In the Insurance sector, underwriting isn’t just a process—it’s the cornerstone of risk evaluation. As we look towards a market projected to surge to $222 billion by 2026, underwriting’s role in shaping premiums and coverages has never been more pivotal. Traditional underwriting has been a complex and time-consuming process involving hundreds of data points (medical history, financial stability, occupation risk, lifestyle factors, etc.) and multiple guidelines that must be carefully reviewed.
Enter Large Language Models (or commonly known as Generative AI), which are not just a stepping stone but a giant leap, bringing a new era of optimized underwriting. By rapidly analysing complex and voluminous data points, it can proactively identify risk and deviations with a high degree of accuracy.
The result? A streamlined process that not only accelerates decision-making but also reduces bias and improves standardization, paving the way for accurately tailored premiums and coverages.
Leading the wave of innovation, we, at LUMIQ, have developed a Gen AI enabled Underwriting Co-pilot that embeds LLM models into the existing Underwriting process of the Customer. It doesn’t replace but enhances the current underwriting workflow with the sharp acumen of Generative AI. The application is designed to create a symbiotic framework where technology works in tandem with human expertise, ensuring that UWs have an extensive support without any disruption to their familiar workflow.
Here’s the impact it makes
Without Generative AI:
For each application, based on the information disclosed by the customer, the data is provided to the Underwriter. Based on this, the UW identifies the most important parameters in the application and starts mapping these to the risk assessment guidelines.
However, since these data points are often in hundreds, the journey is fraught with potential oversights and critical errors can lead to significant financial repercussions.
With Generative AI:
Our Underwriting Co-Pilot, with its advanced LLM Models meticulously matches and aligns each data point with the company’s respective underwriting guidelines. The application is designed to highlight any parameter that deviates from the norm and pairs/overlay it with the relevant section of the guideline for a seamless review.
Assessing various risk parameters such as profile risk, financial risk, geographical risk, Morbidity risk, Political Exposure, etc. the system acts like a well-orchestrated symphony. Furthermore, the LLM models don’t just evaluate – it predicts. Recommending the Next Best Action, it presents a concise risk narrative for each application.
However, even with all this automation, it is designed to always respect the Underwriter, who have the option to reject the recommendations and modify the next steps based on his/her experience and a particular case. Incorporating all this feedback the LLM Model continuously learns and adapts, and able to provide more nuanced recommendations. It’s this collaborative intelligence that enables the system to evolve.
Case in Point:
Consider an application where there is a substantial mismatch between the applicant’s income and the requested coverage. The module aids underwriter by providing the following recommendations:
Decision: Decline the case
Reason for Decision: The applicant’s profile presents a high financial risk due to a significant discrepancy between annual income and the proposed sum assured
Next Steps: Inform the applicant of the decision and the reasons for the rejection and advise to consider applying for a lower sum assured that aligns with their financial profile
This solution also provides detailed feedback on all the parameters and risks it has identified and highlighted, which can be reviewed by the Underwriter to check the accuracy of assessment.
How does this work?
The Underwriter Co-Pilot is a cloud-based solution, which is designed to be cloud-agnostic and can work with multiple proprietary and open-source LLMs available in the market. The key components involved in this solution are:
- Data Ingestion and Pre-Processing – The solution can integrate with various internal and external applications to ingest data about applicant. This step can also include extraction of data from scanned documents, which an applicant might have submitted for policy approval, and may also include cleaning of data, data format normalization, etc.
- Embedding Generation – The textual information from the underwriting manuals and policy documents are processed to generate vector embeddings. This transformation allows the data to be processed and understood by machine learning models.
- Feature Engineering and Model Deployment – Key attributes relevant to risk assessment are extracted and processed to a format suitable for training and prediction. The pre-trained LLMs can be fine-tuned with insurance domain specific data to improve decision making accuracy.
- Assessment and Recommendation – Advanced algorithms/ML Models analyses every data point, matches with the UW rules and generates risk scores. The Gen AI model, by collating, all the information, recommends the decision along with a detailed explanation and associated risks.
- Feedback and Re-training – UWs can provide feedback on the LLM recommendations and the same is used to refine model output for future cases.
The solution also includes components like load balancer (distribute incoming requests to ensure system reliability and availability), container orchestration (to manage deployment and scaling of containerized application), database management (MongoDB, PostgreSQL, etc.), API Gateway and so on. In terms of Data Security and Compliance, the solution uses cloud native services to ensure that all data stored or processed by this solution is in line with the regulations put forward by IRDAI and other regulatory authorities in India.
The benefits our solution brings to the table are also very robust and tangible:
- Increase in Underwriter Productivity – With this technology, UW can process much more applications in lesser time
- Consistency in Decision making – The application provides standardized evaluations, ensuring decisions are based on uniform criteria
- Improved Accuracy – The ability to process vast amounts of information and learn from past interactions, minimizes the risk of human error
- Cost Reduction – Reducing the need for extensive analysis, the productivity is enhanced, and operational costs are lowered
- Enhanced Customer Experience – Faster processing times mean quicker responses, resulting in a satisfactory CX
LUMIQ’s Pioneering Role in Data & Analytics
LUMIQ, a Data & Analytics company, has over 10 years of experience in the Financial Services Industry. With our deep domain expertise and commitment to innovation, we have always been at the forefront of the transformation to convert data into valuable assets. We have helped Insurance companies improve their data maturity by enabling effective management, analysis and value creation.
We don’t just follow trends, we set them. With our cutting-edge solutions, we are transforming the process of Underwriting for the Insurance sector. Dive into this transformative journey with us, and let’s shape the marketing narratives of tomorrow, together.
Contact us at sales@lumiq.ai to know more details about the use cases that will be beneficial for you.
