The AI in Finance Summit, hosted by Re-Work April 20-21, 2023, in New York City, brought together industry leaders, experts, and enthusiasts to discuss the latest advancements in artificial intelligence (AI) and their applications within the financial sector. In this blog post, we will explore key takeaways from the event, including success factors, cognitive courtesy, AI team building, the power of AI, data-centric AI, implementation, and scaling, leveraging large language models (LLMs), generalist AI systems, Bayesian optimization, and AI strategy priorities.

The Power of AI in Financial Services

AI is transforming the financial industry by providing a range of solutions, from predictive analytics and anomaly detection to document understanding and natural language processing. With AI, financial firms can now match over 200 million trades with a 95% accuracy rate, according to Diana Meditz, Director of Advanced Digital Solutions AI/ML from BNY MELLON. AI’s potential to revolutionize the industry becomes evident in design thinking sessions, where participants ideate innovative solutions that were previously thought impossible.

Success Factors in AI

According to Bjorn Austraat, Senior Vice President, Head of AI Acceleration at TRUIST, “success in AI projects is 5% good model and 95% everything else.” It is essential to move fast and slow simultaneously, especially in highly regulated industries like finance. While it takes around six months to build a model, deployment could take another six months.

Cognitive Courtesy

Effective communication is crucial when designing AI systems. Simplicity is key, and metaphor plays a significant role in creating an intuitive user experience. Mr. Austraat emphasized that “pre-translating content for different audiences is necessary, and soon, regulation may require explaining AI usage in LLMs.”

The Rise of Data-Centric AI

Henry Ehrenberg, Co-founder of Snorkel, a company providing tools to enable data-centric AI, described data-centric AI as “using a foundation model that includes text, video, and images to provide a more comprehensive understanding of a given problem.” While ChatGPT has its limitations, it offers a starting point that can be fine-tuned and adapted for specific use cases in financial services. Examples include the efficient creation of training data to improve model accuracy and the enhanced collaboration between subject matter experts and data scientists. AI does not work out of the box for most enterprise applications, and achieving high-quality results requires expertise and adaptation.

Implementing AI Across an Organization

The successful implementation of AI in an organization requires more than just technological advancements; it also requires a focus on people and processes. Companies must be willing to adapt, change, or even eliminate obsolete roles and departments as AI becomes more prevalent. Olga Tsubiks, Director of Strategic Analytics and Data Science at RBC said that “innovation centers can serve as hubs for ideation and experimentation, but they must be supported by proactive and decisive leadership.”

Leveraging LLMs (Large Language Models) for Value Creation

ChatGPT and other LLMs have shown promise in improving enterprise productivity, but their practical applications are still being explored. Plausible use cases include optimizing internal knowledge management, enhancing research capabilities, and automating call centers. However, “challenges remain in terms of data access, sharing personal identifiable information (PII), and balancing the “buzz factor” with tangible value creation,” according to Claire Gubian, Global Vice President of Business Transformation at DATAIKU, a ten-year-old AI and machine learning company.

AI as a Business Strategy

As AI continues to evolve, it is becoming more integrated into the core business strategy of organizations. This requires clear communication, collaboration between data scientists and business stakeholders, and focusing on generating ROI. This shift has been exemplified by companies like Fidelity, where “instead of AI guys trying to shop ideas around, AI is becoming part of the business culture,” in the words of Upal Sen, VP, AI Product Owner at Fidelity Investments. In order to ensure success, both data scientists and business leaders must communicate effectively and focus on measuring the value AI brings to the organization.

Conclusion:

The AI in Finance Summit provided valuable insights into the current and future state of AI in the financial industry. As AI continues to evolve and become more integrated into the core business strategies of organizations, it is essential for industry leaders to strike the right balance between innovation and regulation, and to focus on the value AI brings to their organizations. By adopting a strategic approach, AI has the potential to revolutionize the financial industry, leading to increased efficiency, better decision-making, and improved customer experiences. It is important for organizations to remain proactive in adopting emerging technologies, providing training to their workforce, and upholding strong ethical values to ensure long-term success as AI continues to evolve.

Author: Deborah Baxley, Director, Payments Consulting Network

Deborah Baxley is an international mobile/cards payment consultant, a recognized expert in the industry, and a creator of growth strategies for new and existing markets with more than 30 years of experience consulting cards and payment companies. Through her work in fifteen countries, she has advised issuers, acquirers, networks, and processors on product direction and competitive positioning, delivering millions of dollars in new revenue or operating cost savings. As a steering committee member of the US Payments Forum, founding and Board member of the Faster Payments Council, co-founder of NYPAY, and through frequent keynote speaking and writing, she is recognized for insights on the future of retail and commercial payments. She is a featured expert on NPR Marketplace and was recently quoted in the NY Times on the topic of faster payments fraud.

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