Realize Your True Claims Subrogation Potential with AI

By August 5, 2020 March 9th, 2021 Blog
Subrogation Prediction AI, Realize Your True Claims Subrogation Potential with AI

The auto claims settlement process is quite complex, and the data generated is both massive and complex. It’s no wonder that insurance companies often find themselves unable to leverage their auto insurance claims data for subrogation opportunities successfully. The challenge often lies in recognizing the right opportunities and not having the right technology to achieve proper segmentation.

Here a few reasons that lead to subrogation related challenges for insurers:

  • Lack of experienced resources who can correctly identify the subrogation potential
  • Lack of an umbrella strategy for determining subrogation potential and pursuing it
  • Difficulty in keeping track of statutes of limitations and regulations that affect subrogation across all states
  • Inadequate investment in modern technology

Subrogation pays rich dividends toward mitigating an insurance company’s loss ratio. Hence, it makes sense to invest in focused resources, technologies, and strategies. Let’s take a look at the requirements of effective subrogation.

Choosing Experienced and Well-trained Resources

The practice of hiring experienced adjusters, well-versed with the intricacies of pursuing subrogation, enhances the subrogation efforts of insurers. The adjusters are trained in:

  • Techniques to identify the potential for subrogation
  • Investigation procedures to determine the third party’s potential negligence
  • Methods of preserving evidence
  • Asking the right questions

Insurers would also need to budget for periodic training refresher courses in state regulation, statutes, updates in policy contracts, defects and recalls, and case law.

Moving Toward Automation with the Right Technology

US insurance carriers are still playing catch-up when it comes to moving toward automatic settlements when compared to their global counterparts. The automatic settlement rate in the US is between 15% to 20%, whereas the UK and Australia figures are as high as 40%. Relying on data is critical for moving forward. Using the right technology with useful security features and proven algorithms can help insurers take advantage of automation.

Using an AI Platform to Identify Subrogation Potential from Claims Data

A data analytics platform, powered by AI/ML technologies and specifically designed for diving deep into auto claims data, can be easily applied to identify subrogation opportunities. Using a complex system of scoring and assigning probabilities, the machine learning algorithm can flag cases with the highest subrogation potential.

At the same time, its deep learning capabilities can be leveraged to gather information from all relevant sources such as adjuster reports, image evidence, witness record, and other public platforms. Using these data points can help adjusters assess the case and determine third party negligence and attach supporting evidence.

Besides, a platform’s advanced technical capabilities like the ones listed below, can accelerate and improve subrogation recovery:

  1. Processing unstructured text and images to identify recovery potential.
  2. Automatically detecting subrogation potential throughout the claim lifecycle.
  3. Accounting for regional recovery laws and timelines, and predicting the recovery amount.

With InsurAnalytics’ AI-powered platform, it is possible to increase recovery by 40% and reduce subrogation cycle time by 50%. The AI platform delivers definitive and timely subrogation prediction, with fewer false positives, and eliminates leakage. The AI platform enables P&C insurers to drive the real-time end-to-end low-touch to no-touch claims journey.

Here are a few benefits that insurance carriers would experience with an InsurAnalytics’ AI decision-making platform for subrogation:

Reduced overheads due to:

  • Automation of time-consuming processes from low-touch to touchless journey
  • Quicker payouts and settlements
  • Faster investigations

Accurate predictions as:

  • All relevant data gets leveraged and included in the analysis
  • Information added on different stages processed effectively
  • Identifies complex patterns that would otherwise remain unseen
  • Deep Learning algorithms are pre-trained on auto insurance claims data

Lower loss ratio achieved because of:

  • A higher number of subrogation opportunities caught and processed
  • Better successful settlement ratio due to better evidence gathering and investigation
  • Zero leakage

To find out more about improving your subrogation efforts, get in touch with our experts.

Siddhartha Vowles

Siddhartha Vowles

Siddhartha (Sid) has over 8 years of designing and customizing products and solutions in the insurance domain. He graduated from Carnegie Mellon University with a master's degree specializing in artificial intelligence. Read More Posts

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