This Accelerator for Return Fraud Monitoring leverages retail sales and returns data to monitor returns activity across all products and all store locations to identify potential fraud. The dashboard uses historical trends to predict return activity and clearly identifies areas that fall outside of those expected values. Actionable data provides managers with the insights that they need to take corrective action to prevent additional losses and ensure employee compliance.

[Connect with Cleartelligence to learn more.](https://www.cleartelligence.com/tableau-aceelerator-return-fraud-monitoring)

### Answer Key Business Questions
- What were the actual returns in a given period, and how does that compare to the expected returns? 
- Are there excess returns, which could signify fraud, occurring in any single store location or group of locations? 
- Are excess returns more prevalent in certain products or groups of products? 
- For which days and times of the week are returns most common? 
- Which associates are processing the most returns? 
- What proportion of the returns are confirmed as invalid (open box fraud) or potentially invalid (returned without receipt)?

### Monitor and Improve KPIs
- Total Sales 
- Total Returns 
- Expected Returns 
- Excess Returns 
- Excess Returns by Location 
- Excess Returns by Product Category 
- Excess Returns by Product 
- Distribution of Returns by Time & Day 
- Distribution of Returns by Associate 
- Percentage of Returns by Return Type

### Required Data Attributes
- Associate Number (String) 
- Product Category (String) 
- Product ID (String) 
- Product Name (String) 
- Product Sub-Category 
- Return Status (String) 
- Store ID (String) 
- Store Location (String) 
- Time (Time) 
- Transaction Date (DateTime) 
- Transaction Type (String) 
- TransactionID (Integer) 
- Return Quantity (Integer) 
- Sales (Decimal) 
- Sales Quantity (Integer)

### Build Your Data Source
The data source for the Return Fraud Monitoring analysis is comprised of a single transactional table with all purchases and returns from a retailer’s operational data store. Records are aggregated by transaction and product, so for each product in a single transaction, there is one row of data.

You’ll need to query the appropriate schema in your own data store in order to analyse these KPIs for your own business.

Returns and Purchases are identified by a field named TransactionType. 

Other attributes included in the data source include the Transaction ID, Product ID, Product Name, Product Category, Product Segment, Store ID, Store Location, Transaction Date, Transaction Period, Customer ID, Customer Name, Order ID and Store Associate. 
There are two measures unique to Returns: Return Status and Return Quantity. There are two measures unique to Sales: Sales Quantity and Sales.

There is a secondary table, with a relationship set on Period, that includes total hours worked by Store Associate by Period.


Return Fraud Monitoring

Monitors retail organisation’s returns for fraud