Underwriters and the Commercial AI Advantage...Underwriters and the Commercial AI Advantage Training...

Underwriters and the Commercial AI Advantage

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Introduction 4

New solutions are needed 6

AI at the ready 10

Non-technical primer on AI 11

AI at work for Business Owners Policies 12

Action plan for commercial underwriting 15

1. Submission 16

2. Segmentation 16

3. Assignment 17

4. Risk Assessment 18

5. Coverage Recommendation 19

Better judgment over time 20

Measuring progress 21

Speed: Faster response times at scale 21

Accuracy: Lower loss ratios 22

Combining for growth 22

The view from here 23

Bibliography 24

Biographies 25

Table of contents

4

In the complex world of commercial underwriting, underwriters play the pivotal role in decision-making—not algorithms. Yet underwriters are faced with a challenge unlike many other professions: they must be quick yet always accurate. In commercial insurance, this dichotomous goal is becoming harder to balance as pricing pressure, speed requirements, and talent scarcity1 converge.

Introduction

4

5

Insurance CEOs agree that they must look to artificial intelligence (AI) for help2, but AI adoption remains low for commercial lines, despite increasing investments in insurtech for other areas3.

Here we will investigate how commercial insurers can address these challenges by rapidly operationalizing AI to empower underwriters. With examples from new underwriting business submission, we show how AI systems can be used to:

automate decisions, allowing underwriters to start time-sensitive work sooner and allocate more time to risk-associated work; and

recommend decision to underwriters in a timly manner to help them accomplish non-automated work more efficiently and accurately.

For insurers that succeed in augmenting underwriters with AI, these capabilities will lead to faster broker response times as well as improved Loss Ratios (LR).


1 The Jacobson Group and Ward Group, “Insurance Labor Market Study,” 2018, https://www.insurancejournal.com/app/uploads/2018/02/industry_labor_mkt_study_summary_q12018-final.pdf.2 Strategy Meets Action, “AI in P&C Insurance: Pragmatic Approaches for Today, Promise for Tomorrow,” White Paper, July 2018, https://strategymeetsaction.com/assets/Complimentary/SMA-AI-in-PC-Insurance-2018.pdf.3 “Global InsurTech Investment Surpassed the $2bn Mark for the First Time,” FinTech Global (blog), November 14, 2018, http://fintech.global/global-insurtech-investment-surpassed-the-2bn-mark-for-the-first-time/.

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Commercial insurance underwriting is notable for its decision-making complexity. Underwriting clerks and underwriters must navigate a sequence of steps to achieve a final quote, including:

Submission: capturing complete and accurate insurance data.

Segmentation: assigning applications into risk groups.

Assignment: prioritizing and assigning applications to underwriters.

Risk Assessment: assessing specific risks for each application.

Coverage Recommendation: assembling coverage options to be offered.

New solutions are needed

7

At every step, accurate information helps reduce uncertainty around decision-making — but the cost of obtaining, validating, and analyzing information adds up fast, and underwriters must increasingly quote quickly or not at all.

Meanwhile, the challenges of speed and complexity are growing in today’s insurance landscape.

Even top insurers are operating near 100% Combined Ratio (CR)4 and experienced underwriting talent remains scarce5.

Meanwhile, existing technology solutions are doing too little to help staff keep up6. To maintain accuracy, speed is often sacrificed first.

4 Shawn Moynihan, “The Top 100 P&C Insurance Companies in 2017,” PropertyCasualty360, July 18, 2018, https://www.propertycasualty360.com/2018/07/18/the-top-100-pc-insurance-companies-in-2017/.5 Denise Johnson, “Insurance Industry Rethinking Recruitment Strategies,” Insurance Journal (blog), January 27, 2017, https://www.insurancejournal.com/news/national/2017/01/27/440212.htm.6 Shaun Dodson, “Focus: Underwriting Intelligence at the Point of Decision,” Insurance Day (blog), April 29, 2018, https://insuranceday.maritimeintelligence.informa.com/ID1121987/Focus-Underwriting-intelligence-at-the-point-of-decision.


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New solutions are needed

Direct premiums written growth (%) Combined ratio (%)

2018 predicted combined ratio for all P&C lines was 99.0%AI can help reduce the combined ratio7

6.3 101.3

3.1 97.8

3.0 90.9

5.3 100.2

0.0 98.0

7.3 107.8

1.5 101.7

0.2 102.3

3.3 73.5

3.1 44.4

1.1 94.3

0.0 93.8

2.8 104.5

2.6 97.1

5.1 89.8

8.2 104.1

4.1 99.0

Commercial auto

General liability

Accident and health

Medical professional liability

Commercial multiperil

Private auto

All personal lines

P&C total

Aircraft

Homeowners

All commercial lines

Fire and allied lines

Worker’s compensation

Farmowners

Marine lines

Fidelity and surety

Mortgage guaranty

7 Tim Zawacki, “US P&C industry’s Q3 underwriting loss narrows on better auto, home results,” S&P Global Market Intelligence (blog), November 20, 2018, https://www.spglobal.com/marketintelligence/en/news-insights/latest-news-headlines/48294052 Denise Johnson, “Insurance Industry Rethinking Recruitment Strategies,” Insurance Journal (blog), January 27, 2017, https://www.insurancejournal.com/news/national/2017/01/27/440212.htm.

9

As a result, most insurers regularly lose out on policies that they could have written, given more time to process. This is not a sustainable position for insurers, nor is it good for the economy overall. Over time, innovation and growth can suffer when businesses are unable to insure at the speed, coverage, and price they need8.

Insurers now recognize the potential of AI to help with these challenges, but operationalizing AI for the full complexity of commercial underwriting presents unique challenges. AI performs at or above human-level accuracy in many tasks, but 100% accuracy is unheard of. Continued demand for personalized service in commercial insurance also discourages full underwriting automation.

8 Emmett J. Vaughan and Therese M. Vaughan, [En anglais] «The Insurance Device,» in Fundamentals of Risk and Insurance, 11th edition (Hoboken, New Jersey: Wiley, 2013).

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Modern AI is ready to augment commercial underwriters because it’s not just one tool or capability set, but a whole new approach to creating software. To show how to take advantage of three distinct benefits of this approach, we explore how AI systems can help one high-value example in the field of commercial insurance: Business Owners Policy underwriting.

AI at the ready

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Non-technical primer on AI

AI is not just for automation.

The core strength of AI is to transform data into useful predictions9, and AI systems can operationalize predictions either by automating or informing decision-making10. In many contexts, the greatest business valuable is achieved when automation and information are balanced based on context-specific details.

For underwriters, three features make this approach valuable:

AI for automation alleviates underwriters to spend more time focusing on higher-value activities, such as building relationships with brokers or applying expert judgment to higher-risk cases.

AI for informing leaves a final decision to underwriters, but provides suggestions and visualizations that help underwriters make decisions faster and with greater confidence.

AI learns how to make predictions using data about underwriting decisions made in the past. By preserving and scaling this judgment reflected in these decisions through automation and information, AI systems help make the collective judgment of underwriters more explicit, durable, and scalable.

All three benefits can be seen in the following example of AI for Business Owners Policies.


Data AI

?

??

Predictions

Automation

Information

Artificial intelligence systems transform data into useful predictions that can be applied for automating or informing work

9 Ajay Agrawal, Joshua Gans, and Avi Goldfarb, “Prediction, Judgment and Complexity: A Theory of Decision Making and Artificial Intelligence,” Working Paper (National Bureau of Economic Research, January 2018), https://doi.org/10.3386/w24243.10 Shoshana Zuboff, In The Age Of The Smart Machine: The Future Of Work And Power (New York: Basic Books, 1989); Jannis Kallinikos, “The ‘Age of Smart Machine’: A 21st Century View,” in Encyclopedia of Software Engineering (Taylor and Francis, 2010), https://www.taylorfrancis.com/books/9781351249263/chapters/10.1201%2F9781351249270-99.

12

Small business insurance accounts for ⅓ of the $23 billion commercial insurance market in Canada11, and the market continues to grow in the U.S12. However, Business Owners Policies (BOP) — the primary tool for delivering insurance to small businesses — are becoming more challenging for underwriters to write due to the rising complexity of options available.

This presents an opportunity for AI to help BOP underwriting by reducing the time and cost of assessment using role augmentation. To achieve this goal, an AI system would first need to be trained to predict what risks features are key for a new BOP submission. This prediction can then be operationalized in different ways.

AI at work for Business Owners Policies

11 Deloitte Canada, “Insurance Re-Imagined: Unlocking the Small Business Opportunity to Help Canadian Insurance Carriers Grow,” February 26, 2018, https://www2.deloitte.com/ca/en/pages/financial-services/articles/insurance-reimagined.html.12 Ryan Smith, “Commercial Insurance Rates up in Q4,” Insurance Business (blog), accessed March 13, 2019, https://www.insurancebusinessmag.com/us/news/breaking-news/commercial-insurance-rates-up-in-q4-121183.aspx.

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A R RecommendAutomate

Decision Automation Decision Recommendation

When risk is predicted to be low, and the AI system has high confidence in its prediction, straight-through processing can be achieved by directly issuing quotes to the broker.

When risk is predicted to be low, but prediction confidence is also low, the AI system could automatically order reports, such as inspection reports, to help improve its prediction confidence.

When risk is predicted to be high, and prediction confidence is also high, the AI system could automatically send a notice of denial-to-quote to brokers.

The AI system can generate multiple recommendations on quote options, allowing underwriters to choose. For BOP, this could mean providing a coverage option with cyber insurance and one without.

The AI system signals to commercial underwriters which key data points are most significant in determining level of risk.

When risk is predicted to be high, the AI system can make a recommendation to reject the application, but leave the final decision to underwriters, and for him to communicate it to the broker.

A

A

A


Training DataApplication data from previous BOP submissions, linked with data describing what risks were assessed and how it was done for each submission

PredictionWhat risks should be assessed, and the method (how), for a new BOP submission

R

R

R

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This example relies on the underwriting judgment of past decisions, allowing underwriters to reinforce that baseline of judgment criteria, and to gradually improve it over time. The example also demonstrates that just because a high-quality prediction could automate a task, doesn’t mean that it always should. Underwriters could declare, for example, that no application should be rejected automatically by the AI system. This becomes a business decision, where, for example, the relationship with the broker is key and human interaction is needed.

By configuring thresholds for how AI is operationalized from case to case, underwriters can maintain control over the conditions when each option is used. Over time, the ongoing training that occurs as underwriters use this system would also improve the accuracy of the underlying prediction, allowing the system to balance prediction operationalization in new ways.

AI at work for Business Owners Policies

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For each sequence of decisions in processing a new submission, we explore how AI could be operationalized to augment underwriters. In each case, we present hypothetical options based on AI capabilities today.

Action plan for commercial underwriting

Submission

Segmentation

Assignment

Risk Assessment

Coverage

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Current State Improved underwriting process with AI

Commercial underwriters segment applications into groups by reviewing submission informa-tion, including original document attachments.

Suggests how applications could be categorized given certain assumptions, for example by extracting features from digitized submission data.

Some submissions never get digitized or must be declined due to high volume.

Processing time decreases. No submission is declined due to time constraints.


Underwriting clerks manually transcribe most documents and manually cross-check for completeness.

Many digitization solutions exist, but most rely on data standards that are not widespread in commercial underwriting. Some data are never standardized, like resumes of principals for errors & omissions.

Submissions are often transferred to underwriters before digitization and validation are fully complete. For speciality lines, brokers frequently bypass data entry entirely to receive a preliminary indication from an underwriter.

For challenging submissions, AI can flag a clerk to review and also suggest a best path to shortcut the task.

Flags submissions predicted to have missing or erroneous information, such as those requiring additional forms.

Digitizes and structures most form data, including handwritten documents, images, and custom broker forms.

If authorized to do so, AI automatically requests forms from brokers/customers.

Some submissions never get digitized or must be declined due to high volume.

Processing time decreases. No submission is declined due to time constraints.

A

A

1. Submission

2. Segmentation


R

R

R

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Dedicated underwriting staff analyze submissions to assign and triage cases. Assignment depends on factors like underwriter specialization, current portfolio and workload, past business with a broker, and more. In commercial property, for example, assignment rules may depend on region.

Most of these factors will be assessed by hand by an experienced member, or automatically put into team logs for underwriters to self-assign.

Colour-codes or flags submissions to help underwriters assign and prioritize cases, for example based on expected processing time, or need for additional forms.

Assigns cases to underwriters based on a variety of factors, including estimates of expected processing time and underwriter specialization, workload, and signing authority.

Assignment is not formally optimized. It carries an opportunity cost as time spent assigning cases displaces time spent validating and analyzing submissions.

AI continuously optimizes submission assignment while retaining the ability to explain its assignment rationale or change on the fly. Underwriters start long-running cases sooner, and they can focus on processing instead of triaging applications.


A

3. Assignment


R

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Using data from multiple sources, such as historical risk data for a geographic region, underwriters determines what specific risks an applicant has and whether these can be covered.

Each submission requires a unique review strategy due to factors like account size or presence of unique risks. For directors & officers, factors such as financial profile of the company require unique assessments and discovery of external data. Underwriters must also discover any requirements for endorsements to assign in final quotes.

Suggests information sources that were useful for similar applications in the past, or suggests a ranked order in which to review data sources. These recommendations help underwriters prioritize information from submissions and outside sources such as databases.

Suggests endorsements that might be necessary or optional for the final quote.

Information overload slows down even the most experienced underwriters. Under time pressure, underwriters sometimes can’t review as much information as desired, leading to less accurate risk assessment.

Commercial underwriters can focus more time on the risk assessment portion due to automation in previous steps. AI also helps underwriters zero in on impactful information more quickly, increasing risk assessment accuracy.


4. Risk Assessment


R

R

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Underwriters build coverage options by assessing policy needs of the customer as well as risk exposure of the existing book of business. If an insurer declines to quote, they must be able to explain why.

For Commercial Property, multiple additional endorsements may be needed. This may require the underwriter to prepare multiple combinations of coverages for the broker.

Recommendations often require multiple, time- consuming revisions as brokers and customers request adjustments.

Recommends a menu of coverage options for underwriters to explore.

Recommends similar-looking submissions for underwriters to compare while quoting.

Learns from decisions and rules on how to quote low-risk premiums over time.

Coverage recommendation can be more of an art than a science. Revising recommendations also poses a significant opportunity cost: to alter a quote, underwriters lose time they could spend on other business and also risk losing an application due to longer than needed processing times.

Underwriters prepare thoughtful coverage options more quickly and with greater insight into coverage decisions. This allows underwriters to exert greater control over how and why they design coverage recommendations over time.

A

5. Coverage Recommendation


R

R

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Better judgment over time

In a mature configuration, AI systems will grow and refine use cases over time, and also optimize the system of predictions overall.

For example, AI would use data describing what predictions were made at each submission step; as well as data describing which coverage options were ultimately offered, adjusted, declined, or accepted; then the system could tune predictions made at different stages in the overall process.


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To verify when AI is helping with the challenges of speed and accuracy, insurers can look at two key metrics: (1) broker/customer response times and (2) loss ratios (LR).

Speed: faster response times at scale

At each step, even small role augmentations help reduce overall processing time. Digitizing submission data instead of manually transcribing it speeds up processing.

Additional time savings are unlocked as new solutions are trained, like automated case assignment, or application triaging that ensures roadblocks are noticed and cleared early.

With greater control over their time, commercial underwriters can also better specialize in specific lines of business or with particular brokers.AI systems are also uniquely capable of scaling benefits like these without dramatically increasing cost. Once an application has been developed, the cost of horizontally scaling it is low.

Measuring progress

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This applies both to automating and informing applications. A strong AI infrastructure for underwriting serves to extend underwriting guidelines into a digital domain, creating good judgment that scales with business and staff.

Accuracy: lower loss ratios

As faster response times allow underwriters to assess more submissions, underwriters must still judge which should be approved and how. AI sharpens this decision-making in two ways.

First, faster response times should grow the pool of customer submissions for underwriters to select from when deciding whether to. Today, insurers are not able to assess all submissions provided to them. By assessing more applications over time, they can select with greater choice. Loss Ratios improve when insurers can exercise greater discretion over what they underwrite.

Second, for this larger pool of assessed applications, AI augmentation creates breathing room for underwriters to dig deeper and assess more accurately.

As prediction power improves over time, underwriters can quote applications that previously appeared too risky and reject applications that previously appeared safe13.

Loss Ratios improve again when underwriters can focus better, using more impactful information, to design coverage recommendations.

Combining for growth

For customers, greater speed and accuracy in underwriting combine to change the cost dynamics of obtaining insurance. As underwriters increase throughput and spend more time building relationships with brokers, the customer’s and broker’s journey improves. Faster, more accurate, and more meaningful underwriting transactions translate into brokers sending more submissions.

The payoff of these effects — processing more applications faster, increasing assessment precision and accuracy, and attracting more customers over time — is a healthier book of business. This benefits insurers, brokers, and customers alike.

13 Ajay Agrawal, Joshua Gans, and Avi Goldfarb, “Prediction, Judgment and Complexity: A Theory of Decision Making and Artificial Intelligence,” Working Paper (National Bureau of Economic Research, January 2018), https://doi.org/10.3386/w24243.


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We believe that AI is an essential tool for the intelligent insurance firm of the future. In the world of commercial insurance underwriting, this tool has been held back by the inherent complexity of decision-making. Insurers can now move forward by deploying AI to center and enhance the role of underwriters, who remain the deciding factor for underwriting success.

When underwriters are empowered with a mixture of predictive capabilities provided by AI, they will push the boundaries of what they can achieve for customers and insurers alike.

As the industry evolves, insurers who lock in these benefits early will quickly pull ahead of competitors who do not. The rise of AI and related technologies will no doubt increase the efficiency of insurers incrementally, but only insurers who empower underwriters will attract new customers and maintain decision quality at speed.

The view from here

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Ajay Agrawal, Joshua Gans, and Avi Goldfarb, “Prediction, Judgment and Complexity: A Theory of Decision Making and Artificial Intelligence,” National Bureau of Economic Research, January 2018.

Deloitte Canada, “Insurance Re-Imagined: Unlocking the Small Business Opportunity to Help Canadian Insurance Carriers Grow,” February 26, 2018.

Denise Johnson, “Insurance Industry Rethinking Recruitment Strategies,” Insurance Journal, January 27, 2017.

Emmett J. Vaughan and Therese M. Vaughan, “The Insurance Device,” in Fundamentals of Risk and Insurance, 11th edition, Wiley, 2013.

Jannis Kallinikos, “The ‘Age of Smart Machine’: A 21st Century View” In Encyclopedia of Software Engineering, Taylor and Francis, 2010.

Ryan Smith, “Commercial Insurance Rates up in Q4,” Insurance Business, January 7, 2019.

Shaun Dodson, “Focus: Underwriting Intelligence at the Point of Decision,” Insurance Day, April 29, 2018.

Shawn Moynihan, “The Top 100 P&C Insurance Companies in 2017,” PropertyCasualty360, July 18, 2018.

Shoshana Zuboff, In The Age Of The Smart Machine: The Future Of Work And Power, Basic Books, 1989.

Strategy Meets Action, “AI in P&C Insurance: Pragmatic Approaches for Today, Promise for Tomorrow,” White Paper, July 2018,

The Jacobson Group and Ward Group, “Insurance Labor Market Study,” 2018,

Tim Zawacki, “US P&C industry’s Q3 underwriting loss narrows on better auto, home results,” S&P Global Market Intelligence (blog), November 20, 2018,

Bibliography


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Carlos BenfeitoHead of Insurance Products With a career spanning over 17 years working at insurance-based institutions like AXA and Intact, and founding insurtech startup Allai, Carlos brings a passionate commitment to building role-centric AI products designed to augment decision-making for strategic roles within the industry, ultimately empowering organisations to improve their workflow capability and provide unparalleled customer service.

Charles DugasInsurance AI Lead Dr. Charles Dugas is a seasoned insurance veteran with over 25 years of Insurance, AI and Analytics knowledge. He founded Element AI’s Insurance practice in early 2017 to help Insurers deploy cutting-edge AI products that augment role productivity and deliver against strategic revenue goals. He holds a B.Sc. in Actuarial Science, a M.Sc. in Electrical Engineering, and a Ph.D. in Machine Learning with Professor Yoshua Bengio, a leading authority in AI: known as one of the ‘Godfathers of Deep Learning’.

Charles MuratUnderwriting Product Specialist

Charles is passionate about insurance, underwriting, and helping insurance companies improve efficiency and quality of customer experience through technology. He brings his underwriting expertise to Element AI’s insurance team to build AI-powered products that augment decision-making for key roles within the insurance industry. Charles holds a B.B.A. in Finance from HEC Montréal. Since his graduation, Charles has worked in underwriting, for two iconic canadian companies, EDC and The Guarantee, before joining the insurance team of Element AI.

Cory SalvesonAdvisory and Enablement

Cory has worked in technology for knowledge management, human learning, and social data science for over a decade. He has also conducted social science research in artificial intelligence and lifelong learning with his alma mater, University of Oxford. At Element AI he researches and writes about AI in industry. Cory holds a B.Sc. in Multidisciplinary Studies from the University of Minnesota, and an M.Sc. with Distinction in Social Science of the Internet from the Oxford Internet Institute.

Biographies


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Contact [email protected]

Underwriters and the Commercial AI Advantage...Underwriters and the Commercial AI Advantage Training...

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Transcript of Underwriters and the Commercial AI Advantage...Underwriters and the Commercial AI Advantage Training...