14 MINS READ
Experience versus technology has been a classic debate in commercial insurance underwriting.
Some view the process as an acquired skill, requiring years of experience in handling multiple data sources and assessing their impact on business risk. However, the function is now beset with talent shortage challenges and increasingly demanding customers. Businesses now want insurance coverage personalized to their specific needs, with quotes delivered to them within minutes instead of days.
Automation in insurance strives to address this challenge. But transforming commercial insurance requires more than just automation. Insurers need to invest in infrastructure with the capabilities to analyze data from internal sources as well as external partners or open data platforms using application programming interfaces (APIs). An ideal next-generation underwriting platform can automate commercial underwriting in small and medium businesses and provide quotes in real time through online channels. For large corporates, the platform can streamline the commercial insurance processes and develop customizable, industry-specific risk analytics tools. We discuss how data analytics-backed underwriting platforms will allow insurers to partner with their customers in protecting their valuable assets.
Trends shaping the insurance industry
Insurance underwriting deals with assessing and pricing the risks associated with the insurable.
Be it the hyper-dynamic cyber landscape or the physical world entrenched with the constantly changing scenarios due to the COVID-19 pandemic and supply chain challenges, risks are evolving in every sphere. The constant changes in the productivity of organizations due to strained supply chains, employee health concerns, geopolitical changes, and compliance requirements make it difficult for reliable business and financial forecasts. The impact of these risks on different industries is often beyond established risk models, coupled with limited expertise or experience.
Set against this ever-changing backdrop, data-driven transformations in personal insurance underwriting, such as telematics-based continuous underwriting for motor insurance, on-demand insurance, and other similar trends, are gaining momentum. Growing market needs of micro, small, and medium-sized enterprises (MSMEs) customers and the dearth of experienced talent are driving similar trends in commercial underwriting as well. MSME customers now prefer direct channels and straight-through processing (STP) for financial transactions, whereas large business customers favor bespoke offerings customized to their requirements.
The perennial needs of insurance carriers to increase profitability, improve the speed to market, and gain competitive advantage necessitate data-driven underwriting, automation, and pricing accuracy. This, in turn, is shifting the expectations from underwriters from mere risk evaluators to solution providers.
Underwriting transformation in progress
The emergent changes in underwriting in commercial insurance collectively warrant a rethink of the traditional underwriting processes (see Figure 1).
For MSMEs, data collection, as well as risk evaluation processes, will need automation. As insurance companies focus on business growth, the role of underwriters will expand. They will be required to explore ways to make a risk acceptable for insurance coverage and keep track of new risk profiles. Small businesses prefer standardized low-latency models for risk evaluation, which can be included in self-service mode. Insurtech companies now provide instant quotes to small businesses using artificial intelligence (AI)-driven engines that leverage open-source data for automated underwriting. MSME insurance companies, like Next Insurance support a wide array of business categories, including companies in retail, construction, education, leisure, and other niche services.
Similarly, in large businesses, risk evaluation is based on a hybrid model, whereby underwriters must assess and recommend innovative risk solutions that address their specific business needs. Meanwhile, the insurer’s system takes over the tasks of data gathering and evaluation. For example, Chubb’s general liability platform Customarq provides customizable general insurance; its industry-specific underwriting practice helps address specific risk coverage requirements of businesses. This process currently involves significant manual effort, but in time, the adoption of automation and AI-driven analytics for faster and more robust risk assessment will increase.
Unlocking value from data-led underwriting
We list the five areas that insurers must focus on when building a data analytics-backed underwriting platform for either MSMEs or large corporations.
High underwriting expenses often stem from the efforts associated with obtaining customer and risk information through back-and-forth communication involving brokers, customers, investigators, and third parties, among others. Streamlining data collection from multiple digital sources will improve the data or decision-making accuracy and bring down expenses drastically. For instance, environmental risks can be evaluated from satellite or drone imagery overlayed with weather events, such as floods. Meanwhile, internal operational risks can be assessed through data sharing on machinery breakdowns, accidents, and other unforeseen events.
Validation of risk appetite, underwriting rules, and risk scoring
With the digitization of rules, insurers will find it feasible to automatically identify if a prospect fits into the insurer’s risk appetite. In addition, they can peruse the various underwriting rules of the insurer and perform risk scoring. This delivers a consistent underwriting result in comparison to depending on only the respective underwriter’s experience or expertise. In more advanced scenarios, AI or machine learning (ML) can be leveraged to learn from historical cases and classify new risk categories based on the analysis of past data.
With in-depth data available for different risk perspectives, risk segmentation can be performed at a more granular level than generally considered. For instance, property risk pertaining to a commercial entity can have multiple sub-segments like local geographical risk, liability risk posed with external fittings, and more.
Loss control and coverage recommendation
It is now feasible to suggest loss control measures and turn some risks into acceptable categories with a systemic approach. This can present new opportunities for business growth. Conversely, it can also help insurers avoid unfavorable risks in certain other cases.
Continuous risk classification
Continuous data collection and analytics can enable swift reclassification of risk categories based on newly available information, which changes the probability and impact of risk factors in the existing underwriting model. The dynamic economic scenarios in the world today can now be assessed in near real time, and the requisite recommendations for risk mitigation can be communicated with the customers for corrective action.