Exploring the future of financial data architecture: AI-native financial data foundation, large-scale financial data processing, and financial AI safety & governance
After several articles on tool-using LLMs, graph-grounded reasoning, and evidence-aware generation, I now want to move one level deeper and examine the technical building blocks required to support an Agentic Semantic Layer. The first technical question is simple: How should financial semantic meaning be modelled, stored, and exposed so that it can be queried and … Continue reading AI-Native Financial Data Foundation (28) – Neo4j: Modelling Semantic Relationships as a Graph
In the previous article, I walked through several papers on tool-using LLMs and agentic orchestration. The main argument was that an AI-native financial data foundation should not rely on an LLM as a standalone answer generator. The LLM should act as an orchestrator over governed semantic capabilities: concept resolution, field discovery, product decomposition, readiness checking, … Continue reading AI-Native Financial Data Foundation (27) – Reading Notes: Graph-Grounded Reasoning and Evidence Checking
In the previous article, I shared my reading notes on several survey-type papers to build a broader landscape view of augmented LLMs, GraphRAG, and tool learning. Those papers were useful because they provided context. They showed that modern LLM systems are moving beyond standalone text generation. LLMs are increasingly being augmented with retrieval, tools, external … Continue reading AI-Native Financial Data Foundation (26) — Reading Notes: Tool-Using LLMs and Agentic Orchestration
Before jumping directly into the agentic semantic layer for the AI-native financial data foundation, I feel I need a bridge. I want to share my reading notes on several key papers that helped me understand this direction and shape the high-level design of the agentic semantic layer. In this post, I will focus on four … Continue reading AI-Native Financial Data Foundation (25) – Reading Notes: Augmented LLMs, Tool Learning and GraphRAG
At this stage of the blog series, my main focus is still foundational. I am not yet writing about the design and implementation of the AI-native financial data foundation. That will come in the next stage while I am currently developing and testing the proof of concept in parallel. Before moving into architecture, components, implementation … Continue reading AI-Native Financial Data Foundation (24) – “AI-Native”, not “AI-Enabled”
As discussed in the previous article, the CDM Event Model is built around state transitions: a TradeState, an Instruction carrying primitive changes, and a resulting after TradeState. Nine primitives compose into business events. A partial novation, for example, is three primitives in one BusinessEvent producing two after-states. This article starts with an end-to-end example - … Continue reading AI-Native Financial Data Foundation (23) – The Full Lifecycle, Lineage, and Two Kinds of Change
The previous article argued that lifecycle modelling should move from event labels to state transitions: before state plus instruction produces after state. This article makes that concrete. First, the two types that sit at the centre of every lifecycle event — Trade and TradeState. Then, the nine primitives that change them. Trade and TradeState A Trade extends TradableProduct. … Continue reading AI-Native Financial Data Foundation (22) – Trade, TradeState, and the Primitives That Change Them
In the previous articles, I spent a lot of time discussing the static structure of financial products. We looked at product nature, economic terms, payouts, price, quantity, schedules, settlement terms, and product qualification. The main question explored was: what is this financial product, structurally and economically? That was a necessary foundation. If an AI-native financial … Continue reading AI-Native Financial Data Foundation (21) — From Product Definition to Trade Lifecycle
The previous articles traced the eight payout types and how they compose into products. An IRS is two InterestRatePayouts. A CDS is an InterestRatePayout plus a CreditDefaultPayout. A commodity swap is a FixedPricePayout plus a CommodityPayout. But if the product type is expressed only through composition, how does a system know what to call it? … Continue reading AI-Native Financial Data Foundation (20) — Product Qualification: The Product Is What Its Structure Says It Is
The previous articles traced six extensions of PayoutBase. This article covers the final two: CommodityPayout and FixedPricePayout. They belong together because they are the fixed and floating legs of a commodity swap — the same compositional pattern as an IRS, but for physical goods rather than interest rates. One leg pays a fixed price per unit. The other … Continue reading AI-Native Financial Data Foundation (19) – CommodityPayout and FixedPricePayout
Data Ninjago (Financial Data Architecture) 
You must be logged in to post a comment.