Warranty Analytics

Supercharge Your Program

We deliver warranty analytics insights, cutting through the complexity of multiple systems, multiple vendors, and multiple data formats to determine the real impact of your warranty program.

Reserves & Fees

Accurately Allocate Reserves

We target 96%+ predictive accuracy every quarter.

Maximize Reserve Recognition

Right-Size Premiums

Optimize Customer Pricing

Create Negotiating Leverage

Minimize Downside

Reduce Risk

Reliability

Predict Product Failures

Our models reveal emerging reliability issues.

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Early Detection of Reliability Issues

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Pinpoint Specific Problems

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Improve Quality

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Identify Supplier Issues

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Optimize Parts Inventory

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Increase Customer Satisfaction

Cost Control

Reduce Warranty Costs

Our real-time scoring algorithms identify suspicious claims before payments are made.

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Uncover Fraud and "Over-Repair"

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Detect Anomalies in Service

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Develop Servicer Scorecards

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Close Policy Loopholes

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Improve Handling Procedures

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Prioritize Claims for Manual Review

20%

Potential Underwriting Adjustment

15%

Potential Increased Program Revenue

10%

Potential Targeted Cost Savings

Related Content

Warranty Analytics is the Key to Detecting and Preventing Dealer and Servicer Fraud

In this post, we provide a thorough analysis of the mismanagement and fraud that can occur as part of warranty claims, something that can amount to a significant cost if left unchecked. We look at how to detect irregular patterns in claims data and how these analytics can provide mechanisms to improve training and monitoring. You’ll walk away with a better understanding of how data and analytics are the key to detecting and correcting fraud.

A Brief History of Predictive Analytics – Part 3

In this third installment of the history of analytics, we look at developments over the past twenty years – phenomena like big data, Google search, social networks and other trends that have grown data to unprecedented volumes, making good analytics more vital than ever.

A Brief History of Predictive Analytics – Part 2

In this second installment of the (brief) history of business analytics, we cover the 1960’s through the 1990’s, which saw the evolution of mainframe computing, relational databases, and data warehousing into the business mainstream. We talk about the evolution of computing systems from processing and storage of transactional data to multi-facted, connected information about customer, sales, and other dimensions over time.

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