AI/ML in Finance: Hype, Hope, and What Actually Works

Everyone’s talking about AI. But what does it really do for finance? Here’s a breakdown of what’s real, what’s useful, and what’s complete nonsense.

Hey folks,

Last week, I shared why I started QuantML—to help Indian finance professionals level up from Excel to Python, and eventually to AI/ML tools that actually work.

Now let’s talk about the elephant in the boardroom: AI in finance.

It’s everywhere. Every company is pitching an “AI-powered” tool. Every VC deck has “machine learning” written in 48pt font. And every finance pro is starting to panic just a little bit.

Let’s slow down. Take a breath. And get honest about what AI/ML can and can’t do in finance.

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What’s Just Hype?

Let’s get the fake stuff out of the way.

1. “AI will predict stock prices perfectly.”
   No, it won’t. If that existed, the person building it wouldn’t be selling a course on YouTube. They’d be on a yacht.

2. “ChatGPT can do my job.”
   No, but it might do the boring 20% of your job. It still doesn’t know how to structure a proper P&L forecast or understand Indian GAAP nuance.

3. “ML will replace analysts.”
   No. It will replace bad analysts who do nothing but copy-paste data and call it insight.

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What’s Actually Useful?

Here’s what’s real and working right now:

• Forecasting with ML models
  Revenue prediction, expense trends, scenario simulations using tools like Facebook Prophet, XGBoost, or ARIMA—especially useful in FP&A.

• Backtesting trading strategies
  Simple ML models can help test hypotheses, reduce noise, and optimize parameters based on real Indian stock market data.

• Anomaly detection
  Machine learning is great at flagging weird financial behavior in big datasets—useful for audit, finance ops, or risk.

• Document automation with GPT
  GPT-based tools can draft emails, create reports, summarize financial statements, and help you automate documentation faster.

• Data cleaning & transformation
  ML + Python = no more wasting hours fixing CSVs. Great for consolidating messy internal data and external APIs.

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How Should You Think About It?

AI/ML is like having a very smart, very fast intern.

It won’t replace your judgment, but it will take your grunt work and return it faster, cleaner, and with fewer “#REF” errors.

But you need to learn how to talk to it—that’s where Python comes in.

Python is the bridge between:

• Financial logic

• Data

• AI tools

Once you learn Python, you’re not just reading about AI anymore. You’re building with it.

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Want to Try Something?

In next week’s issue, I’ll share a simple forecasting model built in Python using Indian company data. Something practical. Something useful. Something your manager will pretend to understand in the next review meeting.

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If you found this helpful, forward it to someone in finance who still thinks machine learning is just another buzzword. Or post it on LinkedIn with a hot take and become that guy.

More soon.
QuantML.in