Data Analytics in Banking & Finance (Risk & Fraud Focus)

Why Choose Garranto Academy for Data Analytics in Banking & Finance Training?

Garranto Academy delivers a focused, half-day awareness programme that demystifies data analytics for banking professionals who work with risk, fraud, and compliance outputs—without requiring any technical background. The course translates complex concepts like big data, machine learning, and risk analytics into clear, practical knowledge directly relevant to financial crime prevention, credit risk, and regulatory oversight. Participants leave with the vocabulary and conceptual understanding needed to engage confidently with data teams, interpret analytical reports, and recognise how analytics strengthens the first line of defence in modern banking.

Course Overview:

The Data Analytics in Banking & Finance (Risk & Fraud Focus) programme is a half-day, instructor-led foundation course designed for banking operations, risk, fraud, compliance, and audit professionals. It provides a structured introduction to digital transformation, the objectives and types of data analytics, key data sources, big data characteristics, unstructured data challenges, analytics tools, machine learning concepts, and the application of risk analytics in credit, operational, and fraud risk management. Entirely non-technical, the course focuses on building conceptual clarity so participants can understand and apply data-driven insights in their daily risk and control responsibilities.

What You'll Learn in Our Data Analytics in Banking & Finance (Risk & Fraud Focus) Course?

Course Objectives:

Upon successful completion of this course, learners will be able to:

• ✓Understand the role of digital transformation and emerging technologies in banking and finance.

• ✓Explain the objectives and types of data analytics used in financial institutions.

• ✓Recognise the business, risk, and compliance benefits of data analytics.

• ✓Identify key sources and types of data used in banking analytics.

• ✓Understand big data concepts, including the 5 V’s and unstructured data.

• ✓Gain awareness of analytics tools and machine learning concepts.

• ✓Appreciate how risk analytics supports fraud detection and risk mitigation.

Prerequisites

• ✓Basic understanding of banking operations and terminology.

• ✓Familiarity with common banking processes and controls.

• ✓General computer literacy (email, documents, spreadsheets).

• ✓No prior knowledge of data analytics, statistics, or programming is required.

Course Outlines:

Module 1 — Digital Transformation, Emerging Technologies and Use of Data Analytics in Banking & Finance

• ✓Key Concepts:

• ✓Overview of digital transformation in banking

• ✓Emerging technologies shaping financial services

• ✓Role of data analytics in modern banking operations

• ✓Link between digitalisation, data availability, and regulatory expectations

Module 2 — Objectives and Types of Data Analytics

• ✓Key Concepts:

• ✓Purpose of data analytics in banking

• ✓Overview of analytics types (descriptive, diagnostic, predictive, prescriptive)

• ✓High-level banking use cases across functions

• ✓Benefits of data analytics: enhanced fraud detection and prevention, improved risk management

Module 3 — Sources of Data for Data Analytics

• ✓Key Concepts:

• ✓Internal banking data sources: core systems, transaction logs, customer records

• ✓Transactional and operational data

• ✓Customer and behavioural data

• ✓External and third-party data sources: credit bureaus, watchlists, market data

Module 4 — Big Data Concepts – The 5 V’s of Big Data

• ✓Key Concepts:

• ✓Explanation of Volume, Velocity, Variety, Veracity, and Value

• ✓Why traditional systems struggle with big data

• ✓Importance of big data in banking and fraud analytics

• ✓Unstructured Data: challenges in analysing unstructured data; examples in banking (emails, social media, call transcripts)

Module 5 — Tools for Data Analytics

• ✓Key Concepts:

• ✓Overview of analytics tools used in banking (traditional vs modern platforms)

• ✓Matching tools to data types and use cases

• ✓Awareness of dashboards, reporting tools, and advanced analytics environments

Module 6 — Big Data and Machine Learning

• ✓Key Concepts:

• ✓Introduction to machine learning concepts (supervised, unsupervised, anomaly detection)

• ✓Relationship between big data and machine learning

• ✓Use of machine learning in fraud detection and anomaly identification; practical banking examples

Module 7 — Risk Analytics in Banking

• ✓Key Concepts:

• ✓Role of risk analytics in banking institutions

• ✓Analytics applications in credit risk, operational risk, and fraud risk

• ✓Importance of analytics in strengthening internal controls, governance, and regulatory compliance

Course Outcomes:

Upon completing the "Data Analytics in Banking & Finance (Risk & Fraud Focus)" course, participants will be able to:

• ✓Describe how digital transformation and data analytics are applied in banking.

• ✓Understand key analytics concepts relevant to risk and fraud management.

• ✓Identify different data sources and data types used in banking analytics.

• ✓Explain the characteristics and challenges of big data and unstructured data.

• ✓Recognise the role of machine learning in modern banking analytics.

• ✓Appreciate how risk analytics enhances fraud prevention and regulatory oversight.

Key Benefits of Learning Data Analytics in Banking & Finance (Risk & Fraud Focus)

Bridge the gap between business and data in just half a day. This course equips you with the foundational knowledge to understand what data analytics can do for risk and fraud management, how to interpret analytical outputs, and where to apply insights in your daily control and compliance work. You’ll gain the confidence to collaborate with data teams and contribute to a more proactive, intelligence-led defence against financial crime.

How Data Analytics Awareness Transforms Banking Risk and Fraud Operations

When risk, fraud, and compliance professionals understand the basics of data analytics, they move from passive report consumers to informed partners in digital transformation. This awareness enables them to ask better questions, identify new data-driven detection opportunities, and support the adoption of machine learning and big data tools that can catch fraud faster, assess credit risk more accurately, and ensure regulatory requirements are met with robust, data-backed evidence.