Faster conversion of statements into structured data
The proof of concept converts bank statement PDFs into structured transactions and balances for review.
Confidential AI proof of concept
FinSight AI
Multi tenant bank statement analysis, financial Q&A and visual intelligence for finance teams.
FinSight AI was developed as a POC for a CA and finance advisory team that needed a faster, more affordable, and more intelligent way to analyze bank statements and extract financial insights. The team worked with bank statements from individuals, small businesses, and clients across different formats and banking layouts.
Project classification: This proof of concept was developed for a CA and financial advisory team.
Searchable and traceable transaction records
Source metadata supports traceable search and question answering across extracted transactions.
Improved visibility into UPI and cash flow patterns
Classification and dashboards surface UPI, cash flow, channel and spending patterns.
01 / Business context
Who needed the solution
FinSight AI was developed as a POC for a CA and finance advisory team that needed a faster, more affordable, and more intelligent way to analyze bank statements and extract financial insights. The team worked with bank statements from individuals, small businesses, and clients across different formats and banking layouts. These statements contained transaction data, account details, payment channels, narration fields, balances, UPI transactions, debit/credit flows, and statement periods. Existing workflows were not fully manual, but they still involved significant effort.
02 / Challenge
What needed to change
The main challenge was that bank statement analysis was still time consuming, fragmented, and difficult for non technical finance users.
Bank statements arrived in inconsistent PDF formatsBank statements came in different PDF formats, layouts, date styles, and transaction structures.
Transaction extraction required manual clean upExtracting transaction data from PDFs required manual cleanup or semi manual processing.
Finance teams depended heavily on spreadsheetsCA teams often had to depend on spreadsheets or expensive accounting systems for analysis.
Modern payment flows such as UPI were difficult to classifyExisting systems were not always flexible enough for modern payment flows such as UPI, transfers, digital channels, and mixed transaction narrations.
Users needed natural language access to financial dataThe platform needed to support multiple users or tenants while keeping financial data organized and separated.
03 / Workflow transformation
Before and after
Previous workflow
- Bank statements arrived in inconsistent PDF formats
- Transaction extraction required manual clean up
- Finance teams depended heavily on spreadsheets
- Modern payment flows such as UPI were difficult to classify
- Users needed natural language access to financial data
Structured workflow
- Parse and structure statements
- Normalise transactions
- Build financial analytics
- Enable grounded financial Q&A
- Review structured outputs
04 / Solution
How we approached it
We designed FinSight AI as a multi tenant financial intelligence platform combining document extraction, transaction intelligence, vector search, RAG based Q&A, metadata enrichment, and real time analytics dashboards. The solution converted bank statement PDFs into structured, searchable, and analyzable financial data.
Parse and structure statements
Extract transactions, balances, dates and source metadata from bank PDFs.
Normalise transactions
Handle multi format dates and classify income, expenses and payment channels.
Build financial analytics
Calculate KPIs and present trends through a multi tenant dashboard.
Enable grounded financial Q&A
Index structured data and answer questions through retrieval augmented generation.
05 / Example workflow
From input to reviewable output
1Parse and structure statements
2Normalise transactions
3Build financial analytics
4Enable grounded financial Q&A
5Validate transaction outputs
6Finance professional review
06 / Delivery scope
What the delivery covered
Business and product workflow
- PDF to Structured Data Pipeline
- Transaction Type Inference Engine
- Metadata Enrichment and Source Traceability
AI, data and automation
- LangChain
Application and cloud engineering
- React
- Python
- FastAPI
- MongoDB
- Docker
Scope boundary
The work was a proof of concept. Production scale accuracy, time savings and financial decision impact were outside the validated scope.
07 / Architecture and controls
How the system is organised
LangChain
React
Python
FastAPI
MongoDB Atlas Vector Search
MongoDB
Docker
TypeScript
Source traceability
Extracted transactions retain statement and metadata context for review.
Structured validation
Dates, balances, debit/credit fields and classifications are normalised before analytics and Q&A.
Human financial review
Dashboards and natural language answers support finance professionals and do not replace qualified judgement.
Tenant separation
The architecture separates organisational data within the multi tenant proof of concept.
08 / Business value
Value observed during validation
1Pilot observed
Faster conversion of statements into structured data
The proof of concept converts bank statement PDFs into structured transactions and balances for review.
2Pilot observed
Searchable and traceable transaction records
Source metadata supports traceable search and question answering across extracted transactions.
3Pilot observed
Improved visibility into UPI and cash flow patterns
Classification and dashboards surface UPI, cash flow, channel and spending patterns.
4Pilot observed
Accessible dashboards for non technical finance users
Finance users can explore prepared dashboards without manually building spreadsheet charts for the initial review.
5Pilot observed
A multi tenant foundation for financial intelligence workflows
The multi tenant design demonstrates how the workflow could be extended across organisations.
09 / Technology
Every component, with its role in the delivery.
Technology choices from the supplied project brief, mapped to the workflow each component supports.
LangChain
RAG and LLM application orchestration
React
Reviewer or product user interface
Python
AI, data processing and backend logic
FastAPI
Backend APIs and workflow orchestration
MongoDB Atlas Vector Search
Vector retrieval and semantic search
MongoDB
Document and application data storage
Docker
Containerised deployment
TypeScript
Typed application engineering
Recharts
Analytics dashboard visualisation
PDF processing
Document parsing, chunking and normalisation
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