By Jean-Stéphane Faubert
Toronto, ON (Dec. 8, 2014) – A fundamental piece of information in the context of auto repair claim servicing is the difference between the total cost of a claim and its original estimated value. It is known as the Efficiency Ratio. When seeking to understand the factors involved in this fundamental ratio in order to improve performance, three claim management categories of analytics stand out for scrutiny: appraisers, body shops, and parts & labor. What can be expected from focusing on these categories is an overall improvement in terms of performance that translates into lowered costs, improved relationships, and improved services.
Appraiser Performance Analytics category
Claim managers need timely information to assess the overall performance of all appraisers, either internal employees, external contractors, or body shop employees. Key measures in this category include the time required to complete an estimate, the number of supplements and/or revisions required to the original estimates, and the distribution of estimates amongst regular and preferred body shops. With this information in their possession, claim managers can easily make better-informed decisions and improve appraiser-related activities.
Body Shop Performance Analytics category
Claim managers know that there is always room for improving the relationship with body shops. In this context, the accurate measurement of body shop performance from different angles of analysis is key. For example, the OEM to generic part ratio can be used to control costs. Also, the average length of time required to complete an estimate can be used to compare the efficiency of different body shops. Similarly, knowing the percentage of payments processed electronically can be leveraged to reduce operational costs. Key metrics such as these can, not only equip claim mangers towards improving relationships and the bottom line, but also be used to incentivize body shops with performance recognition programs.
Repair Parts & Labor Analytics category
The Repair Parts & Labor category has an obvious and direct focus on claim related costs in general as well as on the Efficiency Ratio in particular. This Analytics category provides insight related to the efficiency of repair activities, and equips claim managers towards the direct supervision of the kinds of parts being used for repairs. Two particular Repair Parts & Labor metrics relevant for improving profitability stand out. First, the average number of labor hours spent compared to the original estimates and to the actuals reported for similar incidents. Second, the ratio of OEM to generic parts declared in the estimates provided by the body shops. Considering claims and estimates at the parts & labor level of detail gives claim managers the means to influence the efficiency of the most fundamental claim repair processes and activities.
What has been described previously can be achieved through the innovative integration of data from two fundamental sources: body shop management transactions and claim management transactions. This data needs to be uploaded into an Analytics application that processes the most valuable information and presents it in the most useful form.
The successful integration and presentation of data elements from these two sources must meet some criteria. All relevant data elements must be brought into the Analytics solution from an insurer’s operational systems and/or its Business Intelligence environment, as well as from an external body shop management feed. The data elements should conform to a standard set by a Generic Data Interface (GDI) through which it must pass as it gets uploaded into the Analytics solution. The cost effectiveness of such a solution must be ensured in part by the delivery of pre-packaged reports, dashboards, and KPIs addressing the most pressing needs of claim managers. Ultimately as well, there should be no surprises in store for the target user community: for optimal efficiency, the Analytics solution should allow claim managers to leverage the analytical toolsets they are already familiar with.
With an Auto Repair Claim Analytics solution that meets these criteria, claim managers will have the tools they need to optimize a crucial component of the insurance value chain — the auto repair claim servicing process — while at the same time lowering costs and improving services.
For more details on how the right Analytics solution can improve claim processing performance, please read the InEdge White Paper, Optimizing the Claim Servicing Process with Auto Repair Claims Analytics (PDF).
About the Author
Jean-Stephane Faubert is a Senior Solutions Architect at InEdge. He has more than 20 years of experience at designing and implementing information technology solutions. For more than half of his career, his focus has been on the design and implementation of Analytics solutions. Over the years, Jean-Stephane has designed Analytics solutions for clients in several industries, such as Insurance, Banking, Brokerage, Health, and others. His expertise covers the whole spectrum of Analytics project-specific tasks ranging from strategic planning, solutions architecture, project management and assessment, all the way down to data modeling and detailed design.
InEdge is a leader in Insurance Analytics solutions. Experienced at quickly leveraging data, InEdge seamlessly and powerfully creates business advantage for its clients. Since its creation in 1994, InEdge has designed and implemented some of the most sophisticated analytical applications available today. Our clients add up to an impressive roster of Property & Casualty and Life Insurance companies. Our Analytics solutions improve and make easier decision-making at all levels for our clients.