Volume VII / Sanctions Intelligence
FinanceIntel
Miami Software Journal
A fintech engineering showcase combining OpenAPI, TypeScript, Python, OFAC data, fuzzy search, uncertainty modeling, and AI-assisted risk analysis.
Case Study
Sanctions
Intelligence
Demo
This project demonstrates how raw compliance data can become a developer-friendly API, then become an evidence-based risk engine. The first layer exposes U.S. Treasury OFAC SDN data through a documented OpenAPI interface. The second layer applies uncertainty-aware screening so borderline matches do not become blind yes/no decisions.
The larger concept is a financial intelligence workflow that can combine sanctions data, country risk, news sentiment, and AI analysis to support market research.
The Problem
Messy entity data needs explanation.
Financial and compliance systems often need to reason about names, aliases, countries, sanctions programs, and missing context. A fuzzy score alone does not explain whether a match should be blocked, ignored, escalated, or sent back for more information.
The Build
Part 1 / API Layer
SDN OpenAPI
A TypeScript serverless API for querying OFAC SDN data through OpenAPI, Swagger UI, ReDoc, fuzzy search, metadata, entity lookup, and scheduled refresh.
Part 2 / Risk Engine
ED 209
A Python FastAPI prototype that uses Subjective Logic opinions to explain uncertainty in sanctions screening and recommend actions like GATHER_MORE.
Architecture
From source data to decision support
The point is not just querying OFAC. The point is showing a path from public compliance data to documented APIs to uncertainty-aware reasoning.
- 01
Treasury OFAC SDN source data
- 02
TypeScript OpenAPI layer
- 03
Fuzzy search and entity lookup
- 04
Subjective Logic evidence model
- 05
Decision: clear, block, escalate, or gather more
Why It Matters
A binary flag is often too crude.
A useful screening system should explain what evidence exists, what evidence is missing, and whether the next action is clear, escalate, block, or gather more information.
I can turn messy public financial and compliance data into documented APIs, then build reasoning systems on top of it.
Technical Stack
What I would build next
- Batch screening for multiple entities.
- Country risk dashboard built from sanctions counts and entity types.
- News ingestion with source citations.
- Historical sentiment snapshots.
- A market hypothesis page clearly labeled as speculative analysis.
Recruiter Summary
I built a small financial intelligence portfolio project that combines a TypeScript OpenAPI sanctions data API with a Python FastAPI uncertainty engine. The API exposes OFAC SDN data through Swagger/ReDoc with fuzzy search and entity lookup. The Python layer explores Subjective Logic for compliance screening, so borderline matches can return actionable decisions like gather more evidence instead of a crude binary flag.