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.
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 (44): Mention Extraction, Context Interpretation, and Expected Entity Types
In the previous articles, I discussed query normalisation, query enrichment, and intent classification. These methods help a system clean up the user's language, generate alternative query forms, and determine what the user is broadly trying to do. However, identifying the intent is not enough. Consider the following question: Where does the floating rate index map … Continue reading AI-Native Financial Data Foundation (44): Mention Extraction, Context Interpretation, and Expected Entity Types
AI-Native Financial Data Foundation (43): Intent Classification for Semantic Query
This blog article focuses on three questions: Why is intent classification needed? What should an intent represent? What classification approaches are available? The aim is not to provide a complete machine-learning survey. It is to explain the main design choices and how they support semantic query. 1. Why Intent Classification Is Needed Intent classification identifies … Continue reading AI-Native Financial Data Foundation (43): Intent Classification for Semantic Query
AI-Native Financial Data Foundation (42): An Exhaustive List of Query Normalisation Methods
This is intentionally a technical, list-style article. Its purpose is to provide a reasonably exhaustive catalogue of candidate techniques for reducing superficial language variation without changing the user's intended business meaning. Rather than proposing one preferred normalisation approach, it is intended to serve as a reference for designing the query-planning service: which techniques are available, … Continue reading AI-Native Financial Data Foundation (42): An Exhaustive List of Query Normalisation Methods
AI-Native Financial Data Foundation (41): How Business Language Turns into Semantic Queries
As I have always emphasised, the semantic foundation is the core of my AI-Native Financial Data Foundation initiative and where much of its real value lies. As this series has shown, I have spent a great deal of time and effort thinking through how that foundation should be structured, governed, and made operational. However, building … Continue reading AI-Native Financial Data Foundation (41): How Business Language Turns into Semantic Queries
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
Data Ninjago (Financial Data Architecture) 
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