Day 17 of #AI4Good. I used an AI coding agent to build a personal-finance app — and the single most important design decision was to keep AI, the cloud, and the network completely out of it. Your bank statement is read, parsed, and reconciled on your own device. Nothing is uploaded. Nothing is stored anywhere but your phone.
I'm Naveen Gopalakrishna — an AI Global Black Belt at Microsoft, a compulsive builder after hours. Most #AI4Good builds put AI in the product. SpendLens is the opposite, on purpose: the "good" here is financial privacy, so the intelligence lives in the parser, not in a model, and your money never leaves the device.
- ▶️ Try it in the browser (nothing retained): naveenneog.github.io/SpendLens
- 📦 Android APK: github.com/naveenneog/SpendLens/releases/latest
- 💻 Source: github.com/naveenneog/SpendLens
What it is
SpendLens reads a bank-statement PDF and turns it into an honest picture of where your money went — without a bank login, an OTP, a screen-scrape, or an upload.
- Import a PDF through the native Android/iOS document picker (or drag one into the browser version). Password-protected statements are supported; the password is used in memory and never kept.
- Reconciled summary — total spent, total received, opening and closing balance, and date range, in Indian digit grouping with tabular figures.
- Spend by category, daily-spend trend, and largest spends, each with an accessible table/list equivalent and labels that never rely on colour alone.
- Searchable, filterable transactions with editable categories. Correct a category once and SpendLens remembers the merchant rule for next time.
- Saved statement history on-device, and a one-tap delete everything that really deletes everything.
- Detection for 78 RBI scheduled retail banks — public-sector, private, small-finance, payments, and regional-rural — by official name, known aliases, and IFSC prefixes.
The cover image is the browser version analysing a fully synthetic Axis sample: an Axis Bank Ltd. statement, marked Verified, Active only in this tab, spend broken out by category, and a daily-spend chart — all rendered without a single network request leaving the page.
How it was built
The whole app is Flutter (Dart) with a thin layer of native platform code, and it was built with GitHub Copilot CLI in autopilot mode — the agent researched Indian statement formats, wrote the parser and tests, ran the builds, and drove an Android emulator smoke test end to end.
On-device PDF text extraction. Rather than ship a commercial PDF SDK, SpendLens calls the platform's own engine on a worker thread: PDFBox-Android (Kotlin) on Android and PDFKit (Swift) on iOS, over a method channel. Image-only / scanned PDFs are rejected explicitly (on-device OCR is a future module) instead of pretending they parsed.
A deterministic parser — no ML, no LLM. This is the heart of the trust model. The generic parser:
- recovers multiline transaction descriptions and handles the common Indian date formats;
- infers debit vs credit from the direction of consecutive running balances;
- checks the opening + credits − debits = closing equation, and, when the statement declares its own debit/credit totals, validates those too — in the detected column order;
- preserves
DR, leading-minus, and zero-crossing overdraft balances; - and returns an explicit Verified, Needs review, or Incomplete verdict with the diagnostic attached.
It never converts a parse error into a success-shaped empty dashboard. If the numbers don't reconcile, the UI says so.
Money is integer paise, never floating point. Every amount is stored and summed as an integer so ₹0.01 rounding drift can't creep into a total.
Explainable categorization. Categories come from transparent keyword rules with token-boundary matching, so "Zuber" doesn't get filed under the ride brand "Uber," and your own merchant corrections always override the built-ins.
Local persistence. Statements, edited categories, merchant rules, and settings live in SQLite (analyses stored as JSON documents for schema agility). Category-rule updates and full data deletion are atomic transactions, and concurrent first-access is serialised through a single database-open future so startup can't race.
A custom native document picker. file_picker didn't compile cleanly against Flutter 3.44 / AGP 9, so SpendLens ships its own Storage Access Framework / UIDocumentPickerViewController picker — which also avoids asking for broad storage permissions.
A zero-retention web twin. The GitHub Pages version does the same parsing in the browser with self-hosted PDF.js and a Content-Security-Policy of connect-src 'none' — no cookies, local storage, IndexedDB, service worker, analytics, or upload endpoint. A static verify_web_privacy check runs in CI to keep it that way, and the analysis auto-clears after 15 minutes.
Verified before shipping. 40 automated tests (parser, bank registry, categorization, SQLite, controller, privacy, concurrency, widgets), a clean flutter analyze, the web-privacy verifier, and a scripted Android emulator smoke that imports a synthetic statement, checks reconciliation, and confirms that "delete everything" survives a relaunch.
The good
Most "see where your money goes" apps ask you to connect your bank — handing a third party your credentials, or trusting an aggregator to hold your transaction history. SpendLens is a bet that you shouldn't have to. Everything happens on the device you already trust with the statement. No account, no analytics SDK, no remote parser, no generative-AI dependency; Android backup/transfer is disabled and the iOS database is excluded from backups.
And it's built for Indian statements specifically — the date formats, the digit grouping, and coverage across 78 banks including the small-finance and regional-rural banks that mainstream tools ignore. When a layout is unfamiliar it degrades honestly to Needs review rather than inventing certainty.
The meta-point is the one I like most: an AI agent built a finance tool whose entire value proposition is that AI and the network never touch your money. That's a version of #AI4Good worth keeping in the toolkit — using the agent to ship privacy, not to harvest it.
Try it
- ▶️ Live (zero-retention browser analyzer): naveenneog.github.io/SpendLens
- 📦 Android APK: github.com/naveenneog/SpendLens/releases/latest
- 💻 Source: github.com/naveenneog/SpendLens
naveenneog
/
SpendLens
Privacy-first Flutter app that turns Indian bank-statement PDFs into on-device spending insights - no bank login, cloud upload, analytics, or LLMs.
SpendLens
SpendLens is a privacy-first Flutter app that turns Indian bank-statement PDFs into understandable spending summaries without bank logins, cloud uploads, or LLMs.
Version 0.2
- On-device PDF text extraction and deterministic parsing
- Detection registry for 78 RBI scheduled retail banks
- Reconciliation and partial-parse diagnostics
- Editable categories and remembered merchant rules
- Category, daily-spend, and large-spend views
- Searchable transactions and saved statement history
- Adaptive Android/iOS layouts, light/dark themes, and accessible chart alternatives
- Native Android and iOS document pickers with no broad storage permission
- Password-protected PDF handling
- Local SQLite persistence and complete data deletion
- Zero-retention GitHub Pages analyzer in
docs/ - Self-hosted PDF.js with a CSP that forbids authored network connections
- Explicit browser-memory clear and 15-minute inactivity clearing
Development
Flutter is installed at C:\Users\navg\flutter.
& "C:\Users\navg\flutter\bin\flutter.bat" pub get
& "C:\Users\navg\flutter\bin\flutter.bat" analyze
& "C:\Users\navg\flutter\bin\flutter.bat" test
& "C:\Users\navg\flutter\bin\flutter.bat" run
The current sideload artifact is:
artifacts\SpendLens-v0.2.0.apk
It follows…
Part of the #AI4Good series — one build at a time, from a single laptop, with the "who does this help?" chosen first. GitHub · LinkedIn. #AI4Good
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