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HIGHLIGHTS

  • The commercial insurance underwriting function is grappling with talent shortage and increasingly demanding customers.
  • Insurers need to invest in infrastructure that can analyze data from internal sources as well as external partners or open data platforms using application programming interfaces (APIs).
  • An analytics-backed underwriting platform allows commercial insurers to leverage their wider market reach and greater understanding of risk data to generate increased sales and new business opportunities.

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ABSTRACT

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, which poses challenges in managing them.

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).

 

Figure 1: Rethink underwriting in commercial insurance

Figure 1: Rethink underwriting in commercial insurance

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.

Data assimilation

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.

Risk segmentation

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.

WHAT A TARGET STATE FRAMEWORK LOOKS LIKE

Insurance companies can utilize analytics-backed underwriting solutions to scale up their commercial insurance business using cloud-based platforms.

Automated data collection through APIs or blockchain platforms, along with AI or ML and data analytics will be core to driving this target state (see Figure 2).

 

Analytics backed underwriting

Figure 2: Analytics-backed underwriting – Target state

The success of an underwriting platform will depend on three key factors – real-time availability of relevant data from the ecosystem, scalability of the platform to access new data sources, and maturity of risk-monitoring models to generate relevant insights for underwriters. With innovative communication technologies like 5G, the number of connected devices and machines is expected to proliferate, providing new data sources. Insurers need to work toward building an ecosystem of data providers from customers and partners. The scalable, next-generation underwriting solutions can monitor continuously and generate insights for businesses in real time to prevent material losses. 

PREPARE FOR THE TRANSFORMATION

Analytics-backed underwriting platforms can support commercial underwriters in providing expanded services with real-time quotes, customized risk protection at scale, dynamic risk identification, and recommendations for risk mitigation.

This has the potential to generate differentiated offerings and additional revenue opportunities for commercial insurance companies. Successful transformation of the underwriting process will require insurers to focus on the following:

  • Establish collaborative digital channels to help customers share risk-related data and documents and interact with underwriters for other relevant inputs
  • Identify relevant data sources and partners for customer risk assessment in consultation with all stakeholders, and establish API-based data-sharing frameworks
  • Develop tools for data collection and standardization from relevant data sources
  • Establish scalable, cloud-based data storage for continuous data integration
  •  Collaborate with underwriters to build scalable risk modeling processes and support them with AI or ML tools to identify new risk factors

 

Commercial insurance companies can deploy analytics-backed underwriting platforms on the cloud by using these scalable data integration and dynamic risk modeling systems. Besides, active collaboration with data and technology partners will allow underwriters to regularly monitor the risk and premium pricing models. They can accept or modify the changes based on evolving scenarios, followed by developing platform capabilities for covering business risks across various categories and sizes.

TAPPING INTO UNDERWRITING EXCELLENCE

Data-led underwriting has the potential to transform the insurance buying experience for both MSMEs and large corporates.

As insurtechs unlock initial success in automated underwriting of business risks, commercial insurers must act swiftly to collaborate with technology partners in developing both core features and a data-led underwriting platform to differentiate their offerings. With the successful deployment of an analytics backed platform, commercial insurers can leverage their wider market reach and greater understanding of risk data to generate increased sales and new business opportunities. 

For more information on TCS' Banking, Financial Services, and Insurance (BFSI) unit, visit https://www.tcs.com/banking-financial-serviceshttps://www.tcs.com/capital-markets, and https://www.tcs.com/insurance

Email: bfsi.marketing@tcs.com