The finance industry has a dirty secret: some of the most profitable ML applications are also…

the most boring.

Not algorithmic trading with RL (that's the sexy one everyone talks about). I mean credit risk scoring with XGBoost. Automated document processing for compliance. Transaction monitoring for anti-money laundering. Customer churn prediction.

These aren't exciting LinkedIn content. But they're where the money is.

The fintech + AI intersection pays extremely well because the stakes are enormous — a 0.1% improvement in fraud detection can save a bank millions annually.

Skills that finance companies actually hire for: time series forecasting, anomaly detection, NLP for financial text, risk modeling, and real-time streaming with Kafka. Notice the overlap with other ML work, just with higher stakes and better compensation.

If you're an ML engineer who's willing to learn some finance domain knowledge — understanding how credit scoring works, what regulatory compliance requires, how trading systems operate — you'll find doors open very quickly.

Finance doesn't need more analysts. It needs ML engineers who understand money.