Finance

Property and Casualty Subrogation Analytics

The property and casualty (P&C) insurance industry is being rapidly transformed by AI. Improved computing power in the cloud, cutting-edge algorithms, and big data are some of the tools that make implementation easier. These are some of the key tenets that are helping insurers achieve lowered costs. By adopting AI-powered digital systems, data is collected, aggregated, and analyzed.  As more data is collected over time, the AI systems learn the rules of the business. This helps the AI system improvise the involved processes and helps optimize them.

Allianz has argued that AI will transform the P&C insurance industry in an unforeseen way. This includes adjustments to the way risks are underwritten and how coverages are understood. Assuming complete adoption of AI across all industries reviewed, the report claims AI is expected to increase corporate profitability in 16 industries across 12 economies by an average of 38%.

Subrogation in the P&C insurance industry means that an insurer can legally pursue any third party that caused a loss to the insured. This is done to recover the amount of the claim paid by the insurer, typically for the losses incurred. Any amount recovered through this process goes directly towards the insurance company’s bottom line. A clear understanding of subrogation and the potential to incur losses or avoid them is important because it helps recover payments related to any losses and reduces the overall loss amount. This also reflects in the company’s performance and can help the insured (or the policyholders) reduce their premium outlays.

Challenges

The P&C industry has a history of using traditional paper record-keeping methods for property or casualty insurance or filing a claim.  Slow response times from customer service and going through mountains of paperwork were common, making the reach, implementation, and deployment of digital processes extremely challenging. A paradigm shift in the culture has made the transition from legacy systems a challenge.

Operators in the P&C industry and insurers are part of an industry that remains highly regulated. Because of regulatory changes, processes also need to be updated, which entails a lot of effort and time. Even if the subrogation potential is identified, when the claims are referred to the council, the limitations in the process and regulations can become a hurdle for a future deadline. There have been potential subrogation opportunities that have been given up to avoid incurring the subrogation attorney’s fees.  When the claim is sent to the council, 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, resulting in missed revenue.

Insurers in the P&C industry process numerous claims that comprise several structured data fields, such as a unique claim number, date of the loss, and amount of the original settlement. However, facts about each incident that affect subrogation are usually added as free text notes to the claim and related documentation. These notes can comprise police reports, witness statements, adjuster notes, recorded statements, medical records, and other relevant information usually modified by different people.  Adjusters perform numerous tasks for each claim, including interviewing all the parties involved in the incident, analyzing police reports, evaluating damages, and negotiating claim settlement. After these procedures are complete, identifying potential subrogation opportunities follows. This requires a coherent analysis of all notes related to the claim at once.

As claim notes get added over time, it becomes increasingly difficult for adjusters to analyze the entire claim every time there is a new update. Adjusters also frequently need to handle multiple claims simultaneously, making the task even more challenging. Identifying an opportunity for subrogation in this manner also allows room for errors in judgment or lack of motivation. Often, adjusters miss the liability exposure of third parties. 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.

Thus identifying the potential for subrogation can become very tedious and expensive if it is not automated. Missed opportunities for subrogation translate into a loss of revenue and affects profitability. It is estimated that the insurance industry misses subrogation opportunities worth about $15 billion each year.

Solution Approach

We automated the subrogation potential for a leading end-to-end payment integrity organization operating in the healthcare insurance industry. We studied the available claim documentation. This primarily came from claim notes, what caused the damage, and who is responsible for the damage.

xpressso.ai platform provides out-of-the-box development frameworks.  The project was started with the relevant environments, which were then created automatically. Development images configured based on pre-defined templates were installed on-premises or in a development VM within the xpresso.ai infrastructure. This enabled authentication using LDAP, seamless project setup using Bitbucket, Jenkins, and Docker (ensuring build and deployment without software compatibility issues).

The platform made available by xpresso.ai leverages the latest ML and DL tools while preparing models. It includes Pachyderm-based data versioning, Kubernetes, Kubeflow, and Spark-based ML and DL. It also includes an Istio-based service mesh-enabled microservice architecture, and ELK-based monitoring capability, contributing to a reduction in latency time.

xpresso.ai’s MLOps platform allows establishing high-end Alluxio and Presto-based efficient data connectivity and collecting data from diverse sources. While creating data models, raw, continuous, unformatted, unparsed data from insurance databases, specifically, adjuster notes and claim documentation was collected. By using an xpresso.ai backed claims processing solution, we extracted key information from the adjuster claim notes and used them as predictors for identifying subrogation opportunities.

By using xpresso.ai data versioning and xpresso.ai connectivity libraries, data versions were easily controlled and stored into xpresso Data Model (XDM)-enabled data store. The attributes obtained were used for categorization (employing Pachyderm-based data versioning) and then performing univariate, bi-variate, and Bag of Words analysis — for both structured and unstructured datasets through xpresso Exploratory Data Analysis (Data and Statistical Analysis).  Different datasets and their different versions were easily controlled and stored into an xpresso Data Model (XDM)-enabled data store that enabled easy retrieval and storage of datasets/ files into internal XDM. This was achieved using two excellent features of xpresso.ai:

  1. Data Connectivity Marketplace libraries
  2. Data Versioning

The xpresso Data Pipeline Management (Rapid Model Training and Experimentation) uses Kubeflow-enabled pipelines. Thus, multiple experiments using different models and datasets could be created, tested, paused, and restarted to gain better insight.

This information was obtained from adjuster notes — available in a digital format. These notes include vectors embedded into a Document Vector (DV) for easy identification and processing. Every time the adjuster claim notes were updated, the claim was scored again for its subrogation potential.

With the help of the deep linguistic analysis capabilities offered by xpresso.ai, data analysts were able to accurately extract 700+ attributes covering key information about each claim, such as type of collision, vehicle point of impact, driver actions, liability and injuries of all parties, and many more.

xpresso.ai read factors from a varied recommendation text connection and generated a result. The DV was further used to determine subrogation potential, which continuously identified and learned new trends on existing and new datasets, thereby increasing the engine accuracy. This predictive model provided a subrogation-likelihood score, key reasons for making a claim subrogate, and the expected recovery amount.

Based on the models generated on xpresso.ai, the organization was able to identify subrogation opportunities based on the analysis of historical claim records. It enabled the organization to make informed decisions to pursue subrogation first to minimize effort and maximize returns. Additionally, since the solution predicts claims with subrogation potential and presents key claim facts in a tabular format — easily accessed via web reports — claim adjusters could efficiently analyze claims.

Early identification of subrogation opportunities means a higher probability of payment recovery and quicker settlement with the third party. It also means planning for and maintaining optimal levels of reserve funds to cover anticipated payouts and reduce inadequate reserves.  All of this improves revenue and profitability.

How xpresso.ai can help Finance Organizations transform their journey to cognitive AI solutions

xpresso.ai is an AI/ML Application Lifecycle Management Platform.

xpresso.ai enables complete lifecycle management of AI/ML solutions, addressing the AI transformation journey of enterprises on any cloud platform of choice. xpresso.ai offers functionality essential for building AI/ML solutions – primarily enabling data scientists to rapidly build predictive and prescriptive models. The platform provides a user-friendly interface to develop, deploy, and manage AI/ML solutions at scale. In addition, xpresso.ai supports the incorporation of these solutions into business processes, surrounding infrastructure, products and applications.

Key benefits of xpresso.ai include:

  • Empowers data scientists to transform AI/ML research into solutions 
  • Improves the productivity of data scientists by enabling them to focus on the business problem, developing algorithms and rapid experimentation of models 
  • Addresses the shortage of skilled data science resources with automated workflows, toolkits and frameworks 
  • Manages AI transformation journey costs without any wastage of R&D efforts  
  • Provides an enterprise-ready and secure environment for complete lifecycle management of AI/ML applications-
  • Enables at-scale deployment of enterprise AI/ML applications on-premise, cloud (AWS, GCP, Azure), or hybrid environments

Additional details on xpresso.ai can be found at:  https://xpresso.ai. We can schedule a demo of the platform for anyone interested in learning more.

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