Best Practices for Leveraging Business Analytics in Today’s and Tomorrow’s Insurance Sector

8/8/2019 Best Practices for Leveraging Business Analytics in Todays and Tomorrows Insurance Sector

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Best Practices for Leveraging Business Analytics in

Todays and Tomorrows Insurance Sector

Mark B. Gorman

Principal, Mark B. Gorman & Associates LLCJanuary 2009

Sponsored by

Executive Summary


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Report Coverage

It started as most things do, rather innocently a simple research project on the use o business analytics in

the insurance industry. Te three gentlemen on the research interview call were adamant. We know we havea ways to go, but were getting there. Were making progress. Weve come a long way. My question in return

was straightorward: How do you dene there and how will you know youve arrived? Ater several seconds

o silence one o them said, Tats a good question ...

Tis report is an attempt to answer that question based on interviews in late July, August and September

o 2008. Te interviews were with 15 thought leaders in business analytics in the insurance industry. Tese

individuals, all with signicant experience in starting, growing, using or managing business analytics in their

organizations, shared their insights and expertise in describing the cultural, organizational, technological

and personnel challenges and changes required or their organizations to more broadly adopt and utilize

business analytics.

My goal is not to describe what the insurance market is using business analytics or, but how the use o

business analytics is changing the insurance market, and what organizations need to consider to make that

transormation more powerul, more palatable and more protable. While the intellectual stimulation, the

challenges to traditional thinking and the enthusiasm or the topic were undoubtedly the interviewees, the

conclusions that ollow are mine.

Background

Protable growth is an elusive goal in todays increasingly competitive insurance industry. Rapid development

and deployment o new products and product eatures, balancing broader distribution channel opportunities,

managing risks across the organization, responding to increasingly demanding regulatory and reporting

agency demands, and providing more precise pricing levels require efective decisions to be made with greater

accuracy, eciency and transparency. Personal experience, while still valuable, is oten insucient in meeting

the requirements or consistent, accurate and efective decisions in a rapidly changing marketplace.

Leading organizations are increasingly turning to business analytics as a key means o survival. Business

analytics solutions are being used by insurers to reduce the time required to react to competitive pressure, to

respond eciently to market changes, to increase efectiveness o business managers in improving nancial

results and driving value or the organization, to more efectively manage the many risks the enterprise aces,

and to improve the precision and eciency o operational decisions. Yet, ar-reaching, enterprisewide

programs are still an anomaly. Cultural issues are oten the reason or limited enterprise programs. Why

and what can be done are the ocus o this report.

Best Practices for Leveraging Business Analytics in Todays and Tomorrows Insurance Sector


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For this report, the primary forms of business analytics include (see Figure 1):

Ad Hoc Management Reporting and Dashboards. Tese are business analyticsa.

solutions where analysis and reporting tools are used to provide automatic eedbackon the achievement o key perormance criteria. Tey are also used to do ad hoc

reporting on data rom a variety o data sources in order to improve managements

ability to make better and aster decisions. Common examples include claim

reporting and settlement lag time, call center response times, achievement o service

standards, etc.

Profling and Segmentation. Tese business analytics solutions involve data miningb.

to determine the historic behavior o a group, or the perormance o a grouping

o people, risks or transaction types. Common examples include customers by

proftability, claim types by severity or requency, customers by product preerence, etc.Forecasting. Tis business analytics solution allows an insurer to attempt toc.

determine a time series estimate o what will happen in the uture based on statistical

evaluation o current and historic aggregate data.

Predictive Analytics. Tis business analytics solution attempts to predict utured.

behavior or perormance based on an analysis o historic transactional data, third-

party data (like loss history, motor vehicle, geodemographic data, credit data, etc.)

or derived data oten calculated rom one or more data elements. Te analysis oten

results in a score or recommended action being assigned during the processing

o a transaction. Examples include: determining the loss ratio relativity o a riskbeing underwritten, the pricing adequacy based on anticipated loss experience, the

propensity or raud on a reported claim, etc.

Optimization. Tis business analytics solution ocuses on optimization o businesse.

decisions usually based on multiple scenarios or multiple predictive analytics models.

For insurance, optimization is almost always constrained optimization. For example,

maximizing response to a direct response campaign, constrained by a marketing

budget.


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Figure 1: Primary Forms of Business Analytics: Data Insight and Business Value

LOW

HIGH

Data Insight

Required

Business Value Derived HIGHLOW

In the ollowing paragraphs, I use the terms line o business and unctional units quite oten. As most

readers will know, the granularity o the term line o business difers rom organization to organization. Forone insurer, line o business may mean personal lines insurance. For another, personal lines auto and personal

lines homeowners are both lines o business. Since the granularity matters less than the amount o autonomy

granted to most line-o-business units in insurance, Im reerring to the broad concept that encompasses

both examples. Te same holds true or the term unctional units. Examples o unctional units in insurance

include claims, underwriting, marketing, nance, etc. Here too, unctional units may operate very autono-

mously rom one another. It is the autonomous nature o these operations that Im reerring to below.


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Summary

Te use o business analytics continues to permeate the insurance marketplace. New tool capabilities, new

business models and new data sources are constantly emerging. Te successul organizations will be thosethat concentrate as much on the successul implementation and deployment o business analytics as on the

technology. For these rms, creating a culture where thought leadership has an equal place with operational

excellence, and where business analytics solutions are both centralized and decentralized within the organi-

zation, will be the ocus. Te ranchise model provides a straw man or having this discussion internally by

removing the either/or elements rom the discussion. Finally, those organizations that develop job amilies

or analysts and that can recognize and compensate based on contributions toward success will be the

most successul in attracting, hiring and retaining the resources required or uture success.

o access the entire research report, please go towww.sas.com/gormanresearch .

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Best Practices for Leveraging Business Analytics in Today’s and Tomorrow’s Insurance Sector