Ethics, Compliance & Explainability in FinTech: Building Responsible AI for Financial Systems

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Course Modules:

Module 1: Principles of Ethics in FinTech AI

Why ethics matter in financial technology

Fairness, transparency, privacy, and non-discrimination

Use cases: credit scoring, robo-advisors, and automated underwriting

Module 2: Financial Regulation and AI Compliance

Overview of global regulations: GDPR, ECOA, FCRA, Basel III

Regulatory requirements for automated decisions

Documentation, audit trails, and data access rights

Module 3: Fairness and Bias in Financial Models

Sources of bias in financial datasets and algorithms

Metrics for fairness: disparate impact, equal opportunity

Bias detection and mitigation techniques

Module 4: Explainable AI (XAI) in FinTech

What is explainability and why it matters

Post-hoc explanation methods: SHAP, LIME

Interpreting black-box models (e.g., credit scoring, fraud flags)

Module 5: Trustworthy System Design

Human-in-the-loop systems and override mechanisms

UX and communication for decision explanations

Governance frameworks for FinTech AI (model risk management)

Module 6: Capstone Project – Design a Responsible AI System

Choose a financial AI use case (e.g., loan approval, investment advice)

Identify and mitigate ethical and regulatory risks

Build an explainable, compliant AI prototype with documentation

Tools & Technologies Used:

Python

Scikit-learn, XGBoost

SHAP, LIME, Fairlearn, AIF360

Streamlit or Flask (for explainability dashboards)

Target Audience:

FinTech developers and product managers

Compliance officers and regulatory tech teams

AI practitioners building financial models

Students studying responsible AI in finance

Global Learning Benefits:

Build FinTech systems that are ethical, fair, and regulation-ready

Detect and mitigate algorithmic bias in financial decisions

Increase transparency and trust with explainable AI tools

Prepare for audits, customer protection laws, and global compliance standards

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