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Connected Customer Experience

Setting Standards: Demystifying Warranty Fraud

Author Bruce Burke on May-16-2014

M-W.com defines the word Standard as; “something set up and established by authority as a rule for the measure of quantity, weight, extent, value, or quality.”

As an example; a standard that has been set up and closely followed by companies and consumers alike is the credit scoring scale. In this scale a single number summarizes a person or entity’s credit history, this method of measure is universally accepted everywhere.

Individual credit bureaus use a different name for their score, even if it uses the same algorithm as the other reporting bureaus to generate the score. A similar model may be used by the warranty industry to manage risk.

Detecting and Preventing Warranty Fraud Using Predictive Analytics

During the most recent mize sponsored webinar; Detecting and Preventing Warranty Fraud Using Predictive Analytics, CEO, Ashok Kartham spoke of a new initiative aimed at establishing a scoring model and methodology by which to measure fraud related to warranty claims.

In today’s society everything from the amount of hours you sleep, to your social media standing is being quantified. The warranty fraud scoring mechanism is a new idea, whose time has come.

Scoring is a way to streamline the claims process by providing enterprise organizations a simple way to quickly and easily discern if a claim is suspect, is an anomaly, or has a high risk potential.

To assess you need two scores; first the Service Provider Risk Score (SPRS) and secondly a Claim Anomaly Score (CAS). Scoring is a simple 1–100 numbering - the higher the score, the more likelihood that fraud is involved.

Scoring warranty claims fraud 1 to 100

A service provider's risk score is based on analyzing and benchmarking dealers on many factors like costs, frequency, repair profiles, parts/labor, etc. The score is also impacted by how many times claims were manually adjusted or rejected in the recent past.

The claim anomaly score is used by manufacturers based on past claim patterns and audits in new claims. Additionally, you’ll want to know the reasoning behind the score and contributing factors.

Even though the scoring is simple, the methodology used to assign the score must be robust. It is complex behind the scenes, but it is important to simplify fraud scoring for warranty groups. Scoring provides a super simple decision framework which may be adopted by any organization.

Service Provider Risk Score & Claim Anomaly Score (SPRS & CAS)

Actual thresholds may be different for each organization; however you can decide to audit a dealer, put them on a watch list, or require more information based on the resulting score. On the other hand it is important to demonstrate incentives for lower risk scores by providing faster payment or better margin on warranty services.

mize is currently enrolling companies in a pilot program meant to begin setting this standard. The mize Warranty Anomaly & Risk (WAR) pilot is being created around the idea of importing and analyzing an organization’s entire history of warranty claim data.

Thereby creating a benchmark the organization can begin working from. As more data is becomes available it’s continually assessed and refined resulting in purer and purer outcomes.

The pilot is setup using a cloud-based predictive analytics engine; there is no need to change existing business processes. The analytics are integrated with the companies’ current infrastructure and routines by providing a layer of services that floats above the existing processes kind of like … well – a cloud.

Traditional methods are augmented with predictive analytics - without upsetting existing model or processes

This eliminates the need for investment in expensive new infrastructure hardware, and doesn't call for any new skill sets or training to deploy these new capabilities.  

Groups such as the Global Warranty and Service Contract Association (GWSCA) can help bring more consistency to this new methodology, resulting in an industry standard. Because it is a standard methodology it can be benchmarked within the industry, or even across other related industries.

We would like to invite all relevant associations, vendors and groups to participate in setting this scoring model and associated methodology which would help warranty divisions to become standardized, vastly reducing and eradicating fraud.

To ensure consistency the predictive analytics cloud and scoring methodology is initially being piloted with a single major manufacturer. mize is currently considering additional companies for a second phase pilot which will be limited to five total companies.

We would like to participate in the pilot program

If your organization would like to be considered for participation in the mize WAR pilot program please feel free to contact us for details about this opportunity to combat fraud.

Topics: analytic, warranty, fraud, claims, scoring, predictive, m-ize

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