Predicting Subrogation Potential – Role of Deep Learning (Part 2)

By January 16, 2019Blogs

In part 1 of this blog, we discussed why subrogation is important and how effective subrogation positively impacts the company’s bottom line. We also enlisted the many causes why potential subrogation opportunities are missed. These could be manual methods of evaluation, lengthy and costly investigation processes, and complex rules governing subrogation. These drawbacks are compounded due to overworked and understaffed teams who are not able to effectively prove third party liability and collect subrogation dues. In the following blog, I will discuss how predicting subrogation through deep learning helps to effectively reduce losses and provides greater customer satisfaction and enhanced loyalty.

Deep learning solutions for subrogation

Intelligent automated systems (deep learning systems) involving a combination of text mining and data modelling techniques has been found to improve determination of valid subrogation opportunities. Deep learning systems for subrogation involve:

Any solution would involve a combination of text mining and deep learning based predictive modelling on Claims data to analyse both structured (Claims Data) and unstructured data (Claims Forms & Notes) to predict the propensity score for subrogation opportunities. It can be depicted as follows:

The first step is to ingest structured and unstructured data in a platform (on premise or cloud). The unstructured data in the form of Claims forms and notes are mined for useful information and facts regarding the subrogation opportunities.

The next step would be to train a deep learning model on the ingested data. The data labelling based on the past subrogation cases whether the subrogation is successful or not helps with the better training of the model. Using the supervised learning technique, the model learns combination of features determining successful cases. The model can then be applied to new claims and subrogation potential propensity score is calculated. Higher the propensity score, higher the subrogation potential. 

Key Benefits

The right combination of process optimization and technology applied to the subrogation function can help to improve the company’s profitability and build on its competitive advantage. Deep learning-based subrogation prediction systems help insurance firms make informed decisions on which cases to pursue subrogation so as to reduce effort and improve returns.

Automated subrogation prediction solutions have the following key benefits:

  • Allow all claims to be reviewed for subrogation opportunity: Claims with top propensity score needs to be looked into by subrogation professionals for recovery proceedings. This helps in ensuring there are no missed recovery opportunities. With the limited resources, the claims staff can focus on claims that has high propensity score.
  • Detect cases to be recovered more quickly: The automated tool helps to reduce manual effort, thereby reducing the time required to go through scores of files, reports, notes to detect a subrogation opportunity.
  • Reduce investigation time and costs: Due to a more thorough automated analysis of both structured and unstructured data, the time and costs involved in lengthy investigations reduces drastically.
  • Make highly accurate predictions: Since the model uses a supervised learning technique using past cases as positive and negative examples, the propensity score generated results in accurate predictions which can help the company to make informed decisions.
  • Involve low examiner resources use and increase efficiency: The automated nature of the analysis is low on resource use and thus increases its efficiency.
  • Provide consistent analysis across all claims and time: Since the analysis is based on algorithms and trained on past cases, the potential for human error is minimised. The results are thus consistent across all claims and time.
  • Result in minimal number of missed subrogation opportunities: Missed opportunities due to errors in judgement, omissions on the part of the staff, and faulty systems and process can be overcome in this automated analysis, leading to increased chances of recovering losses.
  • Lead to faster payment recovery: The chances of payment recovery and faster settlement increase due to the early identification of subrogation potential.
  • Indicate the need to maintain optimum reservesThe insurance company can better plan for and maintain optimal levels of reserve funds to cover potential payouts.

Key Takeaways

Using a deep learning approach for predicting subrogation potential early in a claim lifecycle helps with the faster recovery and increased efficiency of claims processing. It enhances customer experience by facilitating quick settlement of the claim and recovery of claim money by subrogation, while keeping the reserves at an optimal level. It impacts the company’s bottom line by increasing the revenue through identification and pursuance of subrogation opportunities missed through manual analysis. It improves company’s top line by identifying and pursuing subrogation opportunities through analysis of previously closed claims missed thorough manual analysis. Accurate and early predictions can help claim adjusters to prioritize and focus on claims that have a greater probability of generating the most benefit for the company. The solution utilizes a constant feedback loop to learn from the actuals vs. the predicted and further improves the propensity score for future claims, increasing the revenue potential further.

Mohit Dilwaria

Author 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.

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