What the training covers

 

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.