AI in Underwriting: A Game Changer at the Core of the Insurance Industry

To most consumers, buying insurance is still a painful, lengthy process. High touch, manual workflows on the insurers’ end create friction and frustration. As McKinsey notes, despite insurance companies’ substantial investments over the past several years in digitizing customer onboarding and policy binding, progress has been slow and incremental, and for many companies it has fallen short of expectations. One particular area in which most companies have failed to meaningfully modernize is underwriting. 

Underwriting involves researching and assessing the degree of risk each applicant or entity brings to the table before assuming that risk; establishing appropriate premiums to adequately cover the true cost of insuring policyholders; accurately pricing the investment risk; and ensuring a profitable outcome for the firm even in the case of claim submission in the future​.  

For most insurance companies, these tasks are currently the most time-consuming and resource-heavy. Much of the purchasing journey remains analog and manual due to the legacy technology stack in most companies. Some insurers take up to 30 days to decide on an insurance policy applicant in a paper-based process that often relies on inadequate information, and is prone to human errors and biases that often result in mispriced premiums​.

Major challenges in underwriting

According to a recent survey, insurance executives are prioritizing digital innovation in underwriting, with over 50% claiming that digitization and automation in this domain are the most important areas for their companies to grow in. Despite their awareness to the importance of automating processes, in reality, insurers are still struggling with several key challenges in implementing digital transformation:

Burden of legacy systems. While many insurers have progressed from fully manual processes to rule-based automation of underwriting workflows, these platforms are far from offering a real end-to-end, automated process. Insurers still face entrenched, legacy technology which prevents them from employing truly agile approaches to optimize the operational efficiency of onboarding and risk assessment, improve pricing and increase customer satisfaction.

Shift in consumer behavior and expectations. Consumers expect financial service providers to deliver the same swift, convenient, and transparent digital experience that they are used to receive from other digital service providers. This is especially true in light of the rise of InsurTechs, which are dramatically disrupting the industry, introducing mobile-first, purely digital experiences and super-speedy offers. Indeed, more than 50% of consumers think that traditional insurance companies are lagging other industries in both responsiveness, technology tools and personalized products​.

Fragmented customer data. Current manual processes and point solutions often rely on inadequate and siloed customer data. While data is increasingly recorded and stored, it is usually fragmented and cannot be harnessed to either simplify and streamline current underwriting, or enhance the understanding of risk to enable more refined, granular categorizations. The result is often mispriced premiums that weigh heavily on insurers’ bottom lines.

The benefits of AI in underwriting 

As a function of the utmost importance in the insurance value chain, insurers investing in AI to augment and streamline their underwriting businesses will ultimately gain a sustainable competitive advantage. AI-based underwriting solutions enable insurers to: 

Optimize risk and pricing. AI widens the scope of data sources that underwriters can use for their evaluations. Big data analytics allow deeper visibility into customers’ risk profiles, personally tailoring premiums to match each individual’s actual risk. Beyond delivering hyper-customized offerings, this also enables insurers to optimize their pricing.

Offer a quick, frictionless customer experience. With consumers increasingly expecting near real-time service in their digital touchpoints, the ability to drastically shorten underwriting workflows from several weeks to an instant can be a game changer for the industry.

Improved profitability. By bringing all these capabilities together, the AI-based automation process improves underwriting profitability, while reducing operational costs, customer churn, and costs for retaining customers. 

End-to-end AI underwriting platform

While most leading insurance companies have already started their journey towards digital transformation, most of them have so far failed to digitize their underwriting offerings at scale. This is mainly due to taking a conservative approach to digitization. According to McKinsey, “The traction of many companies’ accelerated or automated underwriting programs has been limited, largely because insurers have taken a cautious, incremental approach to scaling automated decision making. These companies opt for small improvements to their risk frameworks and processes rather than considering the potential to rebuild and take a more modern approach to underwriting.”

McKinsey postulates that underwriting transformation requires a new mindset of technology adoption — that will digitally enhance underwriting, end-to-end.

To do so, an AI-based underwriting platform must demonstrate the following capabilities:

Data capture. Data processing lies at the very heart of the insurance business.​ Insurers produce and receive massive volumes of new information every day to make decisions, manage operations and create value.​ AI-based data capture (also referred to as IDP — Intelligent Document Processing) is a next-generation solution for extracting data from complex, unstructured documents. IDP uses advanced machine learning and computer vision capabilities that can handle document complexity with far more accuracy than current ​OCR (Optical Character Recognition) tools.​ Powered by the latest AI capabilities, insurance data capturing solutions can process customer data at far greater speed with improved accuracy and fewer false positives. In addition, AI-based data capture transcends the current data silos inherent to underwriting, creating synergy between data from multiple sources to enhance the analysis and understanding of each customer’s risk profile.

Visual assessment. AI-based visual assessment employs Object Recognition capabilities in the process of appraising assets and properties. By analyzing images of properties, AI models can take into account factors that impact the insurance premiums for each particular asset, such as physical condition, materials and types of products, the property’s surrounding, etc. These AI capabilities can also be used for analyzing satellite imagery, giving insurers a broader perspective on the property’s environment.

Risk management. AI-based risk assessment verifies the data entered by both clients and agents, cross-checking this data with external and internal databases, and supporting the process of optimizing each customer’s risk score.​

Pricing. By bringing all these capabilities together, AI-based end-to-end underwriting solutions are designed and built to provide the optimal pricing scheme for each insurer according to their unique context and attributes. AI-powered underwriting creates strong synergies between relevant visual and textual data, combined with a machine-learning based risk assessment process, resulting in an optimized offer for each customer, preventing mispriced premiums, and improving overall profitability.

The BeyondMinds solution 

The BeyondMinds end-to-end enterprise platform enables insurance companies to transform core business processes to human-machine automation, adapting to specific business needs. As a fully customizable end-to-end platform, it covers all underwriting processes with relevant AI capabilities, from data capture through visual assessment and risk management, to pricing optimization. All of BeyondMinds’ AI solutions, across use-cases and verticals, are built on top of a modular universal platform, enabling each customer to deploy its personally tailored AI solution in just weeks, no matter which business they’re in.

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