Page 31 - The Insurance Times March 2025

Role of AI

The Role of AI and

Big Data in

Transforming Life

Insurance

Underwriting

The life insurance industry is undergoing a significant transformation, driven by the adoption of
Artificial Intelligence (AI) and Big Data. Traditional underwriting methods relied heavily on lengthy
paperwork, manual risk assessments, and standardized criteria, which often led to inefficiencies,
delays, and conservative risk evaluations.

Introduction High operational costs
The life insurance industry is undergoing a significant trans- Potential biases in risk assessment
formation, driven by the adoption of Artificial Intelligence Limited access to real-time data
(AI) and Big Data. Traditional underwriting methods relied
With the rise of AI and Big Data, insurers are now able to
heavily on lengthy paperwork, manual risk assessments, and
standardized criteria, which often led to inefficiencies, de- analyze vast amounts of structured and unstructured data
lays, and conservative risk evaluations. However, the emer- to make data-driven decisions, improving the accuracy and
gence of predictive analytics and AI-driven models has revo- efficiency of the underwriting process.
lutionized underwriting, making it faster, more accurate, and
personalized. This article explores how AI and Big Data are The Impact of AI and Predictive Analytics
reshaping the landscape of life insurance underwriting, im- on Underwriting
proving risk assessment, and enhancing the overall customer 1. Real-Time Data Collection and Processing
experience. AI-powered underwriting leverages data from multiple
sources such as electronic health records (EHRs), wear-
The Evolution of Life Insurance Under- able devices, social media, credit reports, and demo-

writing graphic trends. This real-time data collection enables
insurers to build a more comprehensive and dynamic
Historically, life insurance underwriting involved a manual risk profile for each applicant.
review of an applicant's health, lifestyle, financial back-
ground, and medical history. Underwriters relied on actu- 2. Enhanced Risk Assessment with Machine Learning
arial tables, standard risk categories, and medical exami- Models
nations to determine premium rates. While effective to some Machine learning algorithms analyze historical claim
extent, this traditional model had limitations, including: data, lifestyle factors, and medical histories to identify
Long processing times patterns and predict future risks more accurately. This

28 March 2025 The Insurance Times