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The Rise of the Exponential Underwriter

How this role solves a critical business problem

Commercial and specialty underwriting has always been an art and a science. But as technology has advanced and made it possible to include more data points, underwriters now spend nearly half their time getting the data in good shape. With different underwriting processes, tools, and systems across each line of business, and constant chair swivelling, does it feel like the art is playing second fiddle to the science?

Commercial risks are complex – it takes time to analyse risk and determine pricing, to comply with underwriting guidelines, to track exposure, to communicate with brokers and customers expertly and efficiently. All while trying to create a common appetite and go-to-market across the organisation.

 

Let’s face it, it’s a pretty tough gig for CUOs today as underwriting performance is under the spotlight:

How can they perform accurate risk assessments rapidly to report better loss ratios?

How can they significantly reduce costs while maintaining or improving underwriting quality?

How can they generate new business and retain existing customers to increase underwriting profits?

Data, Data
Everywhere

While CUOs evaluate the best ways to respond to the evolving customer and streamline their operations, they’ll be looking at how they improve their strategy, governance, technology, culture, and talent. Yet arguably the area with the single biggest impact to making better decisions quickly, is being able to cut through the data noise to deliver real insight.

Insights rely on data, and in our industry we’ve got a lot of it! The volume of data is simply staggering. But bigger doesn’t necessarily mean better. While the industry is awash in data, getting the right mix of available data to accurately assess and price the risk is a challenge. Where previously historical data was enough to underwrite risk, insurers today need to move from ‘hindsight’ to ‘foresight’ and to understand the impacts of risks in real time.

Accenture describes this, in The Future of the Chief Underwriting Officer report, as a reverse problem to the
data iceberg. Historically a small amount of information was on the surface and the vast scale resided below, but today there is a massive amount of new data sources on the surface. And like a fish looking at a vast sailing ship, it sees part of the hull but not the entire ship above.

For underwriters to differentiate and act promptly they need to be on the front foot, but many still struggle with:

 

Siloed and inaccessible data

New and existing data sets are fragmented and reside in different places.

No single customer view

Tasks at individual, team and risk level aren’t consolidated.

Slow, manual workflows

Underwriters continue to sink time into non-value-add inefficient tasks.

Multiple data formats

Submissions are made in a myriad of formats: structured and unstructured,
internal sources and third-party portals.

Landlocked by legacy estates

Slow, cumbersome, inflexible platforms that are restrictive and a drag on speed and innovation.

Lack of confidence in governance procedures

Lack of clear audit trail and reporting makes it hard to expand the portfolio safely.

Even in large commercial lines today, anywhere from 30 to 40 % of an underwriter’s time is spent on administrative tasks, such as rekeying data or manually executing analyses.

– McKinsey & Co

The Rise of the Exponential Underwriter

For many underwriters, the data explosion provides both a challenge and an opportunity. Solving complex risks problems is a highly sophisticated process that relies not only on deep and extensive first-hand experience but also on mining and interrogating multiple, disparate data sources. Evolved underwriters that can take advantage of emerging technologies (AI, machine learning, RPA etc) to leverage real-time data will become more valuable to their clients and companies.

It is what Deloitte describes as the exponential underwriter: a multiskilled professional who will take the use of alternative data and advanced technology to a whole new level while enhancing their role and becoming more strategic.

Definition:
New data sources and advanced technologies are expected to increasingly supplement yet also augment human underwriters to a degree never seen before. As part of the future of work, the exponential underwriter will leverage emerging tools, information, and skill sets to focus on higher-level challenges and become more strategic in defining the future of the company to enhance business performance and shareholder value.

So where does an exponential underwriter start?

Reinventing the role of underwriting to become insight-driven has never been more important, but where does a CUO start? A good first step is overcoming the nervousness that emerging technologies will replace the underwriter. While the underwriter of the future will look different to today, in commercial and specialty insurance there will always be a need for processing complex cases using deep professional judgment and gut instinct to make better risk decisions. The science will never replace the art.

Indeed, the game-changer is the combination of traditional skills with modern data-driven approaches. Openly embracing new technologies may well provide the answer to the challenge many face: how can they free up critical resource to focus on what they do best, assessing risk.

 

Opportunities abound to use data and technology solutions to automate and streamline the value chain. From intake to risk assessment, to pricing and binding there are many examples of how advanced technology can help an exponential underwriter:

Robotic process automation that takes over mundane, repetitive tasks.

Artificial intelligence to rapidly and accurately extract data from submissions.

Machine learning for SOV and bordereaux to iteratively improve the model.

Deep learning that spots patterns in data that can assist with improving the rating.

Natural language processing that reads documents and contracts such as MRCs.

Advanced technologies and data are already affecting distribution and underwriting, with policies being priced, purchased, and bound in near real-time.

Using AI to create a virtuous circle of data

While modern Underwriting Workbenches automate admin-heavy work and eliminate painful rekeying – undoubtedly one of the biggest benefits is how this technology easily grapples with the challenge of seamlessly integrating new data sets.

It’s estimated around only 30% of insurers are currently fully leveraging big data for underwriting, and actuaries are still wrestling with effecting models in many cases.

For underwriting teams, the panacea is to create what Accenture calls the “self-reinforcing virtuous circle” of data. A loop of actions whereby results allow the loop to be repeated with ever increasing results. Benefits are simple and compelling: less errors, faster time to market, greater profitability.

“Underwriters in the future can make informed decisions in minutes by leveraging gigabytes of data and complex models in the background.”

– KPMG

Becoming an insight-driven organisation

To be hyper-relevant in today’s market and grow in current and future markets, carriers must prioritise insight-driven underwriting. Those that take an enterprise underwriting approach and a strong technology strategy to constantly evolve data will be at the forefront. A modern CUO needs to be both a technology trailblazer, and a data master. Fortunately, they don’t need to do this alone.

Insurers no longer have to accept the current approach as inevitable or inflexible. Harnessing the power of modern technology, data and advanced analytics can help them regain control and underwrite smarter. They can be nimble, fast, future focused. They can be exponential underwriters.

References:

1. Insurance productivity 2030 | McKinsey
2. The Future Chief Underwriting Officer | Accenture
3. The future of insurance underwriting | Deloitte Insights
4. Enabling-the-future-of-underwriting | KPMG

 

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