How LLM Disrupts Data Science in the Banking Industry

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The banking industry leverages data science across multiple domains, ranging from risk management to customer analytics and fraud detection. Below is an overview of the key areas and how each is evolving with emerging technologies like AI, LLMs, and cloud computing.

1. Risk Management and Compliance

What It Involves:

• Credit risk modeling (e.g., probability of default, loss given default)

• Market and liquidity risk analysis

• Regulatory compliance (e.g., Basel III, IFRS 9)

• Anti-money laundering (AML) & Know Your Customer (KYC) checks

Transformation:

✅ AI-Powered Risk Models:

• Traditional logistic regression models are being replaced by machine learning models for better risk assessment.

• Explainable AI (XAI) is helping regulators understand black-box models.

✅ Alternative Data Sources:

• Use of transactional data, social media behavior, and even satellite imagery for credit scoring.

• Graph-based risk modeling is improving fraudulent network detection.