AI Applications • January 19, 2024

Streamlining Claims Litigation and Enhancing Decision-making with Snowpark


Insuring your customers’ futures against unpredictable circumstances is crucial. But what about your securing the financial health of your organization? Anticipating claim outcomes and litigation potential empowers insurance carriers to plan and estimate for what lies ahead.

An insurance firm's profitability and efficiency hinge on the comprehensive management of operations, including sales, marketing, costs, and risk. While some claims follow systematic procedures, others escalate to litigation, demanding increased involvement of legal entities and additional time and resources.

Navigating the rising tide of claims litigation

In recent years, the surge in claims has forced insurance companies to adapt their business models to mitigate the escalating costs of litigation.

Industry trends

Litigation arises when an insured company or individual raises a concern that the claim is unjustly denied, under-settled, or rejected for negotiation. The inability to predict lawsuits during the early stages of the claims process contributes to significant expenses and resource allocation for the insurer. According to industry studies, litigation expenses can account for up to 20% of an insurer's total claims costs, making it a crucial area of focus for efficiency and cost management.

To address these challenges, insurers embrace data-driven predictive analytics to enhance risk assessment and dispute prevention. They are also refining their claims handling procedures and exploring alternative dispute resolution methods to minimize the need for litigation. These proactive measures not only ensure optimal compensation and prevent future claim reopening due to legal proceedings but also aim to reduce the substantial costs associated with litigation for insurers. 

Unveiling the challenges of claims litigation

The claims adjuster's responsibility includes diligently reviewing all claim-related information and approaching each claim in good faith. However, the diverse formats in which valuable data is generated often result in unstructured data, posing challenges for extracting meaningful insights. This wealth of data presents an opportunity to utilize advanced analytical models to enhance claims assessment effectiveness and even predict the likelihood of litigation.

Challenges faced in predicting claims litigation manually

  • Pressure to lower legal costs and improve business intelligence: Claims operations managers are under increasing pressure from insurance executives to reduce legal costs and enhance business intelligence for better case outcomes.
  • Handling data in multiple formats: Varied data formats hinder analysis, impeding actionable insights due to missing data and inefficient workflows.
  • Manual analysis and processing duration: Manual analysis by adjusters leads to high turnaround time and delays in settlement.
  • Resource and time constraints: Increased claim frequency and severely strained bandwidth of resources prolong processing times and burdens the existing workforce.

Qclaims - Harnessing the power of Snowpark for predicting claims litigation

Machine learning solutions offer valuable assistance to insurance companies in identifying claims that have a higher likelihood of litigation. By utilizing these solutions, insurance companies can proactively prevent litigation, leading to optimized costs and efforts, ultimately safeguarding their financial stability and preserving their reputation.

Quantiphi has developed an analytical solution, QClaims with Snowpark to streamline claims litigation prediction for its customers. The design of this solution focuses on several key features:

Identifying patterns

Our AI models have the capability to analyze extensive datasets, such as historical claims data, court cases, and external data sources, to identify patterns and provide predictive insights on the likelihood of litigation for specific claims.

The proprietary model, developed through a thorough evaluation of multiple algorithms, is built on the LightGBM algorithm. It has been trained on sanitized production-grade datasets derived from past claims. The inclusion of weightage scores for each feature enhances the accuracy of predicting the legal risk associated with a claim.

Application on new claims

QClaims enables accurate prediction of the claims that are likely to proceed to litigation, leveraging a pre-trained model trained on our existing dataset. This model can seamlessly analyze new datasets, providing direct insights without significant training or model deployment time.

By incorporating the claims litigation model into the claim life cycle, the risks of a claim ending up in court can be precisely forecasted. This empowers us to apply the appropriate rules for analyzing new claims, ensuring proactive management and mitigation of potential litigation risks.

Data visualization and informed decision making

Comprehensive information is essential for accurate claims assessment, encompassing variables such as age, injury type, residential area, and more. These factors provide valuable insights into the nature of each claim. By utilizing a pre-trained machine learning model, adjusters can leverage these features to make informed predictions about the likelihood of litigation.

Businesses can employ efficient tools like Streamlit to communicate and visualize these insights effectively. Streamlit enables adjusters and other stakeholders to easily explore and interact with analyzed data, gaining valuable insights into the underlying factors influencing litigation probabilities. This empowers them to identify patterns, spot trends, and make well-informed judgments regarding claims.

By leveraging Streamlit and a combination of advanced machine learning models, businesses can transform their claim assessment processes, reduce subjectivity associated with manual evaluations, and enhance their ability to accurately predict the likelihood of litigation. This combination of advanced analytics and user-friendly visualization tools paves the way for data-driven and efficient decision-making in the complex field of claims litigation.

Preventing claims from falling into the litigation stage

Claims flagged with a high risk of litigation will be assigned to a human counterpart for further investigation, helping prevent tensions from escalating. This collaborative approach ensures that claims adjusters receive actionable intervention measures that are specifically tailored to each claim and the insured individual.

Why Snowpark?

  • Snowpark allows developers to code programmable Python notebooks directly in Snowflake, eliminating the need for data hair-pinning and preventing data duplication.
  • Snowpark offers increased execution speed at lower costs, while also providing improved governance and compliance measures.
  • By combining Snowpark's AI/ML capabilities with business rules, organizations can achieve remarkable straight-through processing.
  • Snowpark enables the processing of large volumes of structured and unstructured data using optimized computing capabilities.

Optimizing claims litigation costs with Snowpark and Quantiphi

Quantiphi's proprietary claims litigation solution helps assess each claim early in the claim life cycle, enabling us to make accurate predictions for improved outcomes. These predictions include minimizing the probability of claims reaching the courtroom, optimizing payment processes, expediting satisfactory settlements, and streamlining the overall claim settlement procedure.

The implementation of an intelligent claims litigation prediction model has significant business impact -

  • Enable insurers with efficient resource allocation
  • Prioritize claims with higher litigation potential
  • Reduce overall litigation costs, and take proactive legal measures. 
  • Facilitate strategic decision-making based on insights, and provides visibility into legal expenses. 

The deployment of the pre-trained model enables faster time-to-market and better ROI, making it an indispensable tool for insurance firms to optimize their legal operations costs and drive data-driven decision-making.

Early engagement of the appropriate teams can lead to a fair settlement between the potential claimant or third party and the insurer, improving readiness for the litigation process. This approach mitigates legal spending and provides transparency about expenses and costs. 

Take control of your claims litigation process and unlock cost savings with QClaims. Streamline your operations, enhance risk prediction, and make data-driven decisions to minimize the likelihood of litigation. Don't let legal costs drain your resources—empower your business with advanced analytics and efficient tools.

Contact us here to explore how Snowpark and Quantiphi can transform your claims management and drive better outcomes.

Get in touch with our Snowflake Alliance team 
Pronoy Roy
Snowflake Alliance Lead                               

Chaitra Kadam
Snowflake Go-to-market Lead

Ariela Satterlee
Client Partner Solution

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