Customer Insult Rate
The majority of merchants I work with are happy with their fraud rate. These merchants have strong, layered fraud prevention processes that help prevent fraudulent order approval. However, when I ask “What is your customer insult rate?” the answer is often in some form of “I don’t know.”
To take a step back, let’s define customer insult rate as the false identification of good customers as fraudulent (also known as a “false positive”) or the degradation of service to good customers through friction-filled fraud processes. The Merchant Risk Council’s 2015 Global Fraud Survey estimated that more than 10% of merchants report a false positive rate in excess of 20%. In addition, 37% of merchants do not track false positives. While a single transaction decline may not present a substantial monetary loss, the risk of reduced customer lifetime value can be very substantial. For some industries, this cost can be upwards of 10x the transaction amount. Reducing the false positive rate and maintaining good customers will be main focuses for merchants as consumers continue to shift to online and mobile commerce.
One way to reduce customer insult rate is to integrate the right data attributes into your decision engine within your layered process. Ekata provides data around phone, name, physical address, email, and IP, allowing complete identity verification around five data points in one search. These data points can help provide positive and risk signals for transaction decisioning. In this post, I am going to focus on how address validation helps merchants retain good customers and reduce insult rates.
So how does address validation help merchants reduce customer insult rate and approve more good customers quickly? It allows decision engines and fraud analysts to look at positive and negative signals to reduce the transaction friction for a good customer. The overall goal is to get as many signals to push those good customers through as quickly as possible. Here are some signals our API provides:
- Cross Validation: It is difficult for fraudsters to gain access to all of a customer’s contact data. Here are some address validation cross-checks that Identity Check performs to ensure the transaction ‘story’ makes sense (all of these checks perform fuzzy matching1 ):
- IP Distance from Address
- Risk Signals
- Delivery_Point: Ekata returns whether an address is a commercial mail drop, PObox throwback, PObox, multi-unit, or single-unit. One common fraud technique is to provide a different unit number for a single-unit. Often the merchant will recognize these as unique addresses. Seeing a unit number on a single-unit is a strong positive risk signal.
- Is_deliverable: USPS tells us if the address is deliverable. An address that is not deliverable is a red flag.
- Is_receiving_mail: USPS verifies that the mail will be collected if delivered. A lot of fraudsters will have physical goods delivered to abandoned homes or houses recently foreclosed.
- Usage Type: This flag shows a merchant if the address is “business” or “residential”
- Address standardization/normalization: Normalization of addresses is critical for e-commerce businesses. Fraudsters will make an address look different by using other variations of an address (Court vs. Ct., Northwest vs. NW, etc.).
Ekata can help your business determine what risk signals provide the most lift in your fraud process. Here are the two ways your team can access our data:
- API: We have an easy to integrate, RESTful API with a JSON output. We are fully integrated with the top three fraud platforms in the e-commerce industry: Accertify, CyberSource, and Kount. We also make it easy for your developers to integrate the API into your current systems with our online resources. We have several different API keys available depending upon your data needs.
- Pro Web: Our web interface is a simple and easy to use platform where users can interact and find the data they need. We have an awesome interface for e-commerce merchants that is accessible with a single-click from many ecommerce and fraud platforms.
To help merchants understand how Ekata data can reduce customer insult and improve address validation, we offer complimentary data evaluations. Contact us today to brainstorm some ideas on how we can help solve your current data problems.
1 Fuzzy Matching – A technique to identity matches that are less than a 100% match (ie. Jon and Jonathan).