Data Management In Banking 101

Explore how banks manage data — from governance and infrastructure to integration, reporting, and AI-powered insights

What you'll learn

Understand the Fundamentals of data management, and why it’s vital for banking transformation

Learn how Data Governance and Regulatory Compliance (e.g., BCBS 239, GDPR) shape data handling in financial institutions

Explore core elements of Banking Data Infrastructure, including data architecture, modelling approaches, and storage models

Dive into Metadata Management, with practical emphasis on data quality, data lineage, and data integration

See how banks turn raw data into insights through Business Intelligence (BI) reporting

Examine real-world Data Analytics and AI Use Cases—from fraud detection and credit risk modeling to customer personalization

Requirements

No prerequisites

Description

What is this course about?In today’s data-driven financial landscape, data is the backbone of modern banking operations —powering everything from regulatory compliance and risk management to customer insights and digital innovation. As banking transforms under the weight of new technologies and increasing regulatory demands, effective data management is no longer optional—it's mission-critical.Why should you sign up?This course is designed for professionals who want to bridge the gap between data, technology, and banking operations. Whether you're working in compliance, risk, IT, analytics, or business strategy, you'll gain the tools and knowledge to navigate the complexities of banking data with confidence and strategic insight. Or to be more specific:Gain In-Demand Skills – Master the fundamentals of data architecture, governance, and analytics in bankingEnhance Career Opportunities – Stand out in the banking and fintech industry with specialized knowledge in data-driven decision-makingStay Compliant & Secure – Learn how banks manage data while adhering to regulations. GDPR, Basel III, BCBS 239, and AMLAI & Big Data Readiness – Understand how AI, machine learning, and data lakes are transforming financial institutionsWhat will the course format look like?Expect hands-on examples, architecture diagrams, real case studies, and regulatory insights—all structured to give you practical skills and strategic understanding to lead or contribute to data initiatives in your bank.This course is your gateway to becoming a data-savvy banking professional.

Overview

Section 1: Introduction, Broader Context and Rationale

Lecture 1 What is this course about?

Lecture 2 Why is data crucial in banking?

Lecture 3 What are the types of data in banking?

Lecture 4 Data Management & Data Governance - how do these concepts reinforce each other?

Lecture 5 What does poor Data Management entail and how to overcome these challenges?

Lecture 6 Data Management in the light of Regulatory Compliance & Risk Management

Section 2: Data Architecture (Infrastructure & Storage)

Lecture 7 How does a typical (high-level) Banking Data Infrastructure look like?

Lecture 8 Key Functions and types of (modern) Core Banking Systems (CBS)

Lecture 9 Data Warehousing and Data Lakes in Banking

Lecture 10 Legacy vs. Modern Banking Data Infrastructure

Section 3: Data Management in Banking Deep-Dive

Lecture 11 Master Data Management (MDM) in Banking

Lecture 12 Data Quality Management in Banking

Lecture 13 Data Integration and Interoperability

Lecture 14 Technical Blueprint for Banking Data Integration

Lecture 15 Data Models - Why do they matter in Banking?

Lecture 16 Data Models in Banking - A Three-Step Approach

Lecture 17 Example - (Simplified) Banking Data Model

Lecture 18 Additional Lecture: BCBS 239 - Five-Step Best Practice implementation guide

Lecture 19 Additional Lecture: Data Lineage Best practices implementation

Section 4: Reporting, Insight Generation, Data Driven Decision Making & Compliance

Lecture 20 Differences and Overlaps between BI, Data Analytics, ML & AI

Lecture 21 Excursus: Artificial Intelligence at a glance

Lecture 22 Overview Use Cases - Reporting, Analytics, Intelligence, Prediction & Automation

Lecture 23 Overall coverage of (potential) Use Cases

Lecture 24 Use Case at glance – BI (Business Intelligence) Reporting in Banking

Lecture 25 Use Case at glance - Predictive Analytics and Credit Risk Modeling

Lecture 0 Use Case at glance - Predictive Analytics and Credit Risk Modeling - Example

Lecture 26 Use Case at glance - Data-Driven Approach of Managing Financial Crime Risks in B

Lecture 27 Use Case at glance - Customer Data Analytics and Personalization

Lecture 28 Additional Lecture: Challenges and Trade-offs in AI adoption

Section 5: Miscellaneous: Data Security, Emerging Technologies and Trends Banking DM

Lecture 29 Data Security and Privacy in Banking

Lecture 30 Data Fabric ("Hybrid Multi-Cloud") & Data Mesh ("Data Products") Architectures

Lecture 31 Cloud Migration at Scale – What are the trade-offs?

Lecture 32 Additional Lecture - Open Banking, API Data Integration & Data Monetzation

Section 6: Conclusive Remarks & Future of Banking Data Management

Lecture 33 Summary - Developing a Data-Driven Strategy in Banking

Lecture 34 Future of Data Management in Banking

Lecture 35 THANK YOU!!!

Banking & finance professionals looking to enhance their data management skills,IT & data professionals working in banking technology or fintech,Students and aspiring professionals looking to enter the data-driven finance sector