Banking Analytics Practice Exam
Banking Analytics Practice Exam
About Banking Analytics Exam
The Banking Analytics Exam assesses your ability to interpret, analyze, and leverage financial and customer data within the banking sector. It is designed for professionals aiming to apply data-driven strategies to improve decision-making, enhance customer experience, and drive innovation in banking services.
Who should take the Exam?
This exam is ideal for:
- Banking and finance professionals interested in analytics
- Data analysts and data scientists working in financial institutions
- IT and BI professionals transitioning to banking analytics roles
- Bank managers and product heads looking to leverage data insights
- Students and graduates specializing in finance, analytics, or data science
Skills Required
- Strong analytical and critical thinking skills
- Knowledge of banking operations and financial metrics
- Experience with data tools like Excel, SQL, Python, or R
- Familiarity with BI platforms such as Power BI or Tableau
- Understanding of risk modeling, customer segmentation, and KPIs
Knowledge Gained
- Core concepts of banking analytics and business intelligence
- Techniques for analyzing financial performance and credit risk
- Customer behavior analytics and segmentation strategies
- Fraud detection and predictive modeling in banking
- Use of dashboards and data visualization tools
- Data governance and regulatory compliance in financial analytics
Course Outline
The Banking Analytics Exam covers the following topics -
Domain 1 – Fundamentals of Banking Analytics
- Definition, importance, and scope of analytics in banking
- Types of data used in banking (structured, unstructured)
- Key metrics and KPIs in banking operations
Domain 2 – Data Tools and Technologies
- Excel, SQL, Python, and R for data analysis
- Overview of BI tools: Tableau, Power BI, QlikView
- Database management and data warehousing
Domain 3 – Financial and Credit Analytics
- Analyzing balance sheets, income statements, and ratios
- Credit scoring models and loan risk analysis
- Stress testing and capital adequacy analytics
Domain 4 – Customer and Marketing Analytics
- Customer segmentation and behavior analysis
- Campaign performance tracking and ROI
- Churn prediction and customer lifetime value (CLV)
Domain 5 – Fraud and Risk Analytics
- Fraud detection techniques using data science
- Risk modeling and early warning systems
- Anti-money laundering (AML) analytics
Domain 6 – Regulatory and Ethical Considerations
- Data privacy and compliance (GDPR, RBI guidelines)
- Ethics in data usage and model transparency
- Audit trails and data governance practices
