Predicting Subrogation Potential – Pitfalls (Part 1)

By December 6, 2018 February 18th, 2020 Blog, Insurance
Predicting Subrogation

Claims are requests to an insurance company for coverage or compensation for loss or damages caused by the peril insured by the insurance policy. Upon validation and approval of the claim, the insurance company issues payment to the insured or an approved interested party on behalf of the insured.

Claims are an insurance company’s biggest expense. Claims payouts and loss-adjustment expenses account for up to 80% of the revenue lost by an insurance company. These expenses can be partially offset through claims recovery. Subrogation is one way of getting back the money spent in a claim. It is a legal right enjoyed by insurance companies that allows them to recover the payment it has made to the insured party for damages, from a third party that may have caused the loss, insured the loss, or contributed to it. For example, a driver runs a red light at an intersection and crashes into your car. Your insurance company pays for your damages, but using subrogation, files a claim with the at-fault driver’s insurance company to recover the damages. In this case, the insurance company is subrogated to the owner’s claim against the negligent third party. That means the owner’s right to recover payment from the negligent party has been transferred to the insurer.

In Part 1 of this blog, let’s look at why subrogation is important and what are the major pitfalls to effective subrogation. Part 2 of this blog covers how AI and deep learning can be utilized to identify subrogation potential and its benefits.  

Why is Subrogation Important?

Subrogation is important because any amount recovered through the subrogation process goes directly towards the insurance company’s bottom line. It helps recover loss payments, thereby reducing the overall loss amount. These benefits reflect directly in the company’s performance. The biggest benefit goes to the insurance company’s policyholders as it helps to lower the premiums.

According to Christopher V Tidball, author of ‘Re-Adjusted: 20 Essential Rules to Take Your Claims Organization from Ordinary to Extraordinary!’ it is estimated that subrogation opportunities missed by insurers amount to $15 billion annually in the US alone.

Pitfalls to Subrogation

Effective insurance subrogation helps in recovering up to 22% of paid claims. However, opportunities for claims recovery through subrogation are often missed due to a number of reasons. Some of the top reasons are:

Claims adjusters

When a claim is submitted to an insurance company, it is assigned to a claims adjuster who investigates the claim by interviewing the claimant and witness, consulting police records, and inspecting damage to property. If found suitable, he refers the claims to the subrogation department. Identifying an opportunity for subrogation in this manner requires manual effort and allows room for errors in judgement or lack of motivation.

Recovery teams

Specialized Recovery Teams determine subrogation opportunities. They are highly trained professionals who are better equipped to identify opportunities and maintain consistency across the organization. That said, they are normally a small group overloaded with a large number of claims forwarded to them by adjusters. In addition, many times they are located remotely, so it requires coordination efforts with adjusters, leading to a delay in starting the investigations.

Long wait to Counsel

Even after the subrogation potential is identified, by the time the claims are referred to the counsel, the statute of limitations or some other deadline is about to expire. There are many cases where the subrogation potential is obvious but to avoid incurring the subrogation attorney’s fees, subrogation is “avoided” and sent to the counsel when there is very little time left for investigations and processing with the third party. Considering that it is an investment in time and money, the claims are prioritized based on the dollar amount in question.

Missing third-party liability

Sometimes, inbuilt systems to identify subrogation potential are not sufficiently dynamic to accommodate varying scenarios of claims. The traditional manual strategy of evaluating the themes and patterns in claim files and police reports is a very slow and inefficient process. Most times, claims adjusters miss the liability exposure of third party. As the fault or the individual responsible for the fault is not determined immediately, the final decision about who will pay has to wait until the investigation is complete. The investigations can also prove lengthy and costly, with the result that many insurance companies neglect to pursue their subrogation right.

Investigation missteps

The insurance company needs to prove:

  • The third party was at fault;
  • The third party’s negligence caused the damages which the insurance company paid for;
  • The amount paid for those damages.

If the company is not able to prove these points, the chances of recovering through subrogation are negligible.

There are many other reasons why potential subrogation cases are never studied for probable collections:

  • Lack of proper collection strategy and staff inexperienced in collections
  • Lack of a proper metrics and performance monitoring system
  • Improper handling of disputes, arbitration, and litigation files, resulting in very low win rate

The complex rules governing subrogation, as well as overworked and understaffed teams result in many missed opportunities for recovery. Missed opportunities for subrogation translate into loss of revenue for the insurance company; this can have a significant impact on their profitability.

In Part 2 of this blog, we will see how deep learning is utilized in predicting subrogation potential and how it helps to achieve a rapid, sustainable return on all recovery opportunities. This in turn helps to reduce loss costs, ensure greater customer satisfaction and enhance customer loyalty.

Mohit Dilwaria

Mohit Dilwaria

Mohit is an accomplished IT leader who brings in over 17 years of experience in Data & Analytics (Big Data Analytics, Advanced Analytics, Business Intelligence) & AI (Machine Learning and Deep Learning). As the Associate Director of Professional Services, Mohit looks after the strategic growth and directions of teams for expanding clients use of Cloud Platforms through Consulting Implementations and Education Services. He is responsible for delivering complex deployments of critical Data & Analytics solutions to InsurAnalytics customers globally. He is an advocate of the agile delivery model and has led many large system and transformational programs in Data & Analytics. Read More Posts

Leave a Reply