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.

Programme Curriculum

Banking Data Analytics Training Course

Introduction

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@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 Analytics Banking Insights Predictive Analytics Data Visualization

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

Sep 21 2026

21 Sep — 25 Sep 2026

💻 Online / Virtual session

Online $1,000