Accenture - Risk Analytics Study; Insights for the Insurance Industry (2012)

8/21/2019 Accenture - Risk Analytics Study; Insights for the Insurance Industry (2012)

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Accenture Risk Management:

2012 Risk Analytics Study

Insights for the

Insurance Industry


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2

Contents


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3

Executive Summary 04

Introduction: Risk Analytics in the Insurance Industry 10

Drivers of Risk Analytics in the Insurance Industry 14

Risk Analytics: Making the Investments 16

Maturity of Current Risk Analytics Capabilities 20

in the Insurance Industry

Looking Ahead: Growing More Mature Risk Analytics 26

Capabilities Through Better Integration

Conclusion: Driving Growth and Better Compliance 30

Through Risk Analytics


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4

Executive Summary


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Our 2012 Risk Analytics Study—conducted by Accenture RiskManagement—surveyed more than 450risk professionals (see sidebar, “Aboutthe Research”) across several industrysectors to assess the support for, andmaturity of, risk analytics technologies,tools, processes and talent. Many of thecomponents of risk analytics are familiarto insurers—life insurance companies

as well as property and casualtyinsurers—because they include actuarialtools, business intelligence techniques,reporting, data warehousing and othertraditional technologies.

However, in recent years , moresophisticated analytics approaches andtools have become available in areassuch as risk aggregation, exposure andinvestment concentration, reinsurancemanagement and capital calculations.These developments mean that an

insurance company now has theopportunity to achieve competitiveadvantage through risk analytics.

Across all industries studied, supportis strong for analytics as a means tomitigate risks more effectively, thoughthe patterns of responses from thosesurveyed show that the risk analyticsfield is, in many respects, still inits infancy in terms of its practicalimplementations across these industries.

Companies are investing in riskanalytics and intend to increasethose investments, yet the potentialreturn is often stifled by inconsistentor incomplete data. This preventsorganizations from generating theinsights needed to support a morepredictive approach to risk management.

Sixteen percent of the surveyedcompanies ranked their risk analyticscapabilities as industry leading.Although this is a survey-basedself-assessment, further analysis by

Accenture comparing the data from thisgroup (“Leaders”) with that of the other84 percent of the survey group

(“Laggards”) has confirmed that theseleaders are indeed ahead of the pack—both in the results achieved and in thedistinctive capabilities of an advancedrisk analytics practice.

For example, 83 percent of leaders, butonly 54 percent of laggards, indicate thatthe use of risk analytics has significantlyimproved the quality of decision making.Conversely, one in five of laggardssay their use of risk analytics has notimproved decision making, whereasonly one in fourteen of the leaders(7 percent) has the same difficulty.


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One reason for these findings appearsto be that, among the leaders, specificanalytics tools are better integratedinto decision-making processes.For example, 58 percent of leaderssay stress testing is integrated withstrategic decision making for largeprojects, while only 34 percent oflaggards say this is so. Risk reporting

is also more mature among leaders:71 percent note that risk reports aregenerated and used by operationsand senior management, comparedwith only 50 percent of laggards.

The Accenture 2012 Risk AnalyticsStudy has found that many challengeslie ahead for organizations looking toachieve distinctive capabilities in riskanalytics. What is consistent across thesurveyed groups, however, is that all seerisk analytics as an area that can delivercompetitive differentiation.

Summary of Cross-Industry FindingsThe industries studied vary widely intheir business challenges and strategicgoals, so risk analytics takes differentforms across the different companies.However, based on analysis of the data,Accenture has identified five commontrends across the industries studied.

1. Investments in riskanalytics are increasingand executives expectongoing developments inthis area.

Executives are supportive

About 95 percent of the surveyed

companies are currently using riskanalytics. About half (49 percent) areusing these techniques in a coordinatedway across the company while the otherhalf (47 percent) are implementingsolutions in pockets within particulargeographies or business units. Theprimary applications of risk analytics arefor risk based capital, managing credit,and business strategy.

Sixty-five percent of respondents saythat management use and acceptance ofrisk analytics within their organizationis either excellent or above average.Risk analytics leaders are especiallysuccessful in this area: 62 percent rankthemselves as excellent when it comesto management use and acceptance ofrisk analytics, compared with only

21 percent of laggards.

Investments are increasing

In the past year, 87 percent oforganizations increased theirinvestments in analytics technologiesfor managing risk; 58 percent of thoseincreased their spending more than10 percent, and 14 percent increasedinvestments more than 30 percent.Over the next two years, the vastmajority of organizations anticipate

that their investments in risk analyticswill continue to increase. Investmentsare expected to focus mainly ondata quality and sourcing, systemsintegration and modeling. These findingsare generally consistent across theindustries studied.

Risk analytics leaders are more likelyto have significantly increased theirinvestments. Among the leaders,28 percent have increased theirinvestments by 30 percent or more,

while only 12 percent of laggards havemade that level of investment.

2. The maturity of riskanalytics is uneven acrossessential capabilities andfunctions, so the valuebeing achieved is not yetrobust.

Few companies assess their riskanalytics capabilities as industryleading

Although about half (46 percent) ofthe organizations surveyed rate theirrisk analytics capabilities as beingabove average, only 16 percent ratethemselves as best in their industry.About one-fourth of companies acrossthe industries studied are not even usingrisk analytics in their organizations atthis time.

More than half of organizations(57 percent) say that risk analyticssignificantly improves decision making.However, in terms of specific analyticstools, 62 percent of respondents saythat scenario modeling and stresstesting tools are either not being usedor are only made available to executives,rather than being integrated into

strategic and tactical decision making.As noted earlier, risk analytics leadersare distinguished from their peers intheir ability to drive better decisionmaking from their analytics capabilities.

Some components of thetechnical environment are stillimmature

When asked to rate the maturity oftheir various specific risk analyticscapabilities, the lowest scores (poor

and fair) were in software (13 percent),systems integration (12 percent),and data quality and sourcing (12percent). These areas are likely to havethe greatest impact on risk analyticscapabilities, processes and solutions.

Risk analytics leaders exceed their peersin the maturity of almost all of thesetechnical components. With systemsintegration, for example, 40 percentof leaders describe their capabilities

as excellent, compared with only 16percent of laggards. Similar disparitiesexist between leaders and laggards in anumber of other capabilities:

• Business rules development: Leaders,51 percent claim excellence; laggards,19 percent.

• Modeling: Leaders, 52 percent;laggards, 18 percent.

• Software: Leaders, 44 percent;laggards, 15 percent.

• Reporting and dashboarddevelopment: Leaders, 44 percent;laggards, 17 percent.


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3. Data consistency is asignificant challenge.In general, data availability is nota major issue: Only 7 percent ofrespondents cited a lack of dataas one of their challenges. Theproblem, instead, is often one of dataconsistency, rooted in the inability to

integrate analytics and insights acrosssiloed divisions and functions, severelycompromising the effectiveness of theoverall risk management capability.

Of all respondents, 40 percent determinetheir risk analytics data requirementsby collecting data only pockets withinthe firm. Just 27 percent of thosesurveyed have a fully aggregated viewof risk across their organizations. Thisinability to look at risk broadly and in

an integrated fashion has, potentially,several negative implications forinsurers. In commercial property andcasualty (P&C), for example, simplyunderstanding exposure by industrycan be difficult because policy systemscould be based on Standard IndustryClassification (SIC) or North AmericanIndustry Classification System (NAICS),while investment and credit systemsare often based on the Global IndustryClassification Standard (GICS).

Leaders show significantly moreadvanced capabilities in data quality—completeness, accuracy and consistencyin both producing and collecting data.Fifty-four percent of leaders describetheir capabilities in data quality andsourcing as “excellent,” compared withonly 19 percent of laggards.

More than 54 percent of leadersnote that they are able to take a fullyintegrated view of risk aggregatedacross models, while only 22 percent oflaggards claim this capability.

Laggards are more likely to have troublewith siloed data. Forty-four percent saythat data about risk events is collectedin pockets internally within the firm,while only 23 percent of leaders say thisis so.

4. Risk analytics iscurrently more preventiveand reactive thanpredictive.Only about one-third of companiesstudied (36 percent) say their riskmanagement capabilities are proactive

and strategic; 46 percent say theirapproach is primarily preventive; andalmost one in five (18 percent) saytheir risk management capabilitiessupport merely reactive responsesto events. Spending also reflects therelative scarcity of proactive riskmanagement: The allocation of riskresources, across all industries, isprimarily for preventive activities.

Far greater percentages of leaders areapt to say they use analytics in fraudprevention—82 percent, compared withonly 52 percent of laggards. Leaders alsolink analytics to business strategy moreeffectively: 79 percent say they useanalytics in setting business strategy,while only 60 percent of laggards do so.

5. Lack of expertise inrisk analytics looms as animportant challenge.

Most organizations (71 percent) builttheir current risk analytics capabilitiesin-house with support from outsidevendors and/or consultants. Twenty-nine percent do not rely on any externalspecialists, rather they use internal staff.However, only 19 percent of companiessurveyed rank their staffing capabilitiesas “excellent.”

These findings underscore the factthat analytics is a relatively new field,and that optimal talent sourcing and

development are not yet in placeat many firms. Organizations needto consider how best to meet thatchallenge, whether it is acceleratedinternal development, better hiring ormore comprehensive external sourcingand collaboration, even on a managedservice basis.

Risk analytics leaders tend to bechallenged less by staffing andcapability issues. Only 11 percentof analytics leaders are challengedby a lack of skills to develop riskmodels, for example, comparedwith 24 percent of laggards.

Research from the Accenture Institute

for High Performance (“Counting onAnalytical Talent,” Accenture 2010) hasfound that analytics talent at manyorganizations is not developed andnurtured effectively. Many companiesdo not manage analytical talent asa distinct and valuable workforce.Analytics specialists are oftenscattered throughout departments;many companies do not have a clearpicture of who their analysts are orwhere they reside organizationally.From an enterprise-wide perspective,organizations often have difficultymaking analytics (and its valueproposition) understandable andactionable to executives and companyprofessionals less familiar with theanalytics field.

Companies also struggle with how tostructure an analytics team in termsof whether it should be centralizedor decentralized. Research fromthe Accenture Institute for High

Performance revealed that companiesthat want to build a strong analyticsworkforce are best served by greatercentralization and coordination oftheir analytics talent. Doing so ensuresthat analysts are working “close tothe business” on the most importantinitiatives and also “close to oneanother” to coordinate their effortsand to promote mutual learning andsupport. A centralized approach alsohelps organizations give analysts thekind of meaningful work and careeropportunities that are critical to theirengagement and retention.


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Making the rightinvestmentsThe Accenture 2012 Risk AnalyticsStudy has found strong support foranalytics across several importantindustry sectors, but also reveals thatmany components of the analyticsfield are still growing in maturity. Ingeneral companies should be looking tomake focused investments along threedimensions in particular: technology,people, and organizational structuresand processes.

Advancements need to be made inareas such as modeling and testingbut, as our study clearly found,investments in capability developmentwill be equally important. As analyticsgrows in importance, especially within

the risk function, better approachesto talent sourcing, developmentand retention will be essential,especially as the value of top talentbecomes clearer to companies.

The range of risks to which anorganization is susceptible is increasingin scope and severity; events in theexternal world—natural disasters,political upheaval and economic crises—should heighten stakeholders’ awarenessof systemic risks, which can only be

addressed with a more holistic andintegrated approach to data gatheringand analysis.

However, as our study found, riskanalytics is rarely integrated acrossfunctions and business units—a problemthat can be addressed by looking athow different groups interact andcooperate. As organizations advancetheir analytics capabilities they shouldbe looking for the interdependencies

and not get trapped in siloed views orsingle-dimension structures. This moreholistic approach could require upgradesto current data governance capabilities.

Modeling and testing tools areimportant, but only if they areincorporated into business processes,especially processes for decisionmaking. The ability to leverage analyticstechnologies to generate timely andrelevant business insights dependson working to support behaviorchange and new ways of working.

If tools are available but are notincorporated into workflows, theywill likely have minimal impact.

The challenge for all institutions is theproper focusing of their risk analyticsefforts. There is hardly a major companyanywhere that is not actively involvedin the field of analytics. But companiesneed more than just new tools. Whatthey actually require is more insightfuland timely information to make more

effective decisions that drive businessvalue. The human element—andthe leadership element—are alwaysessential. It is too easy to get lost in thevolumes of data, and be romanced bythe power of technologies and tools. Itis equally important to simply know howto ask the right questions. Generatingmeaningful insights, and harnessing thepower of analytics to anticipate risksbefore they arise, depends ultimately onknowing what you are looking for.

We hope the findings of this researchwill spur discussion and furtherreflection. Please contact me [email protected] formore information.

Steve Culp

Managing DirectorAccenture Risk Management


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35%

28%

20%

9%

8%

Region

Note: Due to rounding, figures may not total 100%

Europe North America China

ASEAN (The Association of Southeast Asian Nations)

Japan/South Korea

40%

22%

19%

19%

Industry


Banking Insurance - P&C

Insurance - Life Chemicals

Breakdown of respondents by region Breakdown of respondents by industry

32%

16%28%

25%

Revenue


Greater than $10 billion $5 billion to $10 billion

$1 bil lion to $5 bill ion $100 million to $1 bil lion

8%

14%

16%

38%

23%

Role


C-Level Executive (CEO, CFO, COO, CIO, CMO, CRO)

Senior Vice President, Executive VP or VP

Managing Director, Senior Director, or Director

Senior Manager or Manager

Other (Analysts, Technicians, Actuaries, Underwriter, etc.)

Breakdown of respondents by revenue Breakdown of respondents by role

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About the Research

The 2012 Risk Analytics Study—conducted by Accenture RiskManagement—is based on a survey of 465 managers and executives

from all major geographic regions. Respondents were from theinsurance, banking and chemicals industries and all held corporatepositions in which they were responsible for developing or utilizingindustry-specific analytics capabilities.

The purpose of the study was to assess the relative maturityof risk analytics methods, tools, technologies and processes;to determine the effectiveness of those factors in drivingbusiness, customer and market insights to support betterdecision making; and to identify current trends.


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Introduction: Risk Analytics in theInsurance Industry


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Base size: Total sample.

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How do you characterize the importance of risk analytics in your organization?

2% 1% 1%

23% 24% 28%

18% 17%

23%

57% 58%48%

All Industries Banking Chemicals2%

22%

17%

58%

Insurance -

Property &

Casualty

6%

14%

16%

65%

Insurance -

Life

Use of risk analytics has significantly improved

the quality of decision making and enables

proactive risk monitoring

The organization currently uses risk analytics

techniques but has not seen a major change in

decision making

The organization recognizes the need for risk

analytics and plans to use it

Do not see a need for the use of risk analytics

in the organization

Figure 1

Significant percentages of insurance firms agree that risk analytics has improved decision-making andrisk monitoring

Facing ongoing regulatory andeconomic pressures, a great manyinsurers are looking to improve theirrisk management capabilities. Propertyand casualty (P&C) insurance companiesare seeking to improve their abilityto respond quickly to risk events and,even more important, to anticipatethem before they occur. Life insurers

and annuity providers—who must dealwith the long tails of their portfolioand the ongoing ramifications of thefinancial crisis with its low interestrates and reduced investment returns—are working to define new products,optimize their overall portfolio andimprove asset performance.

The possibility of taking proactivesteps to avoid issues and stay ahead ofeconomic, regulatory and market eventsis becoming more of a reality due toadvances in risk analytics—quantitativeand qualitative tools and techniquesdesigned to estimate the impact andfrequency of specific risks, and todrive positive business outcomes. Riskanalytics technologies and approachescan augment more traditional reporting,business intelligence and datawarehousing capabilities with newertechniques in modeling, claims analyticsand fraud analytics.

But are firms effectively takingadvantage of risk analytics to meetinsurance industry challenges, drivegrowth and achieve competitiveadvantage? Are they moving beyondbasic analytical applications concernedmostly with data management to thosethat can enable predictive action andeven real-time response? (See sidebar,

”Climbing the Ladder.”) To answer thosequestions, Accenture Risk Managementhas completed a global risk analyticsstudy capturing and synthesizing theinsights from more than 450 analyticsprofessionals across three industries onhow they use risk analytics to tackleindustry challenges, market volatilityand business opportunities.

Moving up the risk

analytics maturitycurveIn general, fairly low percentages ofinsurance firms in our survey currentlyassess their risk analytics capabilitiesas leading edge. Only 16 percent ofP&C companies rank their capabilitiesas among the best in their industry,while 26 percent of life insurers assignthemselves that rating.

However, insurers show strongcommitment to improving theircapabilities in risk analytics. They areinvesting in analytics to improve theirrisk management capabilities, andstrong majorities intend to increasethose investments in the near term.

Firms are already seeing results. High

percentages of insurance firms agreethat the use of risk analytics hassignificantly improved the quality oftheir decision-making processes and hasenabled more proactive risk-monitoring.This is especially true with life insurers:65 percent affirm the value of riskanalytics, compared to the surveyaverage of 57 percent. (See Figure 1.)


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Figure 2

Insurance companies are challenged by a range of technology and process issues in the area ofrisk analytics

Survey data and Accenture experiencepoint to several challenges thatinsurance companies share withorganizations in other industriesin improving their risk analyticscapabilities. (See Figure 2.)

• Enhancing quantitative and qualitativerisk management techniques and

capabilities to meet regulatoryrequirements such as Solvency II.

• Inconsistent planning and executionof risk management across multiplebusiness processes.

• Integrating analytics and insightsacross multiple data sources, andsiloed divisions and functions.

• Lack of expertise and skilledresources, which can cause delays innew product releases and result in

project overruns.

• Harvesting and managingdata across the enterprise,due in part to ineffective datagovernance, poor data qualityand insufficient data integrity.

• Lagging analytics technologies, withcompanies not yet reaping the fullbenefit of IT advancements.

• Inability to communicate results andinsights effectively.

A number of specific capabilities inrisk analytics are important to meetingthese challenges, and some of oursurvey results address these capabilities,including modeling, data managementand reporting, as well as theeffectiveness of firms’ current systemsand technologies.

However, it is important to rememberthat analytics is a means to an end;that end is the delivery of insightsand timely information to make moreeffective decisions that drive businessvalue. So, a key to our study wasexploring not only data-gatheringprocesses and capabilities, and notonly technologies and tools (because

it is easy to get lost in the data andenamored with the tools), but alsoissues related to skills and to particulartechniques such as modeling andstress testing that can help properlymanage and direct an analyticscapability toward meaningful results.

For your organization, to what degree do the following challenges impede the effectiveness of risk analytics processes?


Medium + High impact All Industries Insurance -Property &Casualty

Insurance -Life

Banking Chemicals

Lack of systems integration 71% 78% 66% 74% 63%

Embedding risk analytics intomanagement processes

69% 78% 66% 70% 59%

Measuring and monitoring expected/predicted outcomes vs. actual

66% 74% 55% 68% 62%

Availability and quality of internal/external data

66% 72% 56% 71% 60%

Lack of robust modeling 65% 67% 60% 68% 62%

Outdated legacy systems 67% 66% 57% 75% 65%

Finding the right skilled staff to developthe models

70% 65% 65% 75% 68%

Migrating capabilities across customersegments/lines of business/functionalareas

66% 64% 60% 73% 58%

Lack of budget 64% 64% 65% 64% 61%

Developing real-time analytics 68% 63% 62% 71% 72%

Lack of management buy-in 57% 62% 56% 61% 45%

Lack of easy access and use of analyticsinformation

61% 59% 61% 65% 54%


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Reprinted by permission of Harvard Business School Press. From ”Analytics at Work: Smarter Decisions, Better Results” by Thomas H. Davenport, Jeanne G.

Harris and Robert Morison. Boston, MA 2010, p.83.Copyright © 2010 by the Harvard Business School Publishing Corporation; all rights reserved.

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Although the field of analytics has not yet reached full maturity, ithas been in practice long enough to ask how effective such solutions

are—especially when it comes to managing and mitigating risks.As Jeanne Harris and Tom Davenport put it in their recent book,Analytics at Work , there is a “ladder” of analytical applications thatincreases in sophistication and value as companies move up each rung(see graphic). The bottom rung of the ladder is focused on gettingthe data right. Toward the top of the ladder are more predictivecapabilities, ultimately arriving at a situation where analytics enablesoptimal responses to be embedded in processes, leading to real-timeoptimization of performance.

Prediction and differenti-ated action embedded inprocess

Optimal responseembedded in real-timeprocess

Real-time optimization

Institutional action

Predictive action

Differentiated action

Key targets/segments

Data in order

Predictions of responseby target/segment

Key targets and segmentsdefined

Different approaches fordifferent targets/seg-ments

Well-defined, common,clean, and integrateddata

Ladder of analytical applications

Climbing the Ladder


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Drivers of Risk Analytics in theInsurance Industry


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What are the business needs thatinsurers hope to address throughbetter risk analytics capabilities? Ingeneral, ongoing cost pressures arepushing carriers to manage capital moreefficiently, but to do that, they wantto be more confident about their riskexposure. Effective risk managementcan enable a carrier to free up capital.

Lessons from the most recent globalfinancial crisis weigh heavily here:According to Accenture analysis,some carriers encountered trouble atthat time because of unexpected riskconcentrations in their investments.Cost can be an element, as well. Withongoing budget pressure, efficient useof reinsurance is another importantfactor in the equation.

Some regional differences also exist.Based on Accenture experience, manyfirms in the Asia Pacific region arefocused on growth and are competingfiercely for market presence, requiringa reconsideration of their businessstrategies. In Europe, companies areunder intense regulatory pressure. Inboth cases, a transformation of thecompany’s operating model in lightof risk is important, and analytics is akey enabler of that operating modelto facilitate regulatory compliance orgrowth in market share.

Dealing withregulationOne of the most important factorsfueling the interest in risk analyticsis improving compliance capabilitiesand dealing more effectively withan increasingly complex regulatoryenvironment. In our experience,the Solvency II directive has been a

focus area for insurers across mostEuropean geographies. Even given thecurrent postponement of the SolvencyII implementation, firms shouldconsider ongoing investments in theirquantitative and qualitative capabilitiesto achieve Solvency II readiness.

Although Solvency II applies toEuropean firms, our analysis indicatesthat there is a more pervasivemood across the global regulatoryenvironment that will put additionalpressure on the insurance industryeverywhere to provide policies similar tothose in Solvency II, as a kind of qualitycheck for insurance groups. The key

building blocks of Solvency II—capitalcalculation models, governance, OwnRisk and Solvency Assessment (ORSA),internal controls and supervisoryreporting/public disclosure norms—arebeing adopted by an increasing numberof regulators across the globe.

Driving growthFirms are coping with a complex andever-changing external environment.Insurers are managing financialinstruments that are often complexand that have a high degree ofvolatility. Hence, firms often want tomeasure and monitor these risks on anongoing basis to foster alignment withtheir risk appetite. Risk managementcan be more effective if integratedinto the strategic decisions of the

firm including management of largeprojects. Our survey indicates thatfirms realize the importance of riskanalytics for a variety of businessprocesses including investmentselection, risk selection and pricing,fraud management and loss reserving.

Risk analytics can support moreprecise calculations of risk exposureto support growth. If insurers havemore sophisticated and well-developedanalytics methods, and if the enginesare generating more accurate andrepeatable results for risk capitalmeasures, firms can use these resultsfor more effective pricing, productdevelopment and capital management.The combination of risk-adjustedmetrics, traditional asset and liabilitymanagement, and profitabilityperformance measurements can providethe company with a more balanced viewof business performance.

In our experience, insurers’ effortsto expand and grow across businessunits and geographies can often resultin inconsistent risk managementprocesses. The processes needed toidentify, analyze, assess, report andrespond to risks related to specificareas of responsibility can be complex.In many cases, a consistent riskmanagement framework—which canserve as the basis for execution of riskmanagement throughout the entirecompany—is not in place. This cancreate a situation in which individualsinvolved in different processes do notuse the same standards and data forrecognizing risks and then assessing and

responding to these risks once they areidentified. A standardized, integratedapproach to risk management for allbusiness processes—avoiding duplicativeprocesses and unnecessary activities—can help minimize the costs associatedwith inconsistent execution.

Transforming theoperating modelFirms can benefit from a company-wideintegration of risk management so thatinformation used to assess risk in onecore insurance process is available toassess other business opportunitiesfrom a risk/reward perspective.Integration can also support the abilityto comply with new regulations thatoften increase the burden on insurers

to provide complete and transparentdocumentation of all processes thatbear risks.

Firms should also think aboutintegrating risk considerations morewidely into decisions regardingoperations, capital management andmanagement processes. This approach—called a “risk-adjusted operatingmodel”—can help companies managetheir enterprise-wide risks and aligntheir risk management program withbusiness and regulatory concerns.

The risk-adjusted operating model,supported by effective analytics, candeliver numerous benefits to helpinsurance companies drive growth anddeal with regulatory pressure:

• Better value generation througha broader application of riskmanagement capabilities to supportdecision making and the pursuit of

business opportunities.• Improved business decision making

through alignment of overall riskappetite and business strategy,helping increase the efficiency ofcapital utilization.

• Improved business results throughintegration of risk and core insuranceprocesses and the alignment ofprocesses and systems.

• Enhanced operational efficiency

through an integrated and robustIT landscape, with a comprehensiveand common IT architecturefor multiple functions.


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Risk Analytics: Making the Investments


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Figure 3

Many insurers expect to invest in risk analytics at higher levels than other industries

Over the next two years, how does your organization expect investments in risk analytics to change?

28%

43%

14%13%

1% 0%0%

Banking Chemicals

Increase 0% to 9.9% Increase 10% to 19.9% Increase greater than 20% No change Decrease 0% to 9.9%Decrease 10% to 19.9% Decrease greater than 20%

19%

41%

25%

12%

2% 1%1%

All Industries

19%

28%

34%

13%

4%

1%1%

Insurance -

Property & Casualty

19%

36%

27%

14%

1%3%

0%

Insurance - Life

15%

49%

24%

10%

1%1%1%

The Accenture 2012 Risk AnalyticsStudy found that life insurers as wellas firms in the P&C insurance industryare investing in analytics capabilitiesto better identify, assess and mitigaterisks. In the past year, P&C insurers haveinvested more than any of the otherindustries studied: 23 percent of theinsurance companies surveyed report

investment increases of 30 percentor more over the previous year. Thisis in comparison to 16 percent of lifeinsurers and 14 percent globally acrossall industries.

Companies intend to continue thoseinvestments. As shown in Figure 3, 63percent of life insurers, and 62 percentof P&C insurers, foresee a rise of morethan 10 percent over the next two years.Compared with other sectors studied,even banking, greater percentages ofinsurers intend to increase risk analytics

spending more than 20 percent. Riskanalytics leaders tend to invest at higherlevels. For example, four out of 10 leadershave increased investments in riskanalytics by at least 30 percent, whileonly two out of 10 laggards increasedinvestments at that level.

These investment plans reflect the

longer-term, strategic perspectiveof the risk function in the insuranceindustry—especially for life insurers,where analytics (modeling, inparticular) is key to understandingmarket risk dynamics and their impacton the balance sheet and on theprofit and loss (P&L) statement.



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Figure 4

Analytics investments are expected to increase, especially in data quality and sourcing, softwareand modeling?

Over the next two years, in which areas does your organization expect to increase risk analytics investment spend?(Select all that apply)

53%

49%48% 49%49%

31%

55%

35%32%

40%

36%

25%26%27%

25% 23%

Insurance - Life Banking Chemicals

Data quality and sourcing Systems integration Modeling Software

Business rules development Reporting and dashboard development Management use and acceptance Staffing

50%

41%44%

46%44%

32%

26%

22%

Insurance - Property & Casualty

56%58%58%

50%

55%

39%

32%30%

In terms of specific capabilities, firmsare making strong investments toaddress data quality and sourcing,software and modeling. (See Figure 4.)These investments are integral tomeeting regulatory requirements(e.g., Solvency II, China InsuranceRegulatory Commission, Japan FinancialServices Agency) and pursuing a

variety of business opportunities.

Initiatives include:

• Advanced modeling capabilities, tooptimize the capital required to runthe business.

• Data management requirementscovering data modeling, metadatamanagement, data quality, dataarchitecture and security.

• Implementation of risk-adjusted

performance measures in a consistentmanner across different divisionsof the firm, to promote the use ofappropriate measures for steering,monitoring and reporting as well asfor operational decision making.

Regional investmentdifferencesSome regional findings reflectslightly different risk analyticsemphases. In North America,firms report that the highestimportance for risk analytics is in:

• Risk selection and pricing (63 percent)

• Fraud (61 percent)

• Investment portfolio optimization (50percent)

In Europe and Asia, firms reported thehighest importance of risk analytics for:

• Investment selection (67 percent)

• Fraud (64 percent)

• Loss reserving (59 percent)• Risk selection and pricing (58 percent)

North American firms see the primaryobjectives of risk analytics as moreaccurate underwriting (84 percent),better claims outcomes and fraudprevention (75 percent), calculationof economic capital (68 percent)and better prospecting decisions

(56 percent). Claims managementand fraud detection emerge as thehighest primary objectives for usinganalytics in both life and P&C firms inNorth America. Life insurers, however,place more emphasis on the use ofanalytics for regulatory complianceand pricing. Risk analytics also playsa major role in renewal decisions for

both life insurers and P&C firms.

In Europe and Asia, risk selection(71 percent), loss reserving (66percent) and risk quantification (52percent) are the top areas wherefirms use risk analytics capabilities.

Where are insurance firmscurrently focusing their riskanalytics investments?

In Europe and Asia, current spending

is highest for underwriting (33percent), investments (25 percent)and distribution (23 percent). Mostrisk analytics investments in Chinaare in distribution, while in Europe,Japan and South Korea investmentsare mostly in underwriting.



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Within the insurance functional area, where does your organization currently spend the majority of its risk analytics investments?

5% 3%8%

13% 17% 8%

13% 8%21%

17%

11%

25%

52% 61%

38%

All Insurance –North America

Insurance -Property & Casualty

Insurance - Life

Underwriting

Investments

Equal distribution of investment dollars

Claims

Distribution

Figure 5

North American organizations spend more than one-half of their risk analytics investments on underwriting,while distribution sees the least capital

In North America, almost 70 percentof firms are currently investingin risk analytics for underwritingand investment functional areas.Organizations spend more than one-half of their risk analytics investmentson underwriting, while distributionsees the least capital. Only one-fourth(26 percent) are currently investing in

analytics for claims and distribution.

However, risk analytics investmentsin underwriting among P&C firms arealmost double what they are for lifeinsurers. (See Figure 5.)

Over the next two years, risk analyticsinvestments for North American firmswill be especially focused on the areasof risk selection and pricing (76 percent).This finding underscores the need feltby many carriers to cascade the risk

analysis from the C-suite down to theindividuals in underwriting and sales.The opportunity is to tie risk selectionand pricing together so carriers canmore readily identify what their mostprofitable businesses are, and thenworking with distribution to find moreof those lines of business.

Other important investment areasinclude investment portfoliooptimization (54 percent), loss reserving(44 percent) and fraud prevention(43 percent). In Europe and Asia, acrossmost regions, most investments willcontinue in risk selection and pricing(60 percent) and investment portfoliooptimization (48 percent). Chinese firms

are distinctive in their intention to focusespecially on customer segmentation(53 percent) and loss reserving(57 percent).

What are some reasons for thesespending patterns? In China, forexample, insurers are currently focusedon growing their market share, sousing analytics capabilities to improvedistribution processes is key. Becausemost life insurers only have a localfootprint their exposure to market riskis limited; this may be one reason whythey do not perceive an immediate needto improve in the area of risk analytics.

In Japan, by contrast, the situation isquite different. There, P&C companiesare struggling with their Nat Cat(natural catastrophe) modeling andalso to adapt their economic scenariogenerators (ESGs) to reflect thecurrent volatility in the internationalmarkets and their increased market riskexposure. This situation calls for firms to

improve their risk analytics capabilities.

Base size: North America sample.


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20

Maturity of Current Risk AnalyticsCapabilities in the Insurance Industry


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21

Figure 6

Insurers in general are not confident in the maturity of their modeling capabilities

31%

24%

32%

23%

44%

24%

34%

18%

Banking ChemicalsInsurance - LifeAbove AverageExcellent


How would you rate the maturity of your organization’s risk analytics based upon the components of the risk

analytics process?


The Accenture 2012 Risk Analytics Studyasked insurers to assess their goals andcapabilities in several specific areasof analytics: modeling, stress testing,data management and reporting.

ModelingModeling plays an important role in

helping insurers comply with regulatoryguidelines and make eff icient capitalmanagement decisions that can improvethe bottom line. The proper use ofmodels can guide management teams ofinsurance firms to make more informedbusiness decisions and take timelyaction to mitigate possible losses. Moreeffective capital calculation modelscan help insurers meet their regulatoryrequirements and improve businessperformance by managing different

products and tracking performance bybusiness unit.

In general, insurers are not highlyconfident in their modeling capabilities:Only 55 percent of those participatingin our survey rated themselves eitherabove average or excellent. (SeeFigure 6.) Analytics leaders, however,realize the benefits that modeling candeliver in improving the efficiency ofcapital calculations. In Europe, Japan,

South Korea and China, for example,a significantly higher proportion ofanalytics leaders (76 percent) comparedwith laggards (46 percent) employ riskanalytics for capital calculation.

With regard to the selection ofmodels for deriving capital adequacyrequirements, our study found that 60percent of insurers would prefer usingan internal model. Several decisionsinfluence the preferred model forinsurers including the availabilityof historical data, implementationapproach and the analytical capabilitiesthat are available. However, given

the high cost and complexity ofimplementation, internal modelimplementation would requiresignificant resource commitments.Therefore, we anticipate that internalmodeling may be the preferred choiceof larger firms with more resources.Although the costs are higher, internalmodels can provide a better picture of

risk and can help firms plan their capitalrequirements accordingly.


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Figure 7

Most insurers in Europe and Asia prefer using an internal model

What is your preference with regard to selection of models for deriving capital adequacy requirements/internalgovernance/risk management?

All Insurance –Europe & Asia

ChinaInsurance -Life

Japan &South Korea

Insurance -Property &Casualty

Europe

We utilize an internal model

We utilize a standard model, but we plan touse an internal model in the future

We utilize a standard model

We have not yet made a final decision

Base size: Europe, China, Japan & South Korea sample.

5%

14%

81%95%

2%

17%

20%

61%81%

3%

15%

22%

60%

82%

4%

13%

25%

58%83%

7%

19%

33%

41%

74%

18%

19%

61%80%

2%

Another 22 percent of insurancefirms, according to our survey results,currently use a standard model butintend to move to an internal model inthe future. Sixteen percent preferred astandard model. These numbers aboutthe use of internal versus externalmodels were fairly equal across both thelife and P&C categories . However, some

regional differences exist. For example,high numbers of insurers in Europe andAsia (82 percent) plan to use internalmodels to derive risk, governance andcapital adequacy levels. (See Figure 7.)

Scenario modelingand stress testingScenario analysis can help insurers dealmore proactively with risk by helping

them assess the impact of differentpotential circumstances and responses.For example, in today’s rapidlychanging regulatory environment,insurers have a greater need forcapital. Scenario analysis enables amore structured assessment of thereduced levels of capital available togenerate income. Similarly, analytics

can help determine the impact ofchanges in underwriting policy on thebusiness’s long-term profitability.

Although stress testing has been inuse for many years, it is playing anincreasingly important role in helpinginsurance firms evaluate their riskexposure in different scenarios. In

the current volatile environment, thisevaluative capability is important.According to Accenture experience,stress tests have become a commonsupervisory tool by which a financialservices regulator can gauge a firm’sprofitability and solvency under varioussimulated, stressed economic conditions.

In view of the increasing importanceof scenario modeling and stress testingfor insurers, it was not encouragingthat the insurance firms participating

in our study (particularly P&C firms)scored below the global average forusing stress testing that is integratedinto strategic decision making for largeprojects. (See Figure 8.) As a capabilityand tool, our experience indicates stresstesting can play an important role in acompany’s strategic decision making.With the increased focus on stresstesting in the insurance industry, itsuse could change soon.

Data managementWithin the insurance industry, thesophisticated models that can fostermore effective risk management dependupon the availability of accurate andvalidated data, properly organizedand delivered with the right level ofdetail and granularity. The terabytes

of data accumulated by insurers eachday constitute an ongoing challenge,and firms are responding. As ourstudy found, data quality and controlshave emerged as prominent areasfor increased investment spendingby insurers. Commercial carriers, forexample, often have data challengeswith location concentrations, limits anddeductibles of their various risks.

Another data issue concerns the factthat Solvency II requirements and otherregulatory measures have cast a harshlight on the quality, comprehensivenessand timeliness of insurance firms’data. Many European insurers havetaken initial steps to go through theregulatory requirements and set upat least an interim regime to enablethem to calculate, report and createan initial, end-to-end perspective inaccordance with Solvency II. But


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Figure 8

Relatively low percentages of insurers integrate stress testing with strategic decision making

2%12%

48%

38%

3%

13%

48%

38%

3%

18%

52%

27%

All Industries Banking ChemicalsInsurance - LifeInsurance -Property &Casualty

Stress testing is integrated into strategicdecision making for large projects

Tools are available for usage at the discretionof the decision makers

Rarely used

Not used1%10%

45%

44%

2%11%

49%

37%


To what degree are scenario modeling and stress testing techniques utilized in your organization?

many firms currently lack sufficientdata quality and improving thatquality will be expected as part of thechanges caused by new regulation.

The focus on improved datamanagement also has implications forIT systems and capabilities. Insurerswith multiple back-end systems—or

with information stored in silos thatare not accessible to groups that mayrequire it—often face challenges inproviding consistent, high-quality datato the risk management function. Onecommon goal for insurers, therefore, isto define and implement a frameworkfor data management to supportboth consistency and quality of datathroughout the organization.

Data management is less of a challengefor analytics leaders than for laggards.

The top two challenges for laggards incomplying with Solvency II requirementsare availability of data (47 percent)and data quality (47 percent). Leaders,by contrast, cited issues such asthe continuously evolving nature ofregulations (67 percent) and meetingregulatory timelines (48 percent), ratherthan data problems.

Data governanceSixty-five percent of P&C insurers and56 percent of life insurers performdata quality controls when collectinghistorical data. Sixty-nine percent ofall insurers have a data policy in place,and 41 percent have a data qualitydepartment. There are two elements to

data quality to bear in mind. One taskis to get the data right and complete.Second, however, carriers often havemultiple legacy systems and so theyshould work to effectively conformthat data across the systems withoutany loss of integrity or granularity tosupport advanced risk analytics.

Like data quality, data governance isimportant for the effective managementand use of information by multiplestakeholders within the company. Whenownership of data is unclear, redundantdata sources may be in use withoutclearly assigned responsibilities as tothe manipulation, retention or deletionof data.

Analytics leaders in our survey aresignificantly ahead of their peers whenit comes to a focus on data governance.For example, for 80 percent of leaders inEurope, Japan, South Korea and China,data quality controls are performedwhile collecting historical data; this istrue only for 56 percent of laggards.


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How would you rate the maturity of your organization’s risk analytics capabilities based upon the components of the

risk analytics process?

Data quality and sourcing

Business rules development

Management use and acceptance

Above Average Excellent

38%

41%

37%

27%

26%

30%

Banking

39%

42%

33%

18%

24%

29%

Insurance - Life

36%

41%

46%

27%

23%

17%

Chemicals

32%

37%

36%

Systems integration 33% 23%29% 20% 40% 17%31% 16%

Software 35% 22%42% 14% 32% 15%32% 21%

Staffing 42% 19%39% 16% 34% 21%34% 18%

Reporting and dashboarddevelopment

42% 20%36% 18% 38% 21%37% 26%

23%

21%

26%



Figure 9

Low percentages of insurers rank their reporting capabilities as “excellent”

ReportingBased on our study results, lifeinsurance firms are not particularlyconfident in their reporting anddashboard development capabilities;only 54 percent of life insurers ratedthemselves either above average orexcellent. The situation with P&C

insurers is better, with 63 percentrating themselves either above averageor excellent, and over a quarter ratingthemselves excellent. (See Figure 9.)

Solvency II and other emergingregulatory requirements have playeda key role in shaping firms’ reportingcapabilities. Regulators have increasedtheir focus on the quality and frequencyof reporting required by insurers, andrisk analytics is playing a major role inensuring that those requirements aremet. Similarly, internal reporting anddashboards for senior managementare important for effective steeringof the business, and risk analyticscapabilities can help firms makeaccurate and timely informationavailable to all decision makers.

Our analysis indicates that, to helpmeet regulatory requirements for riskreporting, the industry is currentlyheavily engaged in collecting data and

in developing processes and systemsfor compliance such as QuantitativeReporting Templates (QRT), Solvencyand Financial Condition Report (SFCR)and Regular Supervisory Report (RSR).

In addition to the requirementsspecific to Solvency II, the industrywill be challenged shortly to reflect

accounting-specific InternationalFinancial Reporting Standards (IFRS)as well. To improve internal reportingand operational reporting to supportrisk-enhanced core insurance processes,it will be crucial to provide consistencyin regulatory reporting and to make useof investments made in data governanceand overall data management.

However, because regulatory reportingand operational reporting on risk-enhanced insurance processes such

as product development and pricingcould generate different requirementsconcerning the accuracy and granularityof underlying data, industry and ITexperts may be called upon to alignthese implementations in a cost-efficient and coordinated manner.

Not surprisingly, the most prominentusage of risk reporting for insurers isfor regulatory compliance, followed bythe use of reporting by operations and

senior management.

Solvency Pillar III requires insurersto improve their transparency forsupervisors, as well as for the financialmarkets, shareholders, policy holders,rating agencies and regulators. Thatbroader and deeper transparency cangive insurers a more expansive viewof the company and its risk exposures.If the same instruments are used for

external and internal reporting, thenmore advanced capabilities may becalled for in areas such as real-timereporting, automation to generatemultiple reports, and the abilityto provide end users the option tocustomize reports. Efficient reportingand business intelligence mechanismscan take full advantage of the data andhelp a firm move beyond compliancefor its own sake to a situation wherereporting and analytics are being

used to identify and move on businessopportunities. Based on our experience,many companies already have or plan tohave data warehouses or managementinformation systems to meet theirinternal steering requirements.


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25

17%33%

61%62%

18%38% 48%

65%

18%35%

68%66%

5%27%

36%68%

30%53%

67%73%

18%37%

58%66%

What software would be used to meet Solvency II reporting requirements?(Select all that apply)

Europe

Insurance - Property &Casualty

Insurance - Life

Japan & South Korea

China


Current actuarial tools

Development or enhancement ofin-house software

Current reporting packages

Plan to buy or lease specific products(e.g., SAS)

Base size: Europe, China, Japan & South Korea sample.

Figure 10

Most insurance firms rely on in-house tools and software

Software andsystemsOur study found that insurance firms aremostly relying on their in-house toolsand software for Solvency II reportingpurposes. However, the use of external,third-party tools is an emerging trend

and several insurers are planning to buyor lease specific reporting tools as well.(See Figure 10.)

Differences between leaders andlaggards emerged in comparingdifferent companies’ approachesto the use of software and tools tomeet Solvency II reporting needs.For example, 67 percent of leaders inEurope, Japan, South Korea and Chinaare considering the development or

enhancement of in-house software,compared with only 24 percent oflaggards. And 24 percent of leadersplan to buy or lease specific productsfor meeting Solvency II reportingrequirements, compared with 18 percentof laggards. A higher percentage oflaggards (69 percent) prefer to usecurrent actuarial tools for Solvency IIreporting than do leaders (52 percent).

The overall systems environment hasa clear effect on the adequacy ofanalytics capabilities and, in turn,on compliance effectiveness. Manyinsurers find themselves susceptibleto errors and higher costs becauseof heterogeneous, loosely coupledsystems, which often require a greatdeal of manual input. Effective risk

analytics technologies improve thelevel of automation which can increasethe accuracy of data, enhance speedto market and reduce costs. Betterreliability can translate into improvedtrust levels among shareholders as wellas auditors and regulators.

Talent managementCurrent staffing devoted to analyticsand reporting is not necessarily large

within insurance firms. When asked howmany employees are typically engagedin managing reporting for SolvencyII requirements, about one-third (31percent) indicated that from three tofive people are involved; 21 percentsaid that from six to 10 people were onstaff; 38 percent reported staffing levelsabove 10 people.

Asked to rate the maturity of theirstaffing capabilities in the riskanalytics area, differences betweenleaders and laggards emerged. One-third of leaders rated themselves as“excellent” in staffing, comparedwith 13 percent of laggards. Seventy-eight percent of leaders ranked theirstaffing capabilities as above average

or better, compared with 48 percentof laggards. Twenty-three percent oflaggards fear that finding the rightskilled staff will have a high degreeof impact on their risk managementprocesses, while only 11 percent ofleaders share that level of concern.

Because of the relative immaturityof the risk analytics field, talentsourcing and development appearsto be lagging. Organizations should

consider strategizing now about howto improve their talent managementprocesses for the risk analytics function,and should consider internal sourcingand development as well as the useof external consultants and managedservices. Our study found that 71percent of insurance firms are currentlyusing outside vendors and consultantsto build their risk analytics capabilities.


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Looking Ahead: Growing More MatureRisk Analytics Capabilities ThroughBetter Integration


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27

5%

9%

54%

32%

86%

7%3%

53%

37%

90%

5%

8%

55%

32%

87%

14%

59%

27%

86%

5%

11%

53%

32%

85%

5%

11%

53%

32%

85%

Would developing an integrated approach to risk and analytics give your organization a competitive advantage?


ChinaInsurance -Life

Japan &South Korea


Europe

Yes, certainly

Yes, probably

No, probably not

No, certainly not

Base size: Europe, China, Japan & South Korea sample.Note: Due to rounding, figures may not total 100%

Figure 11

Insurers agree that an integrated approach to risk and analytics can provide competitive advantage

As insurers look ahead to advancingthe maturity of their risk analyticscapabilities, what are some importantsteps they can take? One of the mostpervasive challenges to be overcomeby insurers across the globe is asiloed, non-integrated approach torisk analytics. Eight-six percent ofrespondents agree that developing

an integrated approach to risk andanalytics gives their organizationa competitive advantage; however,few have actually achieved such alevel of integration. For example,in spite of their confidence abouttheir analytics capabilities, abouthalf of life insurers (48 percent)collect data about risk events onlyin pockets within their companies.

Integration of riskprocesses andcapabilitiesIntegration of specific analyticscapabilities across claims, underwritingand distribution is seen by survey

respondents as a key to effectiverisk management. Across the surveypopulation, 88 percent of insurancefirms feel that developing an integratedapproach to risk and analytics wouldgive their organization a competitiveadvantage, and those numbers werenearly identical for life and P&Cinsurers. (See Figure 11.) With thiskind of integration, information usedto assess risk in one core insuranceprocess can be made available to

assess other business opportunitiesfrom a risk/reward perspective.

The integration of risk and financeprocesses within core insuranceactivities can also be a source ofcompetitive advantage. Most insurershave not linked risk and pricing withsales but those who do can use risk-related information more effectively tosupport dedicated sales activities. Theseactivities can then be tailored to client

segments, providing a better perspectiveon potential risks and rewards.


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21% 21% 23%19%

49%

32%

20%

49%

31%

52% 55%54%

27% 24%22%

All Industries Insurance -Life


Banking Chemicals

There is a fully integrated view of riskaggregated across models

There is some degree of integrationbetween models

Separate risk models are used for eachtype of risk

What is the level of integration for risk models in your organization?


Figure 12

Only about one-third of insurers say they have a fully integrated view of risk across models

Differences among survey respondentsarose when looking more specificallyat different kinds of integration acrossclaims, underwriting and distribution.For example, 38 percent of leaders ratedthe integration between claims andunderwriting as excellent, comparedwith only 8 percent of laggards. This gapshowed up consistently in the ability to

integrate other areas:

• Claims and distribution: Leaders,48 percent claim excellence; laggards,18 percent.

• Distribution and underwriting:52 percent of leaders; 19 percentof laggards.

• Claims, underwriting and distribution:38 percent of leaders; 11 percentof laggards.

Integration withmanagementprocessesIntegration with management processesis also a challenge. Seventy-eightpercent of P&C firms and 66 percent

of life insurers say that the inability toembed risk analytics into managementprocesses is having a high or mediumimpact on their firms. In terms of aspecific capability such as stress testing,only 27 percent of P&C insurers feel thatstress testing is integrated into strategicdecision making for large projects,compared to 38 percent overall.

Overall, risk management can beimproved through the considerationof risk in the decision-making process,and analytics play a vital role. Forexample, an effective integration ofrisk-based capital methodologies indecision-making tool kits—requiringhigh data quality and a commonmeasurement approach such as onebased on economic capital—canweigh both the combined effects ofrisk-taking activity and the impact ofsuch activity on economic value. Witheconomic capital models, insurers

can optimize capital allocation from astrategic risk/reward perspective, andcan also gain a potential competitiveadvantage by leveraging enterpriserisk management tools and economiccapital modeling in their strategicdecision-making processes.

Integration of riskmodelsOur survey also asked about the degreeto which insurers have integrated theirrisk models. Only 32 percent of lifeinsurers and 31 percent of P&C firmsclaim to have achieved a fully integratedview of risk aggregated across models.One in five insurers say that separaterisk models are used for each type ofrisk. (See Figure 12.)

Model outputs are generally better fora firm when the model can provide afully aggregated view of all types ofrisk. To achieve this aggregated view, ahigh level of integration between themodels is needed. This means that allimportant models used for differenttypes of risk within the organizationshould be able to make consistentassumptions and generate comparableresults. Having a large number of


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non-integrated models can add to thecomplexity and is potentially harmfulfor the organization because the correctlevel of risk exposure across all types ofrisk can be difficult to quickly ascertainwhen required.

Although having an integrated riskmodel can be desirable, such an

outcome is often difficult to achievein practice. Fewer than one-third ofinsurance firms reported having a fullyintegrated view of risk across all models.The good news, however, is that half ofinsurance respondents have some levelof integration between their risk models.This is encouraging to note becausethese firms can plan to move frompartial integration to a more completeintegration over the course of time andmake better use of their risk models.

Enabling betterintegrationWhat are some steps companies canconsider taking to address the challengeof siloed or non-integrated riskfunctions? Here are a few.

Consolidate andstandardize the IT

environmentIn our experience, many insurancecompanies operate with a fragmentedrisk architecture that fails to support thefull use of risk management tools andmodels within the context of the overallbusiness. Outdated legacy systems andout-of-date architectures that preventeffective integration are also a factor, arestriction noted by 78 percent of P&Cfirms and 66 percent of life insurers.In other words, given the existing ITlandscape and the manner in whichdifferent parts of the business operate,risk analytics may amount mostlyto various point solutions generatedand used in different areas. Modelingteams have one subset of technology,underwriting teams have another, andthen those working on the reportingside have their own homegrown system.The systems in total may not provide anintegrated view of risk.

This situation can be addressed bystandardizing the IT environment.A comprehensive, uniform ITlandscape—reflecting an industry-specific reference architecture as astructure to enhance capabilities—canhelp to support multiple functionsand realize available synergies. Asthey contemplate moving to a more

integrated and holistic approach to riskanalytics, f irms should keep in mind thecomplementary nature of the variouselements involved in the transformation.For example, improvement andintegration of IT architecture cansupport increased automation andmake it easier to provide robust ITsupport, which in turn can help toreduce risk management costs.

Improve data governanceData governance also challengesmany insurers. Effective integrationof analytics capabilities is enabledby the ability to share high-qualityand consistent data. However, inour experience, many firms haveinsufficient rigor when it comes tospecifying who owns data, who setsit up and who manages it. A potentialbenefit of integration is consistentdata management between the risk andfinance functions, which can result in

lower costs, reduced financial risk andlower required reserves.

Integrate with strategicplanningConsideration should be given tointegrating risk management intostrategic planning so that investmentsmore adequately reflect the risksinvolved. This integration can enablethe company to balance risk and reward

in considering available opportunities.Based on our analysis, many companieslack the common risk vocabulary, theshared metrics and key performanceand risk indicators (KPIs/ KRIs) and thefirm-wide access to data (along withcentralized databases) needed to fullyintegrate risk and financial information.


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Conclusion: Driving Growth andBetter Compliance Through RiskAnalytics


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Changing regulatory regimes suchas Solvency II put a high priorityon actuarial and risk managementcapabilities, and improving analyticscapabilities is key to making thathappen. Under Solvency II, the actuarialand risk management functions haveroles and responsibilities that go farbeyond standard activities such asrisk identification and modeling. The

impact of these functions within theorganization and on decision-makingprocesses is high.

Greater economic volatility in themarkets, accompanied by increaseduncertainty, has generated pressure toimprove risk management capabilitiesin the insurance industry. Insurersare investing in more sophisticatedrisk analytics tools to improve themeasurement of rare risk events thatcarry a high degree of severity for

their portfolios. These tools can helpto reduce underwriting risk and risks inother areas, contributing to profitableand sustainable growth.

New instruments such as mortalitybonds, along with highly risky and/orcomplex assets such as hedge funds andventure capital, call for consideration ofsophisticated risk management methods

and tools to provide a more accuratepicture of risk. These methods andtools can help insurers manage theirexposures more effectively, and can alsosupport their efforts to capitalize onopportunities, improve organizationalperformance and, ultimately, enhancelong-term shareholder value.

Insurers show a strong commitmentto making the investments necessarilyto improve the maturity of their riskanalytics capabilities, and they are

open to the guidance of externalfirms with experience in this area.Over time, an integrated approach tomodeling and risk management can leadfirms to more predictive capabilitiesand, ultimately, the possibility ofembedding real-time analytics intobusiness and management processes.


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About the Authors

Eva Dewor

Eva Dewor is an executive director,responsible for Risk Management inGermany, Europe, Africa and LatinAmerica – Insurance. Based in Munichand with over 16 years of consultingexperience, Eva specializes in helping

organizations enhance their riskmanagement capabilities throughits integration in decision making,steering and reporting. Working withrisk executives of multinationalsfrom across the financial servicesindustries, Eva helps them becomehigh-performance businesses.

Markus Salchegger

Markus Salchegger is a senior director,responsible for Risk Management

Insurance in Austria, Germany andSwitzerland. Markus holds a PhD inMathematics, and over the past 15 years,he has used his extensive experience ininsurance, reinsurance, banking, assetmanagement and software developmentto analyze, design and deploy solutionsfor risk management and financial serviceapplications that help clients becomehigh-performance businesses.

Ferko Spits

Ferko Spits is a senior managerresponsible for Accenture RiskManagement Insurance for North Asia(China, Japan and Hong Kong). Basedin Hong Kong, for the last 13 yearsFerko has been working with financialservices companies to transformtheir risk and finance functions,processes, data management and ITarchitectures. He combines broadindustry experience in the insurance,reinsurance and banking sectors with

extensive specialization in risk andregulatory requirements to help clientsbecome high-performance businesses.

Prasanna Varadan

Prasanna Varadan is a senior manager,Risk Management, India. Based inChennai, India, Prasanna has more than12 years of consulting experience infinancial services and risk management.He has worked with global and regionalfinancial service firms across Africa,Asia Pacific, Europe and North America

to transform their businesses and riskcapabilities. His specialized experience inrisk management, regulatory compliance,credit risk and operating model strategyhelps Accenture create differentiated,industry specific offerings to help clientsbecome high-performance businesses.

About AccentureManagement ConsultingAccenture is a leading provider of

management consulting servicesworldwide. Drawing on the extensiveexperience of its 16,000 managementconsultants globally, AccentureManagement Consulting works withcompanies and governments to achievehigh performance by combining broadand deep industry knowledge withfunctional capabilities to provideservices in Strategy, Analytics, CustomerRelationship Management, Finance &Enterprise Performance, Operations, Risk

Management, Sustainability, and Talentand Organization.

About Accenture RiskManagementAccenture Risk Management consultingservices work with clients to createand implement integrated riskmanagement capabilities designedto gain higher economic returns,improve shareholder value and

increase stakeholder confidence.

About AccentureAccenture is a global managementconsulting, technology services andoutsourcing company, with 257,000people serving clients in more than120 countries. Combining unparalleledexperience, comprehensive capabilitiesacross all industries and businessfunctions, and extensive research on

the world’s most successful companies,Accenture collaborates with clients tohelp them become high-performancebusinesses and governments. Thecompany generated net revenues ofUS$27.9 billion for the fiscal year endedAug. 31, 2012. Its home page iswww.accenture.com.

Copyright © 2012 Accenture

Documents

Accenture - Risk Analytics Study; Insights for the Insurance Industry (2012)