Introduction

Banking Data Quality Management Training is designed to help banking and financial services organizations build trusted, accurate, secure, and regulatory-compliant data ecosystems. With the rapid adoption of Digital Banking, Artificial Intelligence (AI), Machine Learning (ML), Open Banking, Cloud Banking, and Real-Time Analytics, maintaining high-quality financial data has become a strategic priority. This course equips professionals with advanced knowledge of Data Governance, Data Quality Frameworks, Master Data Management (MDM), Data Validation, Data Lineage, Metadata Management, Regulatory Reporting, and Risk-Based Data Controls to improve operational efficiency and customer experience.

The program focuses on practical approaches for implementing enterprise data quality strategies, resolving data issues, improving customer and transaction data accuracy, and supporting compliance requirements such as Basel regulations, AML/KYC standards, GDPR principles, and financial risk management frameworks. Participants learn industry best practices through real-world banking scenarios, case studies, and hands-on exercises that enable them to transform poor-quality data into a valuable business asset

Programme Curriculum

Banking Data Quality Management Training Course

Introduction

Course Duration

5 days

Course Objectives

By completing this Banking Data Quality Management Training, participants will be able to:

Understand the fundamentals of Banking Data Quality Management and Enterprise Data Governance.
Develop effective Data Quality Frameworks aligned with banking business objectives.
Implement Data Governance Models for financial institutions.
Apply advanced Data Profiling and Data Assessment Techniques.
Design automated Data Validation and Data Cleansing Strategies.
Improve Customer Data Quality Management (CDQM) practices.
Manage Master Data Management (MDM) initiatives in banking environments.
Establish strong Metadata Management and Data Lineage Controls.
Build data quality scorecards using Data Quality Metrics and KPIs.
Support Regulatory Compliance and Risk Management Reporting.
Use modern technologies including AI-driven Data Quality Analytics and Automation.
Implement continuous improvement strategies using Data Quality Monitoring Frameworks.
Create a sustainable Data Excellence Culture across banking organizations.

Target Audience

Banking Data Management Professionals
Data Governance Managers and Officers
Chief Data Officers (CDOs) and Data Strategy Leaders
Banking Risk and Compliance Professionals
Business Intelligence and Analytics Professionals
Database Administrators and Data Engineers
Digital Transformation and IT Managers
Financial Services Consultants and Auditors

Course Modules

Module 1: Fundamentals of Banking Data Quality Management

Introduction to Banking Data Quality Concepts and Principles
Importance of high-quality data in Digital Banking Transformation
Data quality dimensions: accuracy, completeness, consistency, validity, and timeliness
Role of data quality in customer experience and operational excellence
Case Study: Improving customer data accuracy in a global retail bank

Module 2: Banking Data Governance Frameworks

Designing an effective Enterprise Data Governance Strategy
Data ownership, stewardship, and accountability models
Creating banking data governance policies and standards
Aligning governance with regulatory requirements
Case Study: Implementing data governance at a multinational financial institution

Module 3: Data Profiling and Data Quality Assessment

Techniques for identifying banking data quality issues
Data profiling methods for customer, transaction, and account data
Detecting duplicate, missing, and inconsistent records
Establishing data quality assessment frameworks
Case Study: Reducing duplicate customer records through profiling analytics

Module 4: Data Cleansing and Data Remediation Strategies

Data cleansing methodologies for financial databases
Designing data correction and remediation workflows
Improving customer and transaction master data
Automation of data quality improvement processes
Case Study: Resolving inaccurate customer information affecting AML compliance

Module 5: Master Data Management (MDM) in Banking

Fundamentals of Banking Master Data Management
Customer, product, and account master data management
Creating a single customer view
MDM integration with CRM and core banking systems
Case Study: Building a unified customer data platform for digital banking

Module 6: Metadata Management and Data Lineage

Understanding metadata-driven data management
Creating end-to-end banking data lineage models
Tracking data movement across banking applications
Supporting audit, compliance, and reporting requirements
Case Study: Improving regulatory reporting accuracy through data lineage

Module 7: AI, Automation, and Advanced Data Quality Analytics

Applying Artificial Intelligence in data quality monitoring
Machine Learning approaches for anomaly detection
Automated data quality rules and alerts
Predictive analytics for identifying data risks
Case Study: Using AI-powered monitoring to detect fraudulent transaction data patterns

Module 8: Regulatory Compliance and Data Quality Excellence

Data quality requirements for banking regulations
Supporting AML, KYC, Basel, and risk reporting processes
Building continuous data quality monitoring frameworks
Developing enterprise data quality dashboards
Case Study: Creating a regulatory-ready data quality operating model

Training Methodology

Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Data Quality Management Data Governance Data Profiling Regulatory Compliance

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 26 2026

26 Oct — 30 Oct 2026

💻 Online / Virtual session

Online $1,000