How does InsurTech improve underwriting accuracy?

The introduction of new and diverse data sets into traditional insurer assessment, pricing and management of risk presents a number of interesting questions and opportunities. How does the product of insurance, until now - a promise to pay, need to evolve when risk of loss can be foreseen, predicted and managed before damage manifests? What happens to the concept of homogeneity - collecting premium from a large number of similar risks to pay for those unfortunate to suffer loss, when the view of risk and granularity of underwriting decisions are bespoke. How does a potential polarisation between those clients that 'tick the box' and those that no longer form part of the pack work alongside the societal virtues that insurance espouses?

Leading P & C Insurance Product companies like Guidewire provides products like HazardHub that curates third party data sources that Insurance companies can rely on to understand Customer Risk Characteristics.Example : They have so many data variables for Commercial property line of business they Insurance companies can tap use to derive accurate premium

The insurance industry is undergoing a data-driven revolution fueled by InsurTech. By incorporating external data sources like telematics from connected cars, wearables for health insurance, and even public records, InsurTech paints a more complete picture of an applicant's risk profile. This not only reveals hidden info. but also allows Artificial Intelligence and Machine Learning to analyze vast amounts of data. AI can then identify patterns, automate risk scoring, and even make adjustments based on changing risk factors. This translates to more accurate underwriting decisions, with the power of predictive analytics helping estimate claim probability, identify fraud, and personalize policies for a more customized insurance experience.

Insurance underwriting and risk assessment is at the dawn of a new era. Advances in data analytics, intelligent insights and new data sources, especially from Generative Ai, will provide unprecedented depth and granularity of data for underwriters and brokers - and even the insurance buyer. In my role in the insurance Market, I'm fortunate enough to hear from a vast range of subject matter experts and frontline insurance professionals on this topic and it's clear that see that change is coming. The question really is timing and applicability. We're seeing a wide range of technology firms developing these new data driven tools, although most are still in a reasonably embryonic stage. As LLMs develop, underwriting is set to enter a new era.

Data analytics is very crucial for underwriting accuracy. Beyond the initial application information, there are often hidden complexities to risk assessment. Additionally, underwriters may lack visibility into the performance of different Lines of Business– hindering strategic decision-making. InsurTech leverages data analytics to tackle these challenges: By analyzing vast datasets, advanced analytics can identify subtle patterns and correlations that might be missed by humans. This leads to a more complete understanding of the risk being insured. InsurTech provides underwriters with insights into LoB performance, highlighting areas that require adjustments in pricing or terms. This empowers them to optimize products and minimize losses.

Underwriting is primarily done by insurance companies by using “structured” data. Traditonal data models.Recent advances in AI field will allow organizations to utilize “unstructured” data to position the insured more accurately at the proper rate. Using AI, companies will be able to discern trends and correlations that because of the time required has not been possible. Models will be able to “connect dots” across seemingly disparate and unrelated sets of data better reflect the risk associated with a particular client. Moreover, this same analysis will allow companies to finally break away from the “one size fits all” world in which the industry now rests and create products that are more customer centric and more appropriately priced.

Underwriting accuracy hinges largely on a complete risk profile. Traditionally, underwriters relied heavily on experience, but this approach faces limitations due to an aging workforce. InsurTech bridges this gap by: Data Aggregation: It enables gathering data from diverse sources – beyond traditional sources – to paint a more holistic risk picture. AI-powered Insights: Leveraging AI and Machine Learning, InsurTech analyzes this data to generate insights that pinpoint hidden risks and suggest appropriate premiums. This combined approach ensures a more accurate risk assessment, allowing insurers to offer competitive premiums while maintaining profitability.