Open Source in Finance Forum London 2026

Provisioning compute capacity in the cloud should be as straightforward as defining what you need, not where and how to get it. Yet HPC engineers face inconsistent, provider-specific APIs, incompatible provisioning models, and duplicated integration logic across schedulers and clouds. Open Resource Broker (ORB) solves this with a unified, open-source API and abstraction layer for provisioning cloud capacity anywhere.

ORB integrates seamlessly with IBM Spectrum Symphony HostFactory to standardize capacity requests, tracking, and release. Its modular design supports additional schedulers and multiple cloud providers beyond the initial AWS implementation. ORB supports CLI, REST, SDK and MCP APIs for consistent scaling across diverse HPC environments.

We'll demonstrate ORB's architecture, end-to-end provisioning, and resiliency patterns including retries, idempotent operations, and failure recovery. We'll also cover extensibility for new schedulers and cloud APIs.

Built for large-scale financial HPC grids, ORB is now donated to FINOS under Apache 2.0 for open governance. Attendees will learn proven strategies to make HPC capacity management more portable, automated, and sustainable.

After adopting spec base development, we were finally able to solve a three year-old challenge and now have 14 different variations of our TraderX repo for developers to interact with an integrate with. This will allow for comprehensive learning in multiple phases of the life-cycle of this project and rapid innovation, by introducing more nodes to our learning graph. This makes the perfect hackathon project, at the perfect time, at the crossroads of major FINOS portfolio growth and Agentic developer tools. Come learn how you can get set up in minutes and how this can help you on your FINOS project journey.

A Tier-1 bank was running more than 80 applications on a document-oriented database estate spanning 400+ instances and 35TB of data. Changes in licensing terms introduced cost pressure and long-term architectural constraints.
This session details the technical and governance approach taken to migrate those workloads without application rewrites. We will cover estate discovery, compatibility validation, migration sequencing, backup redesign with point-in-time recovery, and monitoring consolidation across hundreds of nodes.
We will share practical lessons for tech teams managing large-scale database platforms in financial services.

Financial institutions are using the FINOS 'physrisk' project to bring calculations of physical climate risk in-house. Although excellent vendor solutions exist for managing physical risks, many Banks are concerned by the 'black box' issue: is it really possible to manage model risk without being truly hands-on? Moreover, it is an attractive option to base a system on public domain data, layering on top only the commercial data that brings real value. The goal is not to be paying for public-domain data.
We give a demonstration of how financial institutions can internalise the systems, and how public domain data can be mixed with commercial for capabilities on a par with the best vendor solutions – but fully in the hands of the modelling teams.

In the world of information, financial data plays a major role in decision-making for investors as well as companies. This data spans a wide array of sectors and financial instruments, including stock data, income statements, P&L statements, and so on.

The complexity arises when this information comes in many different formats, from numbers and figures to news and images. This raises the question of how we can effectively consolidate this diverse set of information into a single visual platform capable of providing a holistic and vendor-agnostic solution to consumers.

In this talk, we present a single unified market analytics dashboard that can be leveraged by consumers and investors for data-driven decision-making. This will be based on open-source technologies and will provide an alternative to the costly solutions present in the industry while also avoiding vendor lock-in.

After this session, the audience will have a better understanding of the possibilities and advantages that open-source tools provide in the financial sector and how these technologies can be used to create a scalable and robust financial research platform.

Data quality failures in financial services carry real regulatory and business risk. Traditional rule-based validation catches known issues but misses subtle patterns that emerge over time in production systems.
I built an open source real-time data quality monitoring system processing 332,000+ orders using Apache Kafka, Python, and a 3-model ML ensemble — Isolation Forest, LSTM, and Autoencoder — with SHAP explainability making every alert actionable. The system runs continuously with sub-10ms latency and automatic drift detection.
This talk covers practical lessons for financial services technologists: how to layer ML anomaly detection on top of rules-based validation, how to maintain model accuracy over time, and how open source tooling can replace expensive enterprise data quality products — saving organisations £100K+ annually.