A Platform for the Integration, Development and Operations of Machine Learning Systems (IDOML)

The financial industry is leveraging Artificial Intelligence (AI), particularly Machine Learning (ML), to drive digital transformation by improving processes, cutting operational costs, and delivering personalised services. Established financial institutions and FinTech companies use ML to handle vast amounts of data for innovative solutions. However, many businesses face significant challenges when adopting ML technologies, such as limited budgets, lack of expertise, and technology risks. As a result, only a small portion of the finance industry has fully embraced ML. Some financial incumbents, like BGL/BNP Paribas, have overcome these barriers by setting up in-house data science labs and reaping operational benefits through automation. These companies make ideal research partners for the National Centre of Excellence in Research in Financial Technologies (NCER-FinTech) as they seek to address advanced innovation challenges through collaboration with research institutes. The overarching goal of this project is to facilitate the wider adoption of ML technologies across the financial sector. The project aims to create a “data science lab” startup package that simplifies the process and lowers the investment costs for finance companies to build their own labs. The cornerstone of this package is IDOML, an MLOps software platform designed to support the entire lifecycle of ML systems, focusing on simplifying the deployment process. Compared to the existing cloud-based MLOps platform, IDOML will adopt an open “Minimum Viable Product” approach to remain cost-effective and easy to reuse. The platform will be a practical, affordable, and research-friendly solution, ultimately expanding the reach of ML technology across the financial sector.