This course teaches students the fundamentals of data analytics, and how to use that skill set to analyse transaction and customer data to identify fraudulent or malicious activity.  Whilst the skills learnt in this course can be applied to any database/analysis software, the afternoon is taught in either Microsoft Excel or SQL.

Objectives

By the end of this course students will:

• Understand the core analytical methodologies for identifying fraud
• Understand how to identify trends and anomalies
• Have a practical understanding of how to conduct tests on different data sets
• Understand how to put together a professional standard report of their findings 

Morning – Theory and understanding

The morning session gives students an understanding of the basics of data analytics, how it can be used in combatting fraud and how to identify common fraud trends. This session also gives students a practical insight into application and third party fraud, and how that may appear in data.

Session One (09:30 – 10:00)
Introduction

• Domestics
• Programme outline
• Icebreaker – getting to know you

Session Two (10:00 – 11:00)
Theory and foundation learning

• Understanding data analytics
• Understanding where data analytics fits in countering fraud
• Understanding application fraud and third party (account takeover) fraud

Session Three (11:00 – 12:00)
Trends

• Identifying types of fraudulent activity
• Understanding trends in customer account/transaction data
• Developing hypotheses and test scenarios

Lunch (12:00 – 12:30)

Afternoon – Practical*

The afternoon session is split into two parts; the first looks at how to practically work with a dataset, run a number of tests/validations and avoid flagging false positives.  The second part is a group practical exercise going through a ‘real world’ scenario.

Session Four (12:30 – 15:00)
Data analytics in practice

• Methodologies for working with data sets (either in Excel or SQL)
• Finding outliers and anomalies
• Understanding statistical relevance and avoiding false positives

Session Five (15:00 – 17:00)
Practical*

• A guided group exercise to understand the practicalities of finding fraudulent activity in a data set
• Guidance on how to produce an easy to understand report of findings

Session Six (17:00 – 18:00)
Close

• Knowledge check
• Guidance on further learning and development
• Close

The course itself is very interactive – it is based on practical and usable methodologies which students will be able to apply immediately.  The data sets used in training try to be as general as possible, to cover a wide variety of scenarios.

Whilst no specialist computer skills are required, students should have a basic knowledge of Microsoft Excel or a similar database product.

*The practical exercise can be tailored to the requirements/industry of the wider group.