How I Built an AI-Powered Cash Flow Automation System

A hands-on experiment where I used AI and vibe coding to automate my personal cash flow statement— revealing what the future of finance automation could look like.

In my previous blog, How I Built a Voice-Enabled AI Coach without coding, I shared how experimenting with generative AI helped me create a digital prototype that could listen, reflect, and respond like a coach. In this blog, I will share my experience automating my income and expense tracking using AI.

Note: This article intentionally simplifies certain technical and security aspects. The goal is not to present an enterprise-grade framework, but to demonstrate how non-technical professionals can now use AI to design meaningful automations safely, creatively, and independently.

As a finance professional, I’ve seen automation evolve from macros and RPA to enterprise tools. But I’ve also seen how many valuable ideas never made it past the approval committee, because the cost-benefit equation didn’t justify small automations.

After taking a career break, I began manually tracking my finances each month, a simple way to stay disciplined and understand my spending patterns. When tools like ChatGPT emerged, I decided to bring that process to life through vibe coding, an AI-assisted approach to software development that allows you to describe what you want and let the AI handle the coding.

The Early Experiments: Learning to Vibe Code

My first experiments were small but addictive. I wanted to understand how far AI could help me code and automate everyday finance tasks:

Data Extraction (Gpay) and Input consolidation (Bank Statements):

HTML and PDF statements were converted into structured Excel files, my first real encounter with unstructured data. The process revealed valuable insights into spending patterns and income sources, and it felt empowering!

Finance dashboard:

An interactive dashboard was built using Streamlit to visualize spending trends, inflows, and balances. It later evolved to support multi-year trial balance uploads for small enterprises, enabling year-on-year trend analysis, expense head comparisons, and dynamic income–expense insights.

Investment and market analysis automation:

Stock research was automated by web scraping data for Nifty 50 companies, integrating broker recommendations, and using AI to generate custom scanners on Chartlink.

Each mini project built confidence and taught me something new: data cleaning, logic building, prompt precision, and the joy of seeing results appear instantly.

The Turning Point: Building a Complete Automation

After nearly a year of these experiments, I realized I had something valuable — three years of my own expense data, neatly structured and rich in patterns.

It felt like the perfect dataset to test a bigger ambition:

To build a fully automated cash flow system, end to end, from bank statement extraction to forecasting.

The Architecture of cash flow automation

Step 1: Unifying the Raw Data

I started with multiple bank statements with different file type (PDF/Excel) and formats. The challenges mirrored what every finance team faces:

Non-standardized formats of the statements
Inconsistent debit/credit representations
Ambiguous or truncated transaction descriptions
Date formats that refused to align

In large organizations, this mirrors the data ingestion stage. Teams routinely consolidate inputs from multiple ERPs, regional systems, and banking sources into a standardized reporting format, often the most time-consuming and error-prone part of financial reporting.

Step 2: Building the Rule-Based Categorization Engine

Once data was standardized, the next step was to build a rules-based categorization engine:

Used regex and keyword mapping to assign categories (e.g., “Zomato” → Food & Groceries, “Int Recd” → Interest Income)
Added priority logic to handle overlaps (“Wellness” could be Health related or it could also be Salon & Spa)
Created exception rules to override defaults

In corporate record-to-report, most structured transactions like salaries, rent, or depreciation are auto-posted by the ERP. But rule-based automation adds real value around the system specially in the grey areas that still rely on judgment. Think of journal uploads from different business units that need pre-validation, local GLs that must map to a global chart during consolidation, or intercompany entries that require matching and elimination. It’s the same logic used to speed up vendor reconciliations or flag unusual postings before close.

Step 3: Predicting Classification with Machine Learning

When the rules hit their limit, I added machine learning (TF-IDF + Logistic Regression) trained on 3 years of historical data.

By training on the past data, the model could predict how new transactions might be classified. A confidence threshold of 65% ensured low-certainty predictions were reviewed manually.

In a corporate context, the same principle can power learning from historical patterns to anticipate what comes next, whether it’s forecasting expenses and accruals based on past management accounts, or using invoice intelligence to read free-text descriptions and automatically tag them to the right cost category or project.

Step 4: Forecasting with a Financial Model

The next step was to use this Cash Flow Statement to forecast my fund position for the next 25 years.

The model incorporated variables such as investment corpus, asset allocation across equity, debt, and cash across different timelines (for e.g. aggressive allocation strategy until 60 years) , expected annual returns for each asset class, inflation rates, and planned withdrawals.

These inputs feed into a calculation sheet that automatically generates year-by-year projections. Any adjustments to the input sheet would adjust the portfolio balance, returns, and rebalancing logic in real time.

While this model was built using rule-based financial formulas, it lays the groundwork for a future version powered by machine learning.

In corporate finance, this kind of modelling underpins the entire FP&A and decision-support cycle. The same logic powers driver-based forecasting models that let teams adjust variables like growth rates, headcount, or inflation assumptions and instantly see their impact on the P&L, cash flow, or balance sheet. It’s also what supports scenario planning like The Best, Base, and Worst-case outlooks to guide strategic choices.

Step 5 (The LLM Vision): Creating a Conversational Interface

This final layer is a conversational interface. Built on Python and Streamlit, the app allows users to upload Excel or CSV files and describe, in plain language, what the data is and what they want to see — “Cash Flow Statement for FY 21–24 — show income trends and top cost heads.”

The system connects directly to ChatGPT, which acts as the reasoning engine:

Understands structure item – The LLM reads and interprets the uploaded data.

Understands intent – It captures what the user wants to see or analyze

Generates logic – In the background, it writes Python code to process the data.

Builds dashboards – Automatically produces visual summaries — trends, insights, or category views, without the user needing to specify any chart logic.

Together, these layers formed a complete automation journey — from raw data to intelligent conversation. But beyond the code and dashboards, what truly stood out was how accessible automation becomes when AI enters the picture.

Automation at scale starts with the confidence to automate something small.

Finance leaders who encourage experimentation, invest in basic AI literacy, and shift from ROI-driven approval cycles to learning-driven exploration will build faster, more adaptive teams.

Finance leadership in the AI era is about curiosity, logic, and the courage to experiment. What excites me most is how accessible this has become — if you understand your data and can think in workflows, AI can now handle the heavy lifting.

AI is transforming both the how and the who of automation — it’s redefining who builds it.