Finance is one of the oldest industries in the world, but despite recent innovation, certain areas of finance still rely on archaic processes. Fixed income is a prime example — the debt capital markets behave like it’s still the 1980s, trading is manual, news takes 30 minutes to hit the market, and the data is terrible.
9fin is on a mission to fix that. Join us for an evening of lightning talks, networking, and refreshments, to learn about how tech is reshaping the finance industry and helping professionals work smarter and win more business.
Lightning talk 1: Shaking off the powersuit — how tech helps high yield lawyers work more efficiently.
Jainisha Amin, Head of European Legal @ 9fin and Jori Geysen, Senior Machine Learning Engineer @ 9fin, Olivia Mantock, Senior Legal Consultant @ 9fin, and Elliot @9fin.
In this session, you’ll learn all about how modern tech can improve archaic processes. Our engineering and legal speakers will dive into the work high yield lawyers do in the 9fin platform, demonstrating how Large Language Model (LLM) integrations can help lawyers speed up their legal debt document analysis and free up their time for the work that really matters.
Lightning talk 2: Fraud Prevention in Online B2B Transactions
Dr Marina Theodosiou, Chief Data Scientist @ Two.
In this session, we’ll explore how Two.inc has successfully developed a state-of-the-art fraud prevention engine. By skillfully aggregating specialized ML models in a hierarchical manner, they’ve achieved unparalleled precision. Other elements to the strategy encompasses a robust data-centric infrastructure and an in-house MLOps Ecosystem. This ecosystem acts as an exoskeleton for Data Scientists, enhancing collaboration, enabling swift deployments, and ensuring effective monitoring and updates for ML solutions.
Join us to learn how Two.inc’s holistic model and strategy tackles the multifaceted challenge of fraud prevention!
Lightning talk 3: How we scale our analytical capabilities with increasing demand from multiple clients
Avision Ho, Lead Data Scientist @NatWest Boxed
At NatWest Boxed, we provide Banking-as-a-Service (BaaS) to consumer brands and fintechs so they can offer financial products such as card accounts and flexible lending directly to their customers. The more businesses work with us, the more demand there is to understand the data we collect and process on their customers. However, how can a team of 10 data scientists and analysts scale flexibly and rapidly to meet this growing demand for data insights by more and more businesses working with NatWest Boxed?
In this talk, I will cover how we leverage Large Language Models (LLMs) to create a text-to-sql product that democratises access to our data so that we can provide quicker and easier insights. This way, we can flexibly scale to meet the growing demand for data insights, and do so in a timely manner.
In particular, I will discuss why we chose open-source models like Llama3, the merits of frameworks such as LangChain and Vanna, how we ensured we were privacy and risk-compliant, and the infrastructure to build and deploy this.