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AI and Black Box Underwriting for Small and Medium Businesses

by Gagan Gupta

Data is the foundation of underwriting and risk management. As technology use expands, new data sources are everywhere, but this proliferation can make the risk management process increasingly complex and tedious. Insurance companies continue to collect information and be exposed to vast volumes of data in various formats, but it is often unstructured and can be siloed within distinct business units. This lack of access or a central database for decision-making can cause issues for underwriters, making the underwriting processes reactive and without a nuanced evaluation of risk parameters.

These issues can be even more significant when underwriting small and medium-sized businesses (SMBs). Regarding market opportunity, SMBs represent a compelling option for insurers globally. However, determining risk parameters and identifying other critical information remains a challenge. Accurate data on the account level is often rare for these entities because the information is often outdated, inconsistent across business types, poorly structured, or simply not available. Such quality issues can prevent insurers from deriving meaningful insights to improve risk assessment and decision-making. Insurers can also face challenges in sourcing reliable external data, identifying SMBs with a history of frequent or severe claims, and avoiding high operational costs related to covering such accounts. These factors can result in a poor customer experience for the policyholder and reduced profits or adverse risk selection for the insurer.

There is a solution, however. Data-driven and AI-augmented underwriting processes can help gather, analyze, and highlight critical internal and external data points to facilitate risk differentiation. This, in turn, can help enable automated coverage and pricing recommendations throughout the lifespan of a policy, enhancing the client experience and laying the foundation for truly intelligent digital insurance transformation.

The AI-Based and Black Box Underwriting Ecosystem

An AI-based underwriting system is based on algorithmic infrastructure. Automated underwriting decisions are made based on the available data, with third-party data used to augment and validate risks and predefined rules applied using a digitized risk submission process. Massive internal and external data sets are leveraged to create a sophisticated, real-time risk profile. The technology stack for this infrastructure can include multiple tools and software applications, from AI, Robotic Process Automation (RPA), and Natural Language Processing (NLP) to Optical Character Recognition (OCR), and Application Programming Interfaces (APIs).

AI-based underwriting processes can help uncover hidden but relevant data assets that significantly impact the insurance lifecycle, helping identify ambiguous risk parameters and enabling improved risk selection and categorization, accurate pricing, and better client experience. AI can enhance the quality, relevance, and analysis of data by:

  • Facilitating entity resolution to reduce false positives, correct errors, and reduce inconsistencies.
  • Using machine learning (ML) algorithms and keyword-based intelligence to make data consistent and usable, generating valuable insights. These insights can then be implemented across the insurance lifecycle to adjust risk assessments and coverage based on new developments for better protection of the policyholder.
  • Transforming unstructured, semi-structured, and structured data into actionable information to develop a deeper understanding of risk exposures and facilitate decision-making.
  • Reducing application timelines with AI/ML-based underwriting applications that use a large dataset to identify possible claims and premium-seeking exposures, requiring minimal input from policyholders.

The Case for Adoption of AI-based Underwriting

AI underwriting has gained momentum in recent years due to its ability to analyze large amounts of data and generate insights that would otherwise be inaccessible. Some of the specific benefits of this approach include:

Ability to handle large volumes of data
Large sets of data can be aggregated using AI applications in different formats. Being able to handle such a volume of data is essential for any underwriting exercise, and AI applications can apply pre-set identifiers and models, as well as identify relationships between them. If the results look promising, an underwriter can review them before making a final coverage decision.

Operational flexibility
AI can give your company a competitive advantage by enabling it to be more flexible in its decision-making processes. Difficult market situations, like those presently facing insurers, are an ideal time to adopt AI-based underwriting. It has been observed that adopting AI technology is essential as it enables insurers to operate more efficiently and effectively, refine risk appetite, and improve risk assessment. These changes can lead to reduced operational costs and improved response times.

Innovation and disruption
AI-based underwriting solutions can help insurers in the emerging technology landscape and act as a digital catalyst, helping them stay ahead of the curve and better anticipate what their customers are looking for. In addition, AI-based underwriting solutions can also help insurers provide better customer service and support, as they’ll be able to use these insights in real time while interacting with their customers.

Regulatory compliance
The regulatory environment is ever-changing, and with the speed of technology, there is an increased imperative to adapt quickly without compromising security or customer experience. AI-based underwriting allows insurance organizations to quickly adapt to new regulations while maintaining existing policies, procedures, and controls. So, when something changes in the market risk assessment and pricing models can be seamlessly updated accordingly.

Technological disruption
Rapid advancements in technology are forcing businesses across the insurance industry to be more forward-thinking and agile. Leveraging AI can help these organizations stay ahead of the curve by allowing them to make informed decisions based on new or previously inaccessible data quickly.

Competitive differentiation and improved risk selection
In a competitive market, insurers must differentiate themselves from their rivals. The best way to do this is by offering a better experience for the insured, especially within the SMB marketplace. AI-based underwriting can provide better analysis and results quickly. With AI-based underwriting, carriers can determine if an applicant is eligible for coverage within minutes and make risk selection decisions faster than ever before.

Customer-centric approach
Leveraging tools like AI allows insurers to provide a better experience to policyholders by having a complete picture of a customer’s information, needs, and preferences in a centralized location. This common, shared database ensures that the customer’s data is accessible and available for future reference, reducing the number of times they will be asked to provide the same information to the carrier. This approach also helps ensure appropriate coverage for the SMB client and improves rate adequacy for the carrier by having a holistic view of the account.

The insurance industry is transforming, and AI-based underwriting is an essential step toward technological advancements. To unleash the full potential of the AI and Black Box underwriting systems, including faster and better decision making, improved operational and cost efficiency, and improved policyholder experience, insurers must identify new data sources and be willing to implement new solutions and redefine business processes. Working with strategic technology partners with deep industry and technical expertise and a track record of successfully implementing customer-focused and cost-effective solutions is a critical first step.

Gagan Gupta is AVP, Underwriting at Xceedance

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