Underwriting Process Augmentation

About the Customer & Challenges Faced:

Accurate claim records can positively impact reserve requirements/loss ratio. This leads to premiums that are more reflective of a payer’s actual experience, rather than a conservative estimate. Knowledge of claims history is powerful information when it comes to negotiating/pricing fair and competitive insurance premiums.

A large healthcare payer based in Pennsylvania wanted to revamp legacy processes; using analytics to gain knowledge and confidence to increase deductibles and retention limits.

Solution and Approach:

  • Some major enablers towards large-scale adoption of AI-ML practices and precision CV-based medicine, available through xpresso.ai, include dynamic availability of numerous analytics algorithms, models and methods in a pull-down type of menu, easy management of important issues like data ownership, governance and standards, continuous data acquisition and data cleansing. ​
  • xpressso.ai framework provides out-of-the-box development platforms. The project was started seamlessly 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 framework made available by xpresso.ai leverages the latest ML and DL tools while preparing models and includes Pachyderm-based data versioning, deployment using a Kubernetes orchestration system, Kubeflow and Spark-based ML and DL build and deployment, Istio-based service mesh enabled microservice architecture, and ELK based monitoring capability; contributing to reduction in latency time.​
  • The platform can potentially be fed with input data in the form of insurance databases that include size, sales channels, product mix or geography of an insurer (often viewed as cost drivers) and customer data including available social media exposure, details of actual medical conditions reflective of the customer’s actual experience instead of a general overview. We identified input data points such as claims data, enrollment data, prescription data and member data in our endeavor to improve pricing and customer service for our insurance client’s group insurance customers and create effective models.​
  • xpresso.ai’s AI Ops framework allows establishing high-end Alluxio and Presto-based efficient data connectivity and collecting data from diverse sources. The details collected were added as exploratory variables by using xpresso.ai libraries and analyzed. 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 xpresso Data Model (XDM)-enabled data store that enabled easy retrieval and storage of datasets/ files into internal XDM. This was achieved by using two excellent features of xpresso.ai: ​
  1. Data Connectivity Marketplace libraries​
  2. Data Versioning​
  • We also interacted with actuarial, underwriting, and product teams to discover and understand their nature of costs and current pricing methodology. This helped us develop a predictive model for costs on a cohort basis based on various input data parameters. ​
  • xpresso.ai read factors from a varied recommendation text connection and generated output. From all these variables obtained, models were created and versioned — enabled by xpresso.ai MLOps framework. This resulted in a pricing methodology that incorporated the cost prediction model into the current underwriting process. We were able to help insurers identify potentially high claimants’ high-risk customers and also save underwriters time in gathering data towards different conclusions while underwriting. They were able to focus more on analysis and decision-making, increase the granularity of risk analysis and enabling pricing adequately, improve underwriting productivity and throughput, achieve greater consistency in decision-making and strong governance of the underwriting process, and reach 82% accuracy in cost predictions. This also provided an opportunity for the insurer to extend enhanced customer service by offering advice and protection from health adversities.​

Leveraging xpresso.ai​:

  • By using xpresso.ai, one can leverage high-end data connectivity, efficient data versioning, perform exploratory data analysis and generate inferences using an intuitive process and through an industry-standardized manner.
  • The unique, containerized platform-centric approach offered by xpresso.ai can be used to employ required infrastructure, deploy rapidly to multiple high-availability environments while aligning with best-in-class DevSecOps practices.
  • xpresso.ai also brings in-depth QA-QC testing and logging frameworks, synchronous and asynchronous monitoring, and performance tracking ability.
  • xpresso.ai also has SSO (single-sign-on) for various in-built tools and subsystems that make the platform access seamless throughout.​
  • In a nutshell, all the above features in a single plate under the same hood make xpresso.ai an unbeatable AI Ops framework.

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