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Ernst & Young: Using Tableau for Fraud Analysis
Technology Category
- Analytics & Modeling - Data Mining
- Application Infrastructure & Middleware - Data Visualization
Applicable Industries
- Professional Service
Applicable Functions
- Business Operation
Use Cases
- Fraud Detection
Services
- Data Science Services
The Challenge
Ernst & Young's Fraud Investigation and Dispute Services (FIDS) practice works in a broad spectrum of providing analytical services related to identifying factors related to fraud and forensics. They analyze all types of fraud that can be coming from all types of different data sets, whether or not those data sets are transactional data in form. They also try to uncover elements of unusual patterns or unusual behavioral activity within unstructured data sets of text data – such as emails, instant messages, or SMS text messages that are exchanged in an organization from employee to employee. The challenge was to analyze these large volumes of data and present the results in a way that is easily understandable by a variety of individuals across an organization—from analysts and end users right up to C-suite professionals.
About The Customer
Ernst & Young is a global leader in assurance, tax, transaction, and advisory services. The company has a Fraud Investigation and Dispute Services (FIDS) practice that works in a broad spectrum of providing analytical services related to identifying factors related to fraud and forensics. They also work within investigations, and they work within the anti-bribery and corruption fields as well. The FIDS team analyzes all types of fraud that can be coming from all types of different data sets, whether or not those data sets are transactional data in form. They also try to uncover elements of unusual patterns or unusual behavioral activity within unstructured data sets of text data – such as emails, instant messages, or SMS text messages that are exchanged in an organization from employee to employee.
The Solution
Ernst & Young uses Tableau for data visualization. They perform all of their analytics within SQL and compute fraud scores that will identify errors that need further investigation. Those computations are then fed directly into Tableau. Tableau outputs them very quickly into a visual format, so they’re very easy to understand. For instance, they have four-dimensional charts that help to hone in on individuals that may need further investigation. They connect to different databases directly, which they find to be a very useful feature. It gives them lots of transferability with regards to their underlying data structures. They work with SQL. They work with Oracle. They work with a whole variety of different databases.
Operational Impact
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