The Cloud Native Computing Foundation (CNCF) will be presenting ten talks during the Open Source in Finance Forum in New York on September 30 and October 1. The presentations will drill down on the intersection of cloud native and finance, and will be ideal for enterprise audiences.
Here’s a quick overview of what to expect from the CNCF track at the conference.
The Linux Foundation's Open Source Security Foundation (OpenSSF) Secure Software Development Education 2024 Survey offers crucial insights that are particularly relevant to the financial services industry, including FINOS members such as sell-side banks, buy-side firms, and wealth managers. As these organizations increasingly rely on software to drive operations, the emphasis on secure software development becomes critical.
The report highlights areas where organizations from all industries, including financial institutions, can enhance their security practices to protect sensitive data, manage risk, prevent substantial financial losses, and maintain client trust.
At our Open Source in Finance Forum in London, industry leaders gathered to explore the future of cloud-native incident response in the first FINOS cloud native incident response tabletop exercise (TTX). Watch this recording for an overview of the findings from Francesco Beltramini and Ash Ward from ControlPlane and Laura Penhallow from Qudrature.
Are you a security professional at an FSI organisation? Get involved in our next tabletop exercise at OSFF NYC 2024.
At FINOS, we believe that the right schedule can make or break an event. For the Open Source in Finance Forum (OSFF) New York, we've meticulously crafted a program that not only aligns with industry needs but also maximizes the value for our attendees. Here's a look at why and how we designed the schedule and how you can make the most of it.
I was delighted to host the FINOS’ DEI Special Interest Group (SIG) session at the recent Open Source in Finance Forum (OSFF) London, addressing two seemingly unrelated yet highly connected issues of the day - the business case for diverse teams and how to deploy Responsible AI.
Our DEI group championed by FINOS members such as BMO, Citi, Morgan Stanley and NatWest has an ambitious program of activities that's championing best practices for embedding diversity and inclusivity across financial services institutions.
In the engaging session at the FINOS Open Source in Finance Forum 2024, panelists explored the transformative impact of AI on financial services. Cara Delia introduced the discussion, emphasizing AI readiness as a key strategic initiative for FINOS. Emily Prince highlighted the challenge of making data more accessible for end users, while Ian Micallef discussed overcoming legacy system hurdles and regulatory compliance.
Collin Eberhardt noted the need for new frameworks to handle AI's non-deterministic nature and ethical concerns. The panel concluded with insights into the future of AI, stressing the importance of balancing innovation with robust regulatory practices.
To unlock the full potential of open-source software, as it is with any modern-day software, it must have an integrated ecosystem of complementary solutions that add value.
That is the aim of the FINOS Tech Sprint 2024, which kicked off on July 16th with a gathering of existing FINOS projects and new teams creating new value (code, tests, documentations, GH issues, etc.) using existing solutions in the FINOS ecosystem and beyond.
In the financial services landscape, where payments, transfers, trades, and numerous transactions take place every day, data sits at the financial system’s core and enables its operation. And where there is data, there is potential room to leverage models that learn from it and can help to optimize the operation. Such models within the machine learning realm have become critical tools for tackling tasks (to name a few) such as fraud detection, customer segmentation, sentiment analysis, and risk assessment. Creating these models has become easier in recent years. However, the same can’t be said about deploying and maintaining these models in production environments. This is where MLOps (Machine Learning Operations) comes into play, offering a set of practices and, hopefully, standards that combine machine learning, DevSecOps, and data engineering to develop, deploy, and maintain ML models more reliably and efficiently.
