overall business goals, managing organizational change effectively, and ensuring high data quality. These strategies are crucial for fostering a culture that embraces continuous learning and adaptation, enabling organizations to leverage AI technologies effectively and sustainably.
Strategic Evaluation and Selection of AI Projects
Methodologies for evaluating and selecting AI projects are essential for ensuring they align with strategic business objectives. Emphasis on prioritizing AI initiatives that deliver the most value and outlining critical criteria for assessing the potential success of AI projects helps organizations make informed decisions about which AI projects to pursue, maximizing their impact and return on investment.
Workflow Automation Drives Productivity
Explorations of the productivity gains achievable through AI co-pilots and workflow automation revealed significant potential. Practical use cases demonstrated how AI-driven automation can streamline processes and enhance efficiency. Methods for assessing risks associated with automation and examples of successful implementations highlighted the substantial benefits of adopting AI co-pilots.
Robust Risk Management is Essential for AI Deployments
The importance of robust risk management practices in mitigating the negative impacts of AI technologies was emphasized. Recommendations from the Artificial Intelligence Risk Management Framework (AI RMF 1.0) were highlighted, stressing the need for organizations to adopt comprehensive risk management strategies to safeguard against potential risks associated with AI deployments.
Ethical and Regulatory Considerations are Paramount
The critical role of ethical considerations and regulatory frameworks in ensuring responsible AI deployment was underscored. Incorporating ethical principles into AI development and adhering to regulatory standards minimizes potential harms and maximizes the benefits of AI technologies. A balanced approach is necessary to foster innovation while protecting stakeholders.
Collaboration and Knowledge Sharing Enhance AI Adoption
Networking sessions provided valuable opportunities for attendees to share experiences and discuss best practices for AI adoption. The feedback indicated a strong interest in continuing these conversations, particularly around navigating regulatory environments and ensuring successful AI implementations. Collaboration and knowledge-sharing are essential in advancing AI technologies and their applications.
Overall, the 2024 AI Business Consortium’s AI in the Financial Services Industry Conference effectively highlighted the latest advancements in AI and provided deep insights into managing the associated risks and regulatory challenges. The discussions underscored the importance of robust governance, ethical considerations, and strategic alignment in the successful deployment of AI, ensuring that its benefits are realized across various industries.
About the authors
Guillaume Bolivard
Guillaume Bolivard, an MBA/MS in Finance candidate at Loyola University Chicago's Quinlan School of Business, combines extensive financial expertise with cutting-edge AI research. His career extends across roles in multinational corporations, investment banking and central banking, with a focus on financial modeling, risk management, and equity derivatives. As a Graduate Researcher, Bolivard plays a key role in an AI project applying natural language processing to job postings, validating model accuracy for improved workforce development. His prior experience in Paris's financial sector, coupled with his athletic background as a national-level swimmer, demonstrates his resilience and discipline. Multilingual and possessing strong leadership skills, Bolivard is positioned at the forefront of finance and technology integration.
Jean-Marc Charles
Jean-Marc Charles is an accomplished central banking professional with over a decade of expertise in payment systems. Currently working in the fiduciary department of the Central Bank of Haiti as a Data Analyst, Jean-Marc specializes in analyzing and optimizing payment systems to enhance financial efficiency. He holds an Executive MBA and a master’s degree in digital business Transformation, and he recently graduated with a Master’s in Business Data Analytics from Loyola University. A trilingual leader with a passion for excellence and a strong sense of responsibility, Jean-Marc is adept at leading cross-functional teams to achieve corporate objectives. His analytical and quantitative skills, combined with his deep understanding of digital business and data analytics, make him a pivotal figure in the ongoing transformation of Haiti’s financial sector.
By Guillaume Bolivard and Jean-Marc Charles, Quinlan School of Business, Loyola University Chicago
The second annual AI in the Financial Services Industry conference, organized by the AI Business Consortium, was held on June 27, 2024 at Loyola University Chicago. The event focused on the forefront of artificial intelligence innovations, applications, and execution in the financial services industry. This conference brought together a plethora of industry leaders, AI practitioners, and academic experts who shared their insights on the current advancements, challenges, and future directions of AI technologies.
The keynote address by Agus Sudjianto (former Executive Vice President, Wells Fargo and Head of Corporate Model Risk), titled “Performance is not all you Need: Developing and Validating High-Risk Machine Learning,” set the tone for the event. Sudjianto emphasized the necessity of robustness and governance in AI model development, highlighting that while performance metrics are vital, the models’ ability to maintain reliability under diverse conditions is equally crucial. He advocated for comprehensive model validation processes to ensure long-term reliability and safety, particularly in high-stakes applications.
Panel discussions further enriched the event, addressing critical issues such as risk model management, the regulatory environment and cybersecurity risks, and the strategic deployment of AI in financial services. The panels featured insights from leading experts who shared their experiences and best practices for navigating the complex AI landscape.
Panels and speakers included:
Gen AI for Process Improvement and Related Use Cases
- Gary Class, Industry Strategist for Financial Services, Teradata
- Donald High, Chief Data Scientist, IRS
- Ram Peddu, Chief Data and Analytics Officer for Risk and Finance, BMO Harris Bank
Blueprint for Success: Strategic AI Deployment in Financial Services
- Brent Demar, VP of Decisioning Technology, Discover Financial Services
- Arjun Ravi Kannan, Director of Data Science Research, Discover Financial Services
- Raghu Kulkarni, Chief AI Officer, Equifax
- Yixiu Li, Director of Generative AI Modeling, Discover Financial Services
Regulatory Environment and Cybersecurity Risk
- Loren Bushkar, Innovation Policy Expert, Federal Reserve
- Donna Murphy, Acting Deputy Comptroller for Compliance Risk Policy, Office of the Comptroller of the Currency
- Steven Keith Platt, Director of Analytics and Lecturer of Applied AI, Loyola University Chicago
- Mark Canter, Center for Enhanced Cybersecurity, US Government Accountability Office
Challenges of Deploying AI in a Heavily Regulated Environment
- Arijit Das, SVP of Digital Asset Innovation, Northern Trust
- Doug Evanoff, Director of the Kaufman Center for Financial Policy Studies, Loyola University Chicago
- Jesus Gonzalez, Deputy Global Practice Leader, Aon’s Commercial Risk Group
- Sterling Thomas, Chief Scientist, US Government Accountability Office.
Navigating Model Risk from a Non-Bank and Third-Party Perspective
- Joe Decosmo, Chief Analytics and Technology Officer, Enova International
- Doug Hague, Executive Director and Professor of Practice, School of Data Science, UNC Charlotte
- Kathleen Maley, Vice President of Analytics, Experian
- David McMichael, Vice President and Actuary, Travelers Insurance Company
The event also emphasized ethical and regulatory considerations, with speakers stressing the importance of incorporating ethical principles into AI development and deployment. Regulatory frameworks were discussed as essential tools for ensuring responsible AI use, minimizing potential harms, and maximizing benefits.
New to the conference this year
A series of breakout sessions covering a wide array of topics. These included:
- Raj Sampoornam (Chief Information Officer, Byline Bank) and Stephanie Daugherty (AVP, Business Systems Manager, Byline Bank) discussed the transformative potential of Generative AI in loan origination, highlighting successful implementations and the regulatory challenges encountered.
- Kader Sakkaria (Head of Global Data Technology, Arthur J. Gallagher & Co.) focused on strategies for managing the organizational adoption of AI, aligning AI integration with business goals, and ensuring data quality.
- Sunder Pappu (Senior VP and Head of Technology Strategy, Inland Real Estate Group) delved into methodologies for evaluating and selecting AI projects to maximize success and alignment with strategic business goals.
- Other notable sessions included Dean Haacker’s (CIO, Metropolitan Capital) exploration of workflow automation with AI co-pilots, showcasing productivity improvements through practical use cases and risk assessment methods, and Abol Jalilvand’s (Professor at Quinlan School of Business, Loyola University Chicago) discussion on AI and enterprise risk management provided insights into managing the risks posed by AI technologies, drawing on recommendations from the Artificial Intelligence Risk Management Framework (AI RMF 1.0).
Networking sessions facilitated valuable exchanges among attendees, allowing them to share experiences and insights on AI implementations. Feedback indicated a strong interest in continuing these discussions, particularly around best practices for AI adoption and the challenges posed by regulatory environments.
Key conference takeaways:
Robustness and Governance are Crucial for High-Risk AI Applications
Agus Sudjianto emphasized that performance alone is insufficient for high-risk applications. Robustness and comprehensive governance are critical for ensuring that AI models perform reliably under diverse conditions. Extensive validation processes are essential to achieving long-term reliability and safety in AI deployments, particularly in high-stakes environments where failures can have significant consequences.
Generative AI is Transforming Financial Services
The breakout session highlighted how Generative AI is revolutionizing the loan origination process. Speakers showcased successful implementations, discussing both the benefits and the regulatory challenges that come with integrating AI into financial services. Their insights demonstrated the substantial impact Generative AI can have on improving efficiency and decision-making in financial institutions.
Aligning AI Initiatives with Business Goals is Key
Effective strategies for managing AI adoption within organizations include aligning AI initiatives with overall business goals, managing organizational change effectively, and ensuring high data quality. These strategies are crucial for fostering a culture that embraces continuous learning and adaptation, enabling organizations to leverage AI technologies effectively and sustainably.
Strategic Evaluation and Selection of AI Projects
Methodologies for evaluating and selecting AI projects are essential for ensuring they align with strategic business objectives. Emphasis on prioritizing AI initiatives that deliver the most value and outlining critical criteria for assessing the potential success of AI projects helps organizations make informed decisions about which AI projects to pursue, maximizing their impact and return on investment.
Workflow Automation Drives Productivity
Explorations of the productivity gains achievable through AI co-pilots and workflow automation revealed significant potential. Practical use cases demonstrated how AI-driven automation can streamline processes and enhance efficiency. Methods for assessing risks associated with automation and examples of successful implementations highlighted the substantial benefits of adopting AI co-pilots.
Robust Risk Management is Essential for AI Deployments
The importance of robust risk management practices in mitigating the negative impacts of AI technologies was emphasized. Recommendations from the Artificial Intelligence Risk Management Framework (AI RMF 1.0) were highlighted, stressing the need for organizations to adopt comprehensive risk management strategies to safeguard against potential risks associated with AI deployments.
Ethical and Regulatory Considerations are Paramount
The critical role of ethical considerations and regulatory frameworks in ensuring responsible AI deployment was underscored. Incorporating ethical principles into AI development and adhering to regulatory standards minimizes potential harms and maximizes the benefits of AI technologies. A balanced approach is necessary to foster innovation while protecting stakeholders.
Collaboration and Knowledge Sharing Enhance AI Adoption
Networking sessions provided valuable opportunities for attendees to share experiences and discuss best practices for AI adoption. The feedback indicated a strong interest in continuing these conversations, particularly around navigating regulatory environments and ensuring successful AI implementations. Collaboration and knowledge-sharing are essential in advancing AI technologies and their applications.
Overall, the 2024 AI Business Consortium’s AI in the Financial Services Industry Conference effectively highlighted the latest advancements in AI and provided deep insights into managing the associated risks and regulatory challenges. The discussions underscored the importance of robust governance, ethical considerations, and strategic alignment in the successful deployment of AI, ensuring that its benefits are realized across various industries.