Open Source in Finance Forum New York 2024

In this thought-provoking presentation, we explore the profound impact of artificial intelligence (AI) on software development within the financial services industry. Drawing parallels with historical technological revolutions that reshaped economies, such as the Roman water mills, the steam engine, and the early 2000s push for automation, we demonstrate how AI is poised to be the ultimate general-purpose technology of our time. We delve into the economic and technological implications of AI adoption, highlighting how it drives increased productivity, cost reductions, and an unprecedented expansion of demand for innovative financial products and services. The presentation underscores the emerging productivity polarity, where those who embrace AI will gain a significant competitive advantage over those who resist its integration. Recognizing the pivotal role of open source in driving innovation within the finance sector, we emphasize the urgency for technologists to seamlessly incorporate AI into their daily workflows. By doing so, developers can position themselves as vanguards, fostering collaboration and unlocking new frontiers in open finance software development.

When leveraging AI in financial services, there are new technologies and security challenges to tackle in addition to the usual DevOps and scaling considerations. As now exists several sources of information for AI security best practices, we are not left on our own. But practically applying them in your project is not so well described. In this talk, Rowan Baker and Vicente Herrera will explain how to start with a security guideline recommendation like the “Simple Governance Framework” from the FINOS AI Readiness Group and apply it to a real project They will show how to transform the recommendations into real actions and how specific controls or mitigations have been chosen and implemented in a practical way. Some approaches may be conventional but important, like those regarding supply chain security, others will be more AI specific and novel. They will also show how the guidelines may not cover specific implementation, how to extend the coverage and contribute back so others benefit from the knowledge you have gained.

Test automation has been around for a while now, but teams are still having trouble adopting it--particularly in finance and finserv applications. Historically, there are three exceedingly complex questions when testing these applications, which are made far more complex when migrating to automated tests: data generation, complex workflow coverage, and regulatory compliance. In this talk, Marcus will showcase the myriad ways Generative AI can help solve these three complexities, and will introduce several other risk factors you have to account for when adopting LLMs. Everyone is trying to reconcile where and how AI fits into the new world of software development. Come join this talk to explore both the opportunities and downsides, and how to stay above the hype-cycle.

Generative AI has plenty of use cases in Finance. From the customer support on the front office, to research, business analysis and optimization of investment cases. However most companies get stuck at the "Proof of concept" stage. While it is relatively quick to create a demo with a "Wow effect", building the right pipelines, governance, security and controls and in general get the buy in and approvals from legal and compliance teams is extremely hard. In this presentation we will show a detailed runbook on how to leverage Generative AI cases at scale in the organization, from the perspective of the first company that delivered a production application in the market. We will review how to build an open innovation system that promotes collaboration in what we called "The era of the 14.000 innovators" where every employee became a prompt engineer. And how we harnessed that power to put dozens of use cases in production (internal and external) as well as the edge research on agents and beyond.

we delve into the critical intersection RAG systems and robust data governance practices. As organizations increasingly leverage RAG to enhance their AI capabilities, the need for strong data management becomes paramount. Our discussion will cover: Data Discovery and Routing: Learn how to leverage enterprise data catalogs to enhance RAG's query processing precision and efficiency. Ownership and Explainability: Explore techniques for incorporating data ownership information and rich metadata to improve transparency and decision-making. Data Security and Access Control: Understand the implementation of Role-Based and Policy-Based Access Controls, including innovative approaches using GenAI for dynamic permission assessment. Data Quality and Bias Mitigation: Discover strategies for enriching RAG systems with data quality metrics and methods for detecting and mitigating biased data. Knowledge Graphs and Master Data Management: Uncover how these tools can dramatically improve the accuracy of LLM-based responses Data Lineage and Root Cause Analysis: integrate data lineage capabilities for enhanced explainability and efficient problem-solving. Advanced Prompting Techniques: