Learning Objectives:

  • Define data analytics and its role in banking.

  • Understand the data analytics process: data preparation, modelling, and visualisation.

  • Identify the key competencies needed for data analytics in banking.

  • Apply data analytics to solve banking problems.

2.1 What is Data Analytics in Banking?

Data analytics in banking refers to the process of collecting, processing, and analysing data to gain insights that inform business decisions. The House of Training course defines data analytics as addressing “Issues that Data Analytics can solve” and covers “Concepts of Data Analytics” and “Business applications and processes of Data Analytics” . The IBSL certificate programme focuses on “practical knowledge and skills to analyse, visualise, and interpret banking and financial data using Power BI” .

Key Competencies: The House of Training course identifies “Competencies needed” as a core learning outcome . The IBSL certificate programme covers “Introduction & First Report,” “Data Preparation,” “Data Modelling,” “Banking KPIs,” and “Dashboard Design” .

2.2 The Data Analytics Process

The House of Training course outlines a structured data analytics process: “Data analytics processes – Data preparation, visualisation…” . The IBSL certificate programme follows a similar structure: “Data Preparation,” “Data Modelling,” and “Advanced Analytics” .

Key Steps:

  • Problem Definition: The House of Training course covers “Problem definition for Business applications of Data Analytics” .

  • Data Preparation: The IBSL course covers “Data Preparation” and “Data Modelling” . The NobleProg course covers “Managing data quality, security, and governance” .

  • Data Visualisation: The IBSL course covers “Dashboard Design I” and “Dashboard Design II” . The NobleProg course covers “Exploratory data analysis and visualization” .

  • Analysis and Interpretation: The Amrita course requires students to “Interpret outputs to support data-driven business decisions responsibly” .

2.3 Banking KPIs and Data Analytics

The IBSL certificate programme includes “Banking KPIs” as a core component . The Amrita course covers “Performance measurement in banking and insurance” . The SIBM Nagpur course covers “evaluate the market risk,” “measure credit risk,” and “assess fraud risk” as core learning outcomes .

Key Banking KPIs:

  • Customer Acquisition and Retention: Customer acquisition cost, customer lifetime value, churn rate. The Amrita course covers “Predicting customer churn and retention” .

  • Risk and Asset Quality: Non-performing loan ratio, provision coverage ratio, default rate.

  • Operational Efficiency: Cost-to-income ratio, processing time, error rate.

  • Customer Experience: Net Promoter Score (NPS), customer satisfaction score (CSAT), customer effort score.

2.4 Promises and Pitfalls of Data Analytics

The House of Training course covers “Promises & pitfalls of Data Analytics” . The NUS course covers “AI Ethical Issues, including transparency and black box issues, AI biases, data privacy, societal implications of AI deployment, etc.” . The NobleProg course covers “Ethics and Compliance – Ethical considerations of using Big Data and AI in banking. Navigating compliance and regulatory challenges” .

Key Promises:

  • Efficiency: The Knowledge Academy course notes that AI helps “reduce operational costs, improve accuracy” .

  • Accuracy: AI and machine learning models can detect patterns humans might miss.

  • Personalisation: The Amrita course covers “Data-driven personalization of banking and insurance products” .

Key Pitfalls:

  • Bias: The NUS course covers “AI biases” as a key ethical issue . The NobleProg course covers “Fairness in credit models: bias detection and mitigation” .

  • Data Quality: The NobleProg course covers “Managing data quality, security, and governance” .

  • Explainability: The NUS course covers “transparency and black box issues” .

  • Regulatory Compliance: The NobleProg course covers “Navigating compliance and regulatory challenges” .

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