AI in Finance Summit New York schedule

In an era where financial institutions navigate a labyrinth of global compliance and risk management challenges, the need for innovative solutions has never been more acute. Brennan Lodge introduces a groundbreaking approach at the intersection of AI and governance, risk, and compliance (GRC) with the unveiling of Retrieval Augmented Generation (RAG) technology and its application. This advanced tool exemplifies the fusion of AI's potential with the intricacies of GRC, promising to revolutionize how financial services manage compliance, understand data privacy laws across jurisdictions, and bridge the gap between current practices and regulatory expectations. By leveraging the vast capabilities of RAG which offers a beacon of hope for institutions wrestling with the complexities of regulations like GDPR, CCPA, and the myriad of emerging data protection standards globally.

Innovative AI for Enhanced GRC: Introduction to the role of advanced AI technologies, including Retrieval Augmented Generation (RAG), in revolutionizing GRC processes within the financial services sector.

RAG: Bridging the Compliance Gap: Exploring how RAG technology enables financial institutions to efficiently conduct gap analyses by comparing existing policies and practices against a vast array of global regulations and standards, including GDPR, CCPA, and emerging frameworks.

Practical Demonstrations: Showcasing real-world applications of RAG in GRC scenarios, including live demos on conducting gap analyses and interpreting complex regulatory texts, highlighting the tool’s agility in adapting to the specific compliance needs of financial institutions.

Future Directions in AI-Powered GRC: Envisioning the future of GRC in the financial services industry, with an emphasis on the ongoing development of AI technologies like RAG to provide more sophisticated, efficient, and comprehensive compliance and risk management solutions.

• AI is not new. What has AI been doing for us in Finance?
• What are the key factors of building AI Capabilities?
• As Generative AI advancing quickly, how can we cope with this technology evolution?

Developing scalable and sustainable AI capabilities is no longer a nice-to-have advantage, but a must-to-have competency for companies to survive and thrive in the fast-evolving economy and meet ever-changing consumer expectations.  Building AI capabilities successfully, from the ground up requires not only a deep understanding of the math and technologies, but also developing broad alignment with business strategy, stakeholders, and processes.  In this session, Nan Li will share how to approach AI use cases and develop AI capabilities holistically and strategically.  You will also learn about the common pitfalls to avoid.

- How can AI be leveraged to improve business strategy and production?
- What challenges may need to be considered and how can you reduce the impacts of such?
- What are the building blocks of a successful AI adoption to ensure that the strategy is holistic and achievable?

While generative AI was the technology story of 2023, we expect 2024 will be the year when organizations shift from the art of the possible to real, tangible AI implementation. But only those who can navigate the complexities of this transformative technology will see major competitive advantage and value creation. During this session, attendees will learn:

• What are the data and AI trends that are top of mind for financial services executives in 2024
• How organizations navigate through the complexities of gen AI and ensure that they do not fall behind
• What are the critical challenges faced by technology and business leaders in the wake of concerns around data security, governance and risk

We've all witnessed the capability of Large Language Models (LLMs) to write code. The majority of the teams have embraced LLM-powered tools like GitHub Copilot. Yet, they've only experienced marginal productivity improvements! This is primarily due to being constrained by the number of "pilots" available. Stride has innovated a groundbreaking method to transcend these limitations. We've developed asynchronous teams of LLM-powered agents that integrate seamlessly with your software development environment, adhering to your preferred practices and patterns. Tasks that could take a developer several hours to code, test, and deploy can now be ready in mere minutes.  Join Stride for an exciting panel discussion where we will unveil case studies, share insights, and discuss best practices for implementing this revolutionary approach in your codebases and workflows.

The stakes are especially high for companies working in fintech, insurance and financial services. Firms thoughtlessly rushing to deploy AI face a minefield of new risks including unintended biases, improper use of data and other issues that can lead to major regulatory consequences and impact individuals, families and society as a whole.

• No data, no AI: why innovation will only come from imperfect data in real-world settings
• Keys to making Generative AI work for finance and insurance
• Insights from my experience at Barclays, Yahoo! Microsoft Research, The Institute for Experiential AI and more.
• What we’ve learned from partnering with finance and insurance companies
• How a Responsible AI strategy drives business value

This session provides an overview of how Retrieval-Augmented Generation (RAG) can enhance financial decision-making by utilizing unstructured data. Attendees will explore the technology's fundamentals, its application in financial services, and its role in improving accuracy and speed in operations. 
Anji Ismail is the CEO of Finnt, an AI-native financial analysis and research platform. Finnt specialized in extracting crucial data from large, unstructured, and complex documents—including OMs, PPMs, 10-Q/10-K, as well as prospectuses, reports, leases, loans etc. It then integrates these findings into templated memos, enriched with the company's existing data and real-time market insights.

The democratization of artificial intelligence and shift to AI-centric transformation is disrupting ways of working. Avanade has partnered with Microsoft on AI for almost a decade, and as we have moved towards an AI-first operating model ourselves, we’re focused on empowering our people with generative AI tools to help them be their best.

In this session, Jonida will share Avanade's journey with Copilot for Microsoft 365 - an integrated, extensible set of tools that combines the capabilities of generative AI with familiar Microsoft 365 apps - as part of the the Early Access Program (EAP). She'll focus on our own transformative experiences with Copilot, how we overcame challenges, to accelerate value for our organization and how this has fuelled how we advise our Financial Services clients to get the most out of Copilot.

•    Your organization has good data, is privacy and cyber savvy and can prioritize use cases: what’s your AI play?
•    How to lead and manage change in AI acceleration, including UX
•    Leadership jitters – how fast can we go, and should we go? What happens to those who don’t?

- Discover the importance of aligning your AI strategy with your overall business goals and how to identify which areas of your business could benefit most from AI implementation.
- What is the range of AI technologies available today and how to evaluate which ones are best suited for your specific use case
- Discuss the need for flexibility and adaptability in your AI strategy, including how to adjust your approach in response to changing market conditions, emerging technologies, and evolving customer needs.

- Can blockchain platforms offer an alternative to platform governance?
- How could such platforms impact decision making processes?
- How would this then impact the regulation of such decisions?

In this session, Zoiner Tejada will raise the alarm on compliance issues enterprises may not be considering in their excitement to unlock the potential of AI in their organization and what to do about it:

• Empower and govern growth from one AI agent to one AI agent per employee
• Ensure that an AI agent acting on behalf of an employee doesn't get more access to data than the employee would have directly
• Establish a perimeter of what sensitive information (e.g., trade secrets, PII, etc.) gets sent to the underlying AI model, and what employees are allowed to see in AI model responses

• Customer Service and Personalization
• Fraud Detection and Security
• Enhanced Decision Making
• GenAI Models at Hedge Funds
• Challenges in Implementing AI in Finance

- Overview of the current landscape of card and payments fraud, including common types of fraud and their impact on businesses and consumers
- Discussion of the potential of machine learning and other AI-based techniques for detecting and preventing fraud
- Examination of the challenges and limitations of using machine learning for fraud detection and ways to overcome them
- Explore Use-Cases of how banks and financial services are combatting card and payment fraud

• Explore the current opportunities and challenges in capturing ROI when developing and deploying AI
• Identify areas of improvement in AI Pipelines
• Optimize engineering, infrastructure, culturea teams and decision-making for higher ROI

-What are the primary legal implications you should consider when designing an AI application?

-How do your design choices impact how quickly you can sign up new customers for your AI-related service?

-What changes may be on the horizon for the current legal landscape surrounding AI over the next year?

In the rapidly evolving landscape of Capital Markets, leveraging advanced data analytics and AI has become a cornerstone for front office innovation. This session aims to illuminate the path for investment professionals, portfolio managers, quants, and research analysts to harness the full potential of their data assets. With a focus on practical, high-impact use cases, we delve into how Generative AI is revolutionizing front office operations, from alpha generation to trade execution and risk management. Discover how best of breed technologies empower Hedge Funds, Asset Managers, and Banks to not only navigate the complexities of today's financial markets but to thrive by making more informed, data-driven decisions.