Exploring the future of financial data architecture: AI-native financial data foundation, large-scale financial data processing, and enterprise data quality & governance.
In the previous article, I discussed Contractual Definition for InterestRatePayout — the accrual periods, payment dates, reset dates, stubs, day count convention, and compounding method that turn an economic agreement into a precise contractual structure. Those attributes are essential. But they still do not give us a payment amount. They tell us what the contract … Continue reading AI-Native Financial Data Foundation (11) — InterestRatePayout: Value Determination
In the previous article, I discussed the first F-PAL stage for InterestRatePayout: Economic Agreement. Economic Agreement tells us what has been commercially agreed: who pays, who receives, what notional applies, and what rate logic determines the obligation. But an interest rate payout cannot be understood from economic terms alone. Instead, interest is always connected to … Continue reading AI-Native Financial Data Foundation (10) — InterestRatePayout: Contractual Timing
In the previous article, I discussed the business meaning and product examples of InterestRatePayout. The key idea was that InterestRatePayout is not just a technical object in the ISDA CDM. It represents a common economic pattern across many FICC products: an interest-based payment obligation. In this article, I move from business meaning to structure. Following … Continue reading AI-Native Financial Data Foundation (9) — InterestRatePayout: Economic Agreement
In the previous article, AI-Native Financial Data Foundation (5) – One IRS Example: From Product Nature to ISDA CDM Structure, I used a vanilla fixed-float interest rate swap as an overview example to show how a financial product can be decomposed into economic components. That example naturally leads us to one of the most important concepts … Continue reading AI-Native Financial Data Foundation (8) — InterestRatePayout: Business Meaning and Product Examples
Financial product models can feel overwhelming because of the sheer number of attributes involved. There are many product types, many market-specific conventions, and many fields that appear to describe small details. A single product may involve notionals, quantities, currencies, rates, spreads, indices, strikes, effective dates, termination dates, reset dates, fixing dates, payment dates, credit events, … Continue reading AI-Native Financial Data Foundation (7) — F-PAL: A Framework for Organising Financial Product Attributes
Initially, I thought it might be too generous to allocate a whole article to what looks like a tiny terminology question. After all, is this really worth debating? In normal market conversation, people say "fixed leg", "floating leg", "premium leg", "protection leg", "cash leg", and "securities leg" all the time, and these terms work quite … Continue reading AI-Native Financial Data Foundation (6) – Payout = Leg?
Before getting into the CDM stuff, I want to take full credit for the suspiciously nice diagram in this post. It was hand-crafted by me (not AI)! In fact, not only for this article, I plan to create diagrams to visualise example products, ISDA CDM details, and eventually the knowledge graph around the semantic layer. … Continue reading AI-Native Financial Data Foundation (5) – One IRS Example: From Product Nature to ISDA CDM Structure
After writing the previous article, What is a Financial Product at all?, I originally planned to move directly into a concrete overview example of ISDA CDM. However, while researching the semantic layer of QuantFlow, I came across an article about ontology modelling and AI-native applications. Although the article is not specifically about finance, I found … Continue reading AI-Native Financial Data Foundation (4) – Literature Review: From Ontology to AI-Native Applications
A few years back, I made an attempt to define and classify financial products from a data modelling perspective. I wrote about this in one of my earlier blog posts, Buy-Side Financial Data Models (2) – Financial Instruments. At that time, I organised financial instruments using a framework based on asset classes, derivative types, and … Continue reading AI-Native Financial Data Foundation (3) – What is a Financial Product at all?
In the previous post, I talked about why my thinking around QuantFlow has changed. The short version is that I am starting to believe that the future users of many financial data platforms may not be human quants, analysts, or engineers directly. Increasingly, the real users may be AI agents. However, there is one important … Continue reading AI-Native Financial Data Foundation (2) – FICC Canonical Data Model
Data Ninjago (Financial Data Architecture) 
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