Module 20 Lesson 1: AI in Banking Security
Protecting the money. Learn the unique requirements for AI security in the finance sector, from Anti-Money Laundering (AML) to fraud detection.
Module 20 Lesson 1: AI security in Banking and Fintech
In Finance, a single AI mistake can lead to Millions of dollars in theft or Massive Regulatory Fines. The stakes are "Maximum."
1. Fraud Detection Hijacking
Banks use AI to find "Stolen Credit Cards."
- The Attack: Adversarial Noise. An attacker (the fraudster) learns the "Signature" of the fraud-detection AI.
- They perform their illegal transactions using specific patterns (e.g., $9.99 every 12 minutes) that the AI has been "Reprogrammed" (Module 18) to ignore.
- The result: A "Silent Drain" on customer accounts.
2. Market Manipulation via AI
Modern trading bots use Sentiment AI to read "News" and "Twitter."
- The Attack: Mass Poisoning. An attacker uses a botnet to tweet 1,000,000 fake messages about a specific company (e.g., "Company X is going bankrupt").
- The "Sentiment AI" in the bank's trading bot sees the spike in negativity and automatically Sells millions of shares, causing a real "Flash Crash."
3. The "Unexplainable" Credit Denied
If an AI denies a loan, the bank must be able to explain why (under laws like the Fair Credit Reporting Act).
- The Security Risk: If the "Explainability" tool (XAI) is hacked or manipulated, it might "Cover up" for a biased model, or leak sensitive "Hidden features" used by the bank to determine creditworthines.
4. Securing the "Quant" Model
Investment banks spend billions on "Quant" models. These models are the ultimate "Model Weights" prize (Module 11).
- The Defense: Banks often run their most sensitive models in Enclaves (Confidential Computing) where even the cloud provider cannot see the math.
Exercise: The Fintech Security Lead
- You are building an AI that "Auto-approves mortgages." What is your #1 fear: Inaccuracy or Bias?
- How can "Adversarial Examples" be used to bypass a bank's "Know Your Customer" (KYC) facial recognition check?
- Why is "Model Versioning" critical for financial audits?
- Research: What is "SEC Regulation S-P" and how does it apply to AI and user privacy?
Summary
Fintech AI security is about Integrity and Auditability. In a world where money is just numbers on a screen, the AI that manages those numbers must be the most secure part of the foundation.
Next Lesson: Protecting lives: Protecting AI in Healthcare and Medtech.