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Auto claims automation: streamlining solutions amid increasing complexity and stakeholder demand

Auto claims automation

The world is speeding up. Over the past 20 years, we’ve moved from clunky desktops and fax machines to slim smartphones and high fidelity video communication. Yet for many automotive insurers, cycle time has remained virtually unchanged1.

Tackling this efficiency issue should be on the minds of any insurer’s information technology department. As the world’s collective technology continues to accelerate, stakeholder expectations will place exponentially more pressure on this problem.


 

Begin the auto claims automation deployment with components that have minimal data dependencies

Unchanged claims cycles

Existing claims processing systems are necessarily complex; there are no silver bullet solutions that can overhaul the entire ecosystem. The key to successful implementation lies in selectively redesigning or streamlining processes instead of overhauling everything at once.

Understanding which components of your system have the least complex (or non-existent) data dependencies is a solid starting foundation.

As an example, moving to a paperless solution for total loss settlement can cut cycle time by up to four days, reduce call volume and increase the incidence of first-time settlement2. Since this technology is more of a shift in data transfer versus information processing the data dependencies involved tend to be minimal, if there are any at all. In other words, the data itself does not change, only the means of communication—kind of like how an email and a written letter can contain the same information, but the former tends to be a lot more efficient.


 

Consider ‘minimal viable product’ (MVP) deployment

Efficiencies in auto claims processing

Some solutions work best when fully integrated with others, but can function independently as an early-stage version while other parts of the ecosystem queue for upgrades. The term ‘MVP’ is often used in the start-up world, and Techopedia defines it as follows:

  • [The product] has enough value that people are willing to use it or buy it initially.
  • It demonstrates enough future benefit to retain early adopters.
  • It provides a feedback loop to guide future development.

This mentality can be useful for the streamlining of low-to-moderate data dependent processes. You can use a functional MVP version of the digital system at first, then improve, iterate, and install more sophisticated functionality as the ecosystem adapts.


 

Auto claims automation example

Providing a self-service mobile app for fast damage capture / FNOL reporting is one such solution. The MVP version is not deeply integrated with your systems, but it does empower the insured to transfer information about an incident, including pictures and VIN through intuitive photo guides and in-app instructions, saving up to 15 minutes per file2 for FNOL agents. Appraisers can also use a version of this app (the functionality is virtually the same) which can help improve consistency.

The agent may still need to manually transfer information received from the app into a legacy system, but from the insured’s perspective, things happened a lot faster and smoother. Plus, the time saved per file can have a serious annual impact on the bottom line.

Solera app for fast-damage capture

GoTime Inspection


MVP drawbacks

A big challenge with the MVP approach is acquiring valuable feedback from technically proficient and articulate users. Since major claims are (hopefully) low frequency events, individual insureds may not be in a position to experience multiple versions of an app or similar tool. This limits their ability to provide feedback.

In the mobile app example above, this challenge may be addressed by piloting the system with appraisers first. They will have the chance to use the app many times and will have more incentive to suggest improvements. A more robust version can be made available to insureds in the future.


Examples of data dependent improvements

Many systems rely heavily on others to provide information in order to function. Yes, these are often the toughest pieces of the puzzle to migrate or streamline, but the unrelenting pressure of the digital age will force the necessity sooner or later (all indications are in favour of ‘sooner’).

Imagine an API that gives our example app access to a Direct Repair Program (DRP) database. Armed with partner shop locations, the app’s functionality can extend to allow the insured (or FNOL agent) to select the most proximate (or efficient, see below) collision technician.

In later stages, the system can be supercharged even further. For example, consider software that enables performance based shop allocation and total loss prediction. Data normalization and machine learning tools can be used to route insured’s vehicles to DRP locations that have demonstrated the most efficient history of repair based on a variety of previously inaccessible factors such as trim, damage severity or customer satisfaction index. Similar algorithms can assist agents on making an accurate total loss decision before the vehicle is routed, potentially reducing costs and cycle time associated with multiple tows (up to $100 per file3).

Solera tech for intelligent shop assignment

 

AudaTarget for intelligent shop allocation

Learn more

The major data dependency for this solution is years of historical estimate data, which may not be readily accessible depending on the ecosystem’s current state. However, MVP versions can make use of third party data to prove the viability of the solution while proprietary data is collected or retrieved.


Plan for future state systems

future of auto claims automation

Refitting more and more of the claims cycle process to include automation paves the way for many other exciting and innovative components, including but not limited to:

  • Intelligent appraiser dispatch: increase appraisals by up to one file per day2 through optimizing assignment dispatching based on workload, skill set, geography or any number of custom factors.
  • AI-based voice response routing: Reduce call transfers, prioritize escalation and improve customer satisfaction by using AI to detect vocal stress-levels in incoming calls from insureds.
  • Straight-through processing of estimate with compliance: Reduce image desk analyst workload by up to 90%2 by using machine learning to flag compliance violations (majority of estimates are auto-approved).
  • Digital signatures: Improve cycle time and customer satisfaction by allowing insureds to sign-off on estimates, settlements or repairs right from their mobile device or home computer.
  • Vehicle replacement concierge: reduce rental costs and cycle time2 by providing insureds virtual, real-time views of trim-matched replacement vehicles—right on their mobile device.

All these solutions work towards reducing cycle time, labour costs or improving the insured’s experience. Many achieve all three and complement each other as well. As more and more industries adapt to mobile and smartphone technology, consumer expectations will continue to move in this direction as well.


Solera’s automation philosophy

While we build and install many of these systems for our partners, each is designed to plug and play with non-Solera systems in order to ensure the success of a multi-stage, iterative migration strategy. In today’s lightning fast pace of innovation, our preference is in nimble software and solutions.

If you would like to learn more about how we can help with either getting the process started or bridge a technology gap in an existing auto claims automation project, please contact us today!

 

contact information


References

1 Enterprise Car Rental. Public information on U.S. and Canadian car rental times for repairable claims

2 Internal Audatex pilot studies with major insurers (please contact us for more information)

3 Publicly available data on reported average Canadian tow prices (as of October, 2017)

 

 



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