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  • 1. How to build analytics intothe insurance value chainundiscovered opportunitiesinsurance | analytics

2. unlock valueprofitable growthdeep experienceinnovationinsightWe work with insurers to find opportunitiesthat deliver profitable growth while protectingand optimising their enterprise.Drawing on our deep insurance experience anda new, whole brain analytics approach, wellhelp you unlock new areas of value.whole brain analyticsinsurance analytics | How to build analytics into the insurance value chain 3. Rethinking analyticsThe proliferation of big data, fastercomputers and innovation in analytics,has opened new opportunities forinsurers to better understand the past,control the present and confidentlyembrace new opportunities in thefuture. This paper explores howinsurance companies can apply wholebrain analytics to solve businessproblems and find new areas of value.It also explains the practical realities ofembedding a useful and usable coreanalytics capability throughout theorganisations.As Googles Eric Schmidt famously explained, since thedawn of civilisation until 2003, the human race produced5 exabytes (5 billion gigabytes) of data. Now we areproducing 5 exabytes every two days and the paceis accelerating.At the same time, we have powerful computers andalgorithms that are reducing the cost of collecting andanalysing all this data. This latest example of Moores Lawallows insurers to triangulate their own internal informationwith oceans of external data from social media and mobiledevices. This presents an opportunity to look at yourexisting data from a host of different dimensions, revealingcauses, correlations and insights management would neverotherwise have seen.Other industries are already using statistically-basedalgorithms to crunch through big data: powering Netflixrecommendations; accurately predicting, not just how many,but which patients will be admitted to hospitals next year;calculating the quality of Bordeaux wine (and hence itsappropriate pricing); and forecasting how long someonewill stay in a job.Insurance companies already hold vast swathes of data ontheir customers, and are using analytics for actuarialpurposes and marketing campaigns. But most executivesknow they have only just skimmed the surface when itcomes to turning their data into value.With little room to move the traditional levers of tightenedunderwriting and expense control any further, theres neverbeen a better time to leverage the power of big data andanalytics. Under pressure from many fronts includingglobal competition, smarter consumers and regulatoryconvergence Australian insurers urgently need newideas for improving business performance and creatingcompetitive advantage.Many insurers are asking us: What more can our own data tell us? What else could we learn if we addedexternal data to our models? How can we build the power of analyticsinto day-to-day decision-making?Human beings leave a digital recordNow the internet is inextricably woven into the fabricof our lives, wherever we go and whatever we do, weleave digital traces: mobile phone calls, financialtransactions, traffic patterns, social media and thedata that we, as consumers, trade for free services.This digital profile reveals our movements, spendingpatterns, relationships, health issues and manymore intimate details of our daily analytics | How to build analytics into the insurance value chain 1 4. A leading Insurer utilised customerbehavioural analytics to increase profits bymore than 2% of GWPA leading household insurer with more than 4 millioncustomers leveraged its data to build deeper insightsinto its customers, optimise pricing strategies andenhance propositions to meet customer needs.The insurer developed behavioural customersegmentation and price optimisation models to testprice elasticity based on differential pricing for eachcustomer segment. Attitudinal surveys were utilised tounderstand customer perceptions on the price:valuetrade off. Pricing strategies for each customersegment were refined based on insights gained.The insurer used controlled pilots and continuousimprovement techniques to gather more intelligenceand improve strategies for each customer segment.This resulted in market leading retention rates, andmaterial improvements in profitability.This proposition was awarded a five star rating by anindependent financial research company specialisingin rating, comparing and analysing financial products.5bnwe are producinggigabytesof data every 2 daysinsurance analytics | How to build analytics into the insurance value chain 2 5. Whole brain analytics approachDiagnose the business problem oropportunity and create hypothesesCombine rational and emotionalanalytical models to predict outcomesand understand feasibilityUndertake rapid experimentationto test hypothesesPilot solution and developUpgrade to whole brainanalytics to support quicker,better decision makingSupport decision making by combiningrational analytics with emotional analytics toensure data informs strategy and the strategyis explained by the data.Traditional analytics approaches source data that is available,complete and well organised. They apply analytic andstatistical techniques to the data to suggest conclusions thatthe data supports. Analysts rarely are, or believe they areempowered to use intuition in forming conclusions. Businessoperating models are typically designed with an expectationthat analysis produced hard facts, leaving analysts wary tostep beyond the objective and verifiable.Executives have deep knowledge of products, processes,customers and the business context that analysts often lack.However, executives typically only rely on analysts to performthe science of statistical inference. They typically perusestatistics and information provided by analysts toform hypotheses and make decisions based on what theyinterpret, or gut feel.This type of approach is usually linear, discards availableinformation, is delayed after the fact and can often bedisjointed by not applying business experience and intuitionin the early stages of the analytic process.Adopting a different approach where emotional andrational thinking operate symbiotically will enable you toquickly reach better, more insightful decisions. We call thiswhole brain analytics, a new methodology that combinesrational (data, algorithms, statistics, models) with emotionalanalytics (experience, intuition, experimentation).Whole brain analytics ensures the analytics process isinformed at the right time with deep business experience,ensuring the right questions are asked, the right assumptionsmade and appropriate analytical models datastatisticsalgorithmsRational implementation planexperienceintuitionexperimentationinnovationsignificancemodelsEmotionalOutcomesinsurance analytics | How to build analytics into the insurance value chain 3 6. Embed analytics as a core,interconnected competencyacross the businessvalue chainAlthough many insurers have established analytics as astrategic priority, success depends on their ability to build arobust core capability and competency that can be leveragedacross the enterprise.An analytics mindset should be embedded in the business,supporting specialised business processes and strategicdecision making increasingly in real time.Uncover thereal cause ofyour toughestproblems, beable to anticipatethe future andidentifyopportunitiesto differentiateand grow yourbusiness.Whole brain analytics provides a catalyst for embeddinganalytics into the business value chain, utilising methods andprocesses that bring business intuition, technology and datacloser together. It facilitates more effective collaboration andapplies modelling techniques that enable you to develop adeeper understanding of the information you have, so you cananticipate the future and make better business decisions.banking | embedded into insurance | the value | chain culture | grow insight | industry led analytics| protect world | real optimisewhole brain analyticsdata collectionand managementmodellingand analysisdecisions andinitiativesbusinessinsightsystem contemporary whole analytics- tools brain capabilitiesprocess- | analytics governance analytic structure - innovation | lab | real architecture time analytics data | big data | informationsymptomsintuitionbig datainsurance analytics | How to build analytics into the insurance value chain 4 7. Industry led analyticsAn example - analytics working across theinsurance value chainResearch/strategyGrow customer profitability by looking at your owninformation, digital and social media to identify highpotential customers, their behaviours and preferences.Use this information to help you define your customerexperience strategies and implement initiatives thatwill delight your priority customers and attract newones with the most potential.Continuously monitor and re-evaluate customerspotential, risk attributes, situation and environmentto test the ongoing validity of segmentation and retaina realistic view of customer profitability and risk, astheir circumstances change.Mine the data in the risk world to understand howmarket and credit events are related and use it to betterplan for funding, reducing your risk of having to obtainemergency funding at punitive rates.ProductUse your new customer insight to develop highly relevantand attractive products and product bundles for specificcustomer segments or individual customers, togetherwith an effective pricing strategy that maximises thedelicate risk reward balance.Harness more sophisticated, risk-based pricing to allowyou to introduce products at the right price that wouldonce have been deemed too risky to develop.DistributionEmbed the intelligence you build about your customersinto your distribution strategy to: generate quality leadswhere customers have a high propensity to buy; anddetermine the most effective distribution channel to costeffectively capture their business.Im


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