Digital Insurance: How Technology Is Making Changes In Risk Analysis For Insurance

Carriers, pressed by lower expense insurtech models that emphasize direct-to-consumer engagement and new, rich data sources, are beginning to encounter a bifurcation of risk and client characteristics. As the insurance evaluation and quoting landscape becomes more commoditized, sophisticated models utilizing new sources of data are pushing upmarket in both the personal and commercial insurance spaces.

Consequently, risk profiles are starting to follow a barbell distribution pattern — some risks are more measurable, more easily quantifiable, and therefore require less human intervention in pricing and evaluation. Other risks and client profiles, such as multinational policies in the commercial world, fall into a more complex profile and need a significant operational lift, requiring increasingly sophisticated and deep operational resourcing by carriers.

These divergent risk prediction profiles will accelerate hybrid human/AI models for risk prediction, claims handling, customer relationship management, and one-off major loss handling. The changing risk landscape will accelerate hybrid and AI technology adoption, while catastrophic loss measurements will continue to require deep human expertise.

Within this emerging risk landscape, third-party partners will be key to increasing profit margins for more measurable risk products. ‘More measurable,’ risk is characterized by higher-volume and more frequent instances for which there is more available data. Insurers are identifying new ways to collect increasing volumes of information, disparate data sources that automatically feed into the information being collated. And when IoT or telematics is involved, the pace and depth of data processing increase by several orders of magnitude. Increased quantity and quality of data-generated insights can lead to more risk mitigation as well as reducing the frequency and severity of claims. Risk pooling may be tested since it can be challenging to continue to hold low- and high-risk ‘units’ in the same class.

On the other hand, the less measurable risk is characterized by more severe and uncertain instances. Examples include breaches in cybersecurity, natural catastrophes, and even Covid-19. This model emphasizes solutions, including risk reduction and mitigation, that can expand insurers’ typical role of supporting risk transfers and paying claims. These types of risks will allow carriers to expand the consultative risk management role with clients and will be a potential major source of fee income in the future.

The industry has progressed along this bifurcation path already, but there is still a significant roadway ahead in terms of getting closer to the endgame with both models, as well as a model for a single carrier that can effectively bridge both types of risk. In the ‘more measurable’ risk space, many insurtechs have targeted specific niche insurance markets that tend to generate large amounts of structured data.

Specifically, low-margin personal auto, renters, and some small commercial property markets have seen increasing adoption of pure risk-based pricing (or pricing for one). However, these startups typically face two key challenges: scale across markets because of regulatory hurdles; and systemic risk because of flawed channeling into niche risk pools, which could lead to capital adequacy issues due to adverse selection or black swan events.

To address this changing risk landscape, the insurance industry will need to rely on an infusion of people with technical literacy as well as soft skills to manage customer relationships. As a result, insurance firms will have to get creative to attract high-potential talent over other industries such as financial services and tech — a topic we’ll cover in a future article. Additionally, roles will change for both more and less measurable risk functions, requiring modified organizational structures, roles, skills, and distribution of talent.

Customer centricity will lead the way for future organizational designs in both more and less measurable business units.

In a broader sense, carriers will likely need to segment the business into models that can interface directly with clients — both personal and commercial — either via lower-cost intermediaries that utilize additional data or via highly technical internal support groups for more complex operational needs or less measurable risks.

A single insurer can support both ‘more measurable’ and ‘less measurable’ risk models, but segmenting the operational resources used to support each and ring-fencing the cost structures to understand a true cost to serve for each segment will be critical in establishing profitable divisions. An insurer embarking on this journey will need to thoroughly examine each portion of its organization to ensure that those that focus on ‘more measurable’ risks are utilizing low-cost partners and direct-to-consumer engagement models to compete with the increasing commoditization of that business.

For larger carriers that already have the scale and expertise needed to operate across different markets and regulatory environments, different challenges emerge. Typically, these carriers have been more focused on ‘less measurable’ risks and find the challenge of developing a different operating model to address the more analytical- and AI-driven risks daunting (and sometimes impossible to fit within the preexisting corporate structure). Additionally, as pricing is more and more driven by AI, carriers must retain end-to-end transparency and audit trails on pricing decisions to ensure they are treating customers fairly.

We observe regulators in other areas are starting to hold executives to account for fair pricing in personal lines. We expect that this will soon move into the small and medium-sized enterprises space, and over time into commercial coverage. Carriers should be able to explain the pricing decisions — a task easier said than done for insurers with complex processes and pricing driven by AI.

Within the company, divisions that focus on ‘less measurable’ risks will need to ensure that the talent is skilled in and equipped with digital and analytical tools. And that the business model is oriented toward fee-based consultative services, as opposed to high volume, low touch policy administration. A carrier that can make this transformation can be competitive in both segments, but it will require discipline and a willingness to tolerate different business models across the organization.

Ultimately, a carrier that can bridge both more and less measurable risk segments may potentially see clients grow from simple, measurable risks to more complex, multi-product risks, making the combination of both segments worthwhile.

Inmediate is an insurtech startup from Singapore that is using the latest technology such as Artificial intelligence, Distributed Ledger, and NLP, making insurance processing and underwriting fast, cheap, and flexible. That gives for better processes, lower costs, improved time to market, and new revenue opportunities.



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