Finance has a reputation for being difficult, and for good reason. Even apparently simple concepts become complicated once we look closely at the real financial world. A bond involves an issuer, a security, identifiers, contractual obligations, payments, prices and lifecycle events. A derivative can bring in multiple parties, economic terms, rights, obligations, amendments, novations and … Continue reading Financial Ontology Modelling for Everyone (5): Finance Is Hard. Ontology Should Not Be.
Tag: Financial Semantics
Financial Ontology Modelling for Everyone (4): The End of the Technology-Only Financial Technologist
For a long time, financial technology supported a fairly clear division of labour. Business people understood the financial problem, analysts translated it into requirements, architects designed the system, and developers implemented it. That model was never as clean as the organisation chart suggested, but it worked well enough. A developer could spend years building trading … Continue reading Financial Ontology Modelling for Everyone (4): The End of the Technology-Only Financial Technologist
Financial Ontology Modelling for Everyone (3): AI Makes Shared Meaning Everyone’s Problem
In the previous article, we looked at a problem that sits underneath many disappointing enterprise AI applications. The organisation often already has much of the knowledge the AI needs, but that knowledge is scattered across people, systems, code, policies and operating practices. For years, financial institutions have been able to live with this arrangement. Different … Continue reading Financial Ontology Modelling for Everyone (3): AI Makes Shared Meaning Everyone’s Problem
Financial Ontology Modelling for Everyone (2): Your Organisation Knows More Than Its AI Does
In the previous article, we looked at a strange feature of modern financial AI. Large language models can know an extraordinary amount about finance. They can explain products, markets, settlement, corporate actions, valuation and risk in impressive detail. Yet once we ask them to solve a real problem inside a financial institution, that knowledge often … Continue reading Financial Ontology Modelling for Everyone (2): Your Organisation Knows More Than Its AI Does
Financial Ontology Modelling for Everyone (1): Your AI May Know Finance Better Than You Think — and Still Be Useless
For a while, the story of enterprise AI was mostly about the model. Get access to a stronger large language model, give it more context, connect it to your documents and systems, and the quality of the application should improve. That story is now changing. More people have realised that a powerful model is not … Continue reading Financial Ontology Modelling for Everyone (1): Your AI May Know Finance Better Than You Think — and Still Be Useless
AI-Native Financial Data Foundation (40): Finsight AI-Control — Governance and Runtime Control for Financial AI
The previous article discussed Finsight AI-Foundry and the foundations required to make financial meaning, evidence and reusable capabilities available to AI applications. This article focuses exclusively on Finsight AI-Control. As with the previous article, it is not intended to be a user guide or a detailed product manual. Formal documentation covering configuration, APIs, administration and … Continue reading AI-Native Financial Data Foundation (40): Finsight AI-Control — Governance and Runtime Control for Financial AI
AI-Native Financial Data Foundation (39): Finsight AI-Foundry – The Governed Foundation for Financial AI
The previous article brought together the current beliefs, aims, design principles and products behind the Finsight initiative. It explained why a governed financial AI foundation is needed, what the initiative is intended to achieve and how the Finsight products support that direction. This article focuses exclusively on Finsight AI-Foundry. It is not intended to be … Continue reading AI-Native Financial Data Foundation (39): Finsight AI-Foundry – The Governed Foundation for Financial AI
AI-Native Financial Data Foundation (38): The Belief, Aim, Principles and Products Behind My Finsight Initiative
Background Over time, I encountered many different challenges across the financial data world. Although they often appeared to be separate problems, I increasingly found that many could be traced back to the same root cause: financial meaning was fragmented across systems, documents, models, mappings, rules and people. Financial institutions already hold enormous amounts of data … Continue reading AI-Native Financial Data Foundation (38): The Belief, Aim, Principles and Products Behind My Finsight Initiative
AI-Native Financial Data Foundation (37): Finsight Semantic Repository: Extensible by Design
Financial semantics are never finished. Products evolve. Industry standards release new versions. Institutions replace applications, redesign data platforms and introduce new controls. New analytical and AI workflows also require additional context that may not have been anticipated when the original models were created. The challenge is therefore not simply to define financial meaning once. It … Continue reading AI-Native Financial Data Foundation (37): Finsight Semantic Repository: Extensible by Design
AI-Native Financial Data Foundation (36) — Five Ontology Elements Applied in Financial Services
In the previous blog post, we introduced the four semantic layers of the Finsight Semantic Foundation: the Business, Canonical, Physical and Mapping Layers. Together, these layers organise financial meaning across business concepts, industry-aligned models, real-world systems and the mappings that connect them. But defining the layers is only the beginning. We also need a consistent … Continue reading AI-Native Financial Data Foundation (36) — Five Ontology Elements Applied in Financial Services
Data Ninjago (Financial Data Architecture) 
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