Normalize mock bank, account, balance, and transaction data into a common financial data model, then run explainable insight workflows across cash flow, recurring payments, and financial health signals.
4Data sources
7CDM entities
6Insight tools
Project overview
AI-powered open finance intelligence platform
This project models how modern open-finance platforms transform fragmented account and transaction feeds into normalized, application-ready intelligence. The emphasis is not a generic budgeting chatbot — it is the reusable data platform and workflow layer that makes financial insights explainable, auditable, and safe for downstream product experiences.
🏦
Financial connectivity
Mock Plaid-style institutions, accounts, balances, and transactions enter the platform as fragmented source records from multiple providers and schemas.
🧩
Open finance CDM
Source records are normalized into reusable account, institution, merchant, transaction, category, cash-flow, and signal entities.
📊
Deterministic insight tools
Recurring payment detection, cash-flow analysis, runway estimation, merchant enrichment, and affordability signals are generated through explainable services.
🤖
AI workflow orchestration
An agent workflow plans tool execution, streams workflow progress, surfaces evidence, and generates review-oriented recommendations.
✅
Explainable recommendation layer
Signals are surfaced as evidence-backed risks and opportunities instead of opaque chatbot-style financial advice.
Problem
Financial data arrives fragmented and difficult to operationalize
Financial data providers expose accounts, balances, merchants, and transactions through inconsistent schemas, naming conventions, category systems, and timing patterns. Product teams often inherit raw transactional data without the operational context needed to build reliable financial experiences.
Raw transactions can show what happened, but they rarely explain which patterns matter, which recommendations should be trusted, or what workflow action should happen next.
Solution
Normalize first, reason second
The platform ingests synthetic open-finance data, maps it into a reusable common financial data model, enriches entities with derived metadata, and then runs an explainable workflow that coordinates deterministic financial analysis tools.
The output becomes a reusable recommendation layer: recurring charges, income volatility, subscription exposure, runway, affordability signals, and evidence-backed financial-health opportunities.
Open finance common data model
Reusable financial entities and explainable workflow orchestration
The architecture mirrors common data model patterns used in healthcare and enterprise data systems: preserve raw provider payloads, normalize them into stable operational entities, enrich them with derived signals, and expose a workflow layer that keeps recommendations inspectable and auditable.
01
Ingest
Load accounts, balances, institutions, and raw transactions from connected financial providers.
02
Normalize
Map provider-specific payloads into a reusable common financial data model.
03
Enrich
Attach merchant, category, recurrence, and cash-flow metadata.
04
Analyze
Calculate runway, volatility, subscription exposure, affordability, and financial-health signals.
05
Recommend
Generate explainable product recommendations with visible workflow traces and evidence.
System architecture
AI-native financial data and workflow platform
Frontend
Next.js operational dashboard with workflow streaming, recommendation panels, and explainable insight visualization.