Banking Analytics with Power BI Training Course
Banking Analytics with Power BI Training Course is designed to equip banking professionals, analysts, finance experts, and data-driven decision makers with advanced skills in Business Intelligence (BI), Data Analytics, Financial Reporting, Risk Analytics, Customer Insights, and Dashboard Automation.
Course Overview
Banking Analytics with Power BI Training Course
Introduction
Banking Analytics with Power BI Training Course is designed to equip banking professionals, analysts, finance experts, and data-driven decision makers with advanced skills in Business Intelligence (BI), Data Analytics, Financial Reporting, Risk Analytics, Customer Insights, and Dashboard Automation. The course focuses on transforming complex banking data into meaningful insights using Microsoft Power BI, Data Visualization, Predictive Analytics, KPI Monitoring, and Interactive Executive Dashboards. Participants will learn how to leverage analytics for credit risk management, fraud detection, customer segmentation, loan portfolio analysis, and regulatory reporting in modern digital banking environments.
With the rapid adoption of Artificial Intelligence (AI), Machine Learning (ML), FinTech innovation, cloud analytics, and data-driven banking strategies, financial institutions require professionals who can convert data into strategic business decisions. This training provides hands-on experience through real-world banking datasets, industry case studies, and practical Power BI projects covering retail banking analytics, corporate banking performance, digital banking metrics, compliance analytics, and financial risk intelligence.
Course Duration
5 days
Course Objectives
By completing this Banking Analytics with Power BI Training, participants will be able to:
- Develop advanced banking dashboards and interactive financial reports using Power BI.
- Analyze banking performance using Business Intelligence and Data Analytics frameworks.
- Build automated financial KPI dashboards for executive decision-making.
- Perform customer segmentation and behavioral analytics for banking growth strategies.
- Apply Power BI techniques for credit risk assessment and loan portfolio analytics.
- Create fraud monitoring solutions using banking fraud analytics and anomaly detection insights.
- Design regulatory reporting dashboards supporting financial compliance and governance.
- Integrate banking data sources using ETL processes, Power Query, and data modeling techniques.
- Apply DAX calculations and advanced analytics functions for financial intelligence.
- Analyze profitability through customer lifetime value and revenue analytics.
- Implement data visualization best practices for executive banking reporting.
- Understand emerging trends in AI-powered banking analytics and digital transformation.
- Develop industry-ready analytical solutions using Power BI banking case studies and real-world scenarios.
Target Audience
- Banking analysts and financial analysts
- Business intelligence professionals
- Data analysts working in financial services
- Banking managers and decision makers
- Risk management professionals
- Credit analysts and loan officers
- Finance and accounting professionals
- FinTech professionals and aspiring data specialists
Course Modules
Module 1: Introduction to Banking Analytics & Power BI
- Fundamentals of banking data analytics and financial intelligence
- Overview of Power BI ecosystem and banking applications
- Understanding banking KPIs, metrics, and performance indicators
- Data-driven decision-making in modern financial institutions
- Introduction to banking analytics case studies
- Case Study: Analyzing retail bank performance using customer accounts, deposits, and transaction datasets.
Module 2: Banking Data Preparation Using Power Query
- Importing banking datasets from multiple data sources
- Data cleansing and transformation techniques
- Handling missing values and inconsistent financial records
- Creating automated data preparation workflows
- Building reliable banking analytics data pipelines
- Case Study: Preparing customer transaction data for a digital banking analytics dashboard.
Module 3: Data Modeling for Financial Analytics
- Designing banking data models using Power BI
- Creating relationships between customer, account, and transaction tables
- Implementing star schema architecture
- Optimizing banking analytics data models
- Managing large-scale financial datasets
- Case Study: Developing a banking customer 360-degree analytics model.
Module 4: DAX for Banking Intelligence
- Creating calculated measures using DAX
- Developing financial calculations and KPIs
- Performing time intelligence analysis
- Building profitability and revenue measures
- Advanced DAX techniques for banking dashboards
- Case Study: Calculating loan growth, customer profitability, and monthly banking performance trends.
Module 5: Banking Performance Dashboards
- Designing executive banking dashboards
- Creating interactive charts and financial visualizations
- Tracking deposits, loans, revenue, and customer growth
- Developing management reporting solutions
- Applying dashboard storytelling techniques
- Case Study: Building an executive dashboard for branch performance monitoring.
Module 6: Risk Analytics and Fraud Detection
- Understanding credit risk analytics concepts
- Developing loan portfolio monitoring dashboards
- Identifying fraud patterns through analytics
- Creating risk scoring reports
- Supporting compliance and regulatory analytics
- Case Study: Fraud detection dashboard analyzing suspicious transactions and customer behavior.
Module 7: Customer Analytics and Digital Banking Insights
- Customer segmentation using analytics techniques
- Analyzing customer behavior and engagement
- Measuring digital banking adoption
- Creating customer retention dashboards
- Improving banking services through insights
- Case Study: Customer churn analysis for improving digital banking customer retention.
Module 8: Advanced Banking Analytics & Future Trends
- Applying AI and machine learning concepts in banking analytics
- Exploring predictive analytics applications
- Understanding cloud-based banking intelligence
- Implementing automation in financial reporting
- Building industry-ready Power BI banking projects
- Case Study: Predicting customer loan risks using advanced analytics approaches.
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@datastatresearch.org or call +254724527104
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 DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.