Banking Data Analytics Training Course
Banking Data Analytics Training Course is designed to equip banking professionals, financial analysts, risk managers, compliance officers, and aspiring data analysts with advanced analytical skills to drive data-driven decision-making in the modern banking ecosystem.
Course Overview
Banking Data Analytics Training Course
Introduction
Banking Data Analytics Training Course is designed to equip banking professionals, financial analysts, risk managers, compliance officers, and aspiring data analysts with advanced analytical skills to drive data-driven decision-making in the modern banking ecosystem. With the rapid digital transformation of the financial services industry, banks increasingly rely on Artificial Intelligence (AI), Machine Learning (ML), Predictive Analytics, Business Intelligence (BI), Big Data, Data Visualization, Customer Analytics, Risk Analytics, Fraud Detection, Credit Scoring, Regulatory Reporting, and Financial Data Modeling to improve operational efficiency and customer experience. This training provides participants with practical exposure to banking datasets, real-world case studies, and analytical tools such as Python, SQL, Power BI, Tableau, Excel, and Cloud Analytics Platforms, enabling them to transform raw financial data into actionable business insights.
The course integrates Digital Banking Analytics, FinTech Analytics, Customer Segmentation, AML Analytics, Credit Risk Modeling, Basel Compliance, ESG Analytics, Financial Forecasting, KPI Dashboards, Process Automation, Robotic Process Automation (RPA), Data Governance, and Real-Time Banking Intelligence into a structured learning pathway. Participants will develop expertise in extracting, cleaning, analyzing, visualizing, and interpreting banking data while applying advanced statistical techniques and predictive models to solve complex banking challenges. Through hands-on labs, industry projects, simulation exercises, and banking case studies, learners gain the practical competencies required to support strategic decision-making, optimize banking operations, reduce financial risks, and accelerate digital transformation initiatives across commercial, retail, corporate, and investment banking environments.
Course Duration
5 days
Course Objectives
Upon successful completion of this course, participants will be able to:
- Understand the fundamentals of Banking Data Analytics and digital banking transformation.
- Apply Python, SQL, and Advanced Excel for banking data analysis.
- Develop Interactive Power BI and Tableau Dashboards for executive reporting.
- Perform Customer Analytics using segmentation and behavioral analysis techniques.
- Build Predictive Analytics Models for customer retention and loan performance.
- Analyze Credit Risk, Market Risk, and Operational Risk using analytical frameworks.
- Implement Fraud Detection Analytics using AI and Machine Learning algorithms.
- Perform AML (Anti-Money Laundering) Analytics and suspicious transaction monitoring.
- Create Financial Forecasting Models using time series analysis and predictive modeling.
- Apply Big Data Analytics techniques for high-volume banking transactions.
- Design Regulatory Reporting Dashboards supporting Basel III, IFRS 9, and compliance requirements.
- Implement Data Governance, Data Quality Management, and Data Security best practices.
- Develop end-to-end Banking Business Intelligence Solutions that support strategic decision-making.
Target Audience
- Banking Professionals
- Financial Analysts
- Risk Management Professionals
- Credit Analysts
- Compliance and AML Officers
- Internal Auditors
- Business Intelligence and Data Analysts
- FinTech Professionals and Banking Technology Consultants
Course Modules
Module 1: Banking Industry & Data Analytics Fundamentals
- Banking ecosystem and digital transformation
- Banking products and financial services analytics
- Banking KPIs and performance measurement
- Banking data sources and data lifecycle
- Introduction to banking business intelligence
- Case Study: Developing KPI dashboards for retail banking performance.
Module 2: Data Management for Banking
- Data collection and integration
- SQL for banking databases
- Data cleansing and preprocessing
- Data quality management
- Master Data Management (MDM)
- Case Study: Cleaning customer transaction datasets for analytical reporting.
Module 3: Banking Analytics using Excel, Python & SQL
- Advanced Excel analytics
- Python for banking data analysis
- SQL querying and optimization
- Statistical analysis techniques
- Data automation and reporting
- Case Study: Customer profitability analysis using Python and SQL.
Module 4: Business Intelligence & Data Visualization
- Power BI dashboards
- Tableau visualization
- Interactive banking reports
- Executive KPI scorecards
- Real-time analytics dashboards
- Case Study: Executive banking dashboard for branch performance monitoring.
Module 5: Customer Analytics & Predictive Modeling
- Customer segmentation
- Customer lifetime value (CLV)
- Churn prediction
- Cross-selling analytics
- Predictive customer behavior models
- Case Study: Predicting customer attrition using Machine Learning.
Module 6: Risk Analytics & Fraud Detection
- Credit risk analytics
- Market risk measurement
- Operational risk analytics
- Fraud detection models
- AI-driven anomaly detection
- Case Study: Credit card fraud detection using Machine Learning algorithms.
Module 7: Compliance Analytics & Regulatory Reporting
- AML transaction monitoring
- Basel III analytics
- IFRS 9 reporting
- Regulatory compliance dashboards
- Data governance and security
- Case Study: AML monitoring system for suspicious transaction reporting.
Module 8: Advanced Banking Analytics & Capstone Project
- Big Data analytics in banking
- Cloud analytics platforms
- AI and Generative AI applications in banking
- End-to-end banking analytics project
- Presentation of analytical findings
- Case Study: Building an enterprise banking analytics solution integrating customer, risk, compliance, and financial performance dashboards.
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.