Is the customer experience overrated?

According to analyst firms, consulting companies and various other research, customer experience is the primary priority for insurance companies.  But is customer experience overrated?

Let’s start by considering the primary interactions between an insurance company and its customers: new business, billing, renewals and claims. Ask any insurance executive, especially property & casualty, and they will tell you the most important customer experience is the claims process. Yet a recent survey by Accenture found that more than 40% of customers who submit claims are likely to switch insurers within the following year, regardless of satisfaction.

If the claims satisfaction does not positively influence retention rates, we should ask if the customer experience is just hype?

In an article, “Does Improving the Customer Experience Matter?”, Mark Breading from Strategy Meets Action, writes that to answer this question insurance companies must consider three questions:

  1. Who is the customer?
  2. What influences their experience?
  3. Why does it all matter?

It’s the third question I want to focus on. In a blog I wrote earlier this year, I discussed that the primary objective of an Insurance CEO is to grow the business. For most insurance companies, this means increasing market share. To attract new customers and retain existing policyholders, insurance companies need to differentiate themselves from their competitors and the customer experience is vital to achieving this objective.

To learn more download the white paper: “Six Steps to an Unmatched Customer Experience.”

As the great philosopher, Homer Simpson, once said “People can come up with statistics to prove anything”. So while the report from Accenture may be true, insurance companies know that the customer experience does indeed matter.

I’m Stuart Rose, Global Insurance Marketing Director at SAS. For further discussions, connect with me on LinkedIn and Twitter.


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Getting personal at the touch of a button

Over the past decade in insurance, the rise of the aggregators (organisations that compare quotes between different insurers) and direct channels has had a profound impact on personal lines distribution in the UK.

However, personal lines brokers remain a critical route to market, especially at a time when many insurers have been centralising their offerings and closing local branch offices. In terms of distribution, brokers remain responsible for placing 35 per cent of personal lines business, according to Datamonitor.

And increasingly, this business is placed online, via aggregator websites, insurer extranets and comparative quote systems. Personal lines brokers have embraced the full cycle EDI model (electronic data interchange; an electronic communication system that provides standards for exchanging data via any electronic means), yet the ability to maintain a close dialogue with insurer partners remains critically important.

Beyond offering competitive pricing, successful e-broking requires referrals to be handled efficiently while the ability to tap into quote enrichment methodologies also improves the journey. Processes around electronic trading and product delivery are constantly improving, including the ability to cross-check information electronically.

Within motor insurance, the Driver and Vehicle Licensing Authority (DVLA) MyLicence initiative was launched last summer. It allows brokers and insurers to access instant DVLA data on driving entitlements, motoring convictions and penalty points at the point of quote. The industry, through the Association of British Insurers (ABI) and the Motor Insurers’ Bureau (MIB) has worked jointly with the DVLA and Department of Transport to develop the data sharing programme.

360 customer view

Credit scoring and telematics are other examples of how big data can be used to validate application information and reduce fraud. Third-party data can help establish identity, honesty and level of risk at the touch of a button without having to rely on the customer to go through lengthy questionnaires.

By offering intermediaries the means to access better analytics, insurers have an important role to play in improving the profitability of their core broker accounts. And by maintaining a close dialogue, insurers will be in a better position to tailor their products, via schemes and affinities, to meet the changing needs of their brokers’ client base, allowing them to exploit opportunities as they arise.

SAS Visual Analytics works with insurers to offer more precise insights based on all available data. Our insurance software allows clients to take full advantage of big data for telematics, social media analysis, catastrophe modelling and risk analysis. Download our white paper ‘What Does Big Data Really Mean for Insurers?’ and find out more about Insurance Solutions from SAS.

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5 steps to fostering adoption of fraud analytics

Special Investigation Units (SIU) are extremely process-oriented and follow well-documented procedures to decide when a claim should be referred for investigation and what actions should be taken. Most of the staff are seasoned investigators who may be more inclined to trust their experience and tried-and-true processes than analytical techniques that may seem unintelligible.

They can’t trust the data if they don’t understand it - raw analytical model output is often full of technical jargon that’s not easily understood by the business user. This creates frustration for the end user - often an analyst or investigator with more business expertise than technical or statistical experience – and may lead them to think that the analyses are meaningless and analytics lack any real business value. To convince these users to adopt analytics, the solution must give them easily consumable information.

And, you must integrate the analysis with the business process. Fraud analytics solutions need to meet the investigators’ business requirements and be structured in a way that makes sense to them – not just to the data scientists and IT teams. They need a well-configured user interface (UI) that gives them all the information they need to make a decision about whether or not to proceed with an investigation.

Here are five things you can provide to help them see analytics as the answer:

  1. Provide a “one-stop-shopping experience.” The investigator should be able to complete the triage and review process without ever leaving the UI. In a manual fraud detection environment, the investigator has to access multiple internal and external data sources. By providing instant access and a holistic view of these data sources, you exponentially increase investigator efficiency and start to win converts.
  2. Remove the technical jargon. Use scorecards that speak the business language to show the investigator the rules or scenarios that were surfaced by the model and how much weight each contributes to the overall fraud propensity score. This will help them understand why a referral scored high for fraud risk and allow them to focus their investigation on the most critical factors.
  3. Show and tell. Analytical output surfaced in the scorecard should be supported by other data in the UI. Incorporate sections or links to reports that contain the data that support the rules and scenarios triggered in the scorecard. Investigators will not – and should not – blindly accept that the fraud scenarios surfaced in the scorecard are factual. Providing easy access to this data also helps facilitate their investigations.
  4. Leverage third-party data sources. Many SIU’s invest in expensive external data sources and most aren’t integrated with internal data sources. The lack of integration makes it difficult to get the most value from the data. Insurers can get more value from their investment by incorporating it into the UI, combining it with other data elements, and displaying it in a meaningful and user-friendly format. Common examples of this third-party data could include medical bill audit data, industry watch lists, sanction lists, loss history data, estimate data and public records information.
  5. Give them more than just a score. Provide deeper insight into suspicious activity by incorporating data visualization techniques such as link analysis, maps, graphs, charts, annotations, and highlighting of key text and other data elements that support the scorecard findings. These simple approaches resonate with the end user and add tremendous value by directing attention to the most relevant information.

The most powerful analytics in the world have little value if they can’t be readily understood and adopted by business users. Start with these five tips to get your investigators excited about fraud analytics. These will help them see that analytics can drive better decisions and better outcomes.

What do you think the biggest challenge is to the adoption of analytics? Tell us in the comments.

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Internet of Things: A game changer for insurance

Amol Kokane, SAS

Amol Kokane, SAS

~ This article is co-authored by Binod Jha, Global Product Manager for Insurance Solutions at SAS, and Amol Kokane, Senior Development Manager for Insurance & Risk Management Solutions at SAS ~

How might insurance policies change if sensor data could be automatically transmitted and analyzed from your car, your home and even your clothes?

The Internet of Things (IoT) has the potential to transform both the business and the IT infrastructure of the insurance industry. The data captured and transmitted by multiple sensors and devices – each attached to an associated, insured risk – will redefine existing processes of underwriting and pricing.

Let's look at a few definitions before we get into the possible affects of IoT on the industry.

Insurance premium pricing is based on a set of rating parameters that are derived from predictive models built from historical loss data. The law of large numbers and the associated spread of risk exposures helps in arriving at the pure premium for a pool of homogenous risks. But it’s very difficult to calculate loss propensity at each risk and exposure level in this pool.

Underwriting and pricing happens when a policy is sold, but the risk (both in quality and behaviors) changes over time. This fact should be (but seldom is) factored in the underwriting on a regular basis. So while the insurance industry has historically been data intensive, its ability to monitor claim propensity at each risk and exposure level during the lifetime of the policy is limited. As such, premiums are subsidized by good risks, taking the same hit by association with the bad risks. 

How does IoT change the game?

IoT makes it possible to capture rating parameters as well as the digital shadow (the historical data captured from devices and sensors) of insured objects and people, in real time. This huge amount of data can be analyzed for underwriting and pricing decisions at each exposure level, that is for each instance of the policy coverage period. This moves insurance to the realm of continuous calculations - thereby reducing incurred losses with proactive strategies vs. historical tactics. The IoT ecosystem has already matured enough to enable the early movers in this new IoT operating model for insurance. 

What are the benefits of IoT? 

For insurers, the benefits of analyzing IoT data include:

  • Lower claim severity and frequency. Continuous monitoring of insured risks, real time prediction of loss propensity, and reliable loss control mechanisms will impact claim severity and frequency.
  • More accurate risk assessment. Huge amount of granular data generated by sensors and devices attached to insured risks improves risk assessment accuracy and thus the underwriting process. In turn, this also reduces the suppression of material facts related to risk by customers at the time of policy acquisition.
  • Improved claim servicing. IoT enables automated loss notification in case of an accident or hazardous situation. The details related to loss or damage are captured by sensors and devices. Claim processing cycle time and loss adjustment expenses will be reduced as a result.
  • Higher customer satisfaction. Real time monitoring of insured risks and dynamic pricing creates transparency to both the underwriting and rating process. Educating customers on the factors that impact their specific insurance premium not only takes the required precautions needed to avoid premium loading, but also puts customers in more control of their plan. And it’s expected that with policyholders in the premium driver’s seat, so to speak, they’d be more satisfied, happier customers.

The entire insurance value chain – underwriting, policy servicing, claims, actuarial, reinsurance and customer service – are all impacted, maybe even disrupted, by IoT. Let’s focus how this may play out for a few lines of business.

Life and health insurance

Life and health insurance is expected to see the biggest impacts from IoT for a couple of reasons. Risk quality changes with lifestyle events (marital status) as well as with demographics (like age and occupation) impacts the baseline underwriting of the policy. With IoT, however, continuous monitoring of risk quality becomes a reality. The underwriting and pricing of insurance premiums won’t be limited to the inception of the policy – but will change during the entire coverage period. In this new world, the insurance premiums will fluctuate just as with monthly utility bills.

And the days may not be too far off when IoT wearable devices capture significant measures, like heartbeat, temperature, blood sugar, exercise duration and report them to insurers. Or when clinical diagnoses are be made from non-invasive methods, like analysis of sweat or tears. Premiums might even be calculated on a daily basis, transparent to the end customers - encouraging healthier lifestyles perhaps and reducing premiums accordingly.

Auto insurance

The first round of business model disruption has already taken place in the automotive side of insurance with the popular adoption of usage-based insurance (UBI) over traditional pricing in countries like the US, Canada, UK, Italy, Germany, and others. With UBI, vehicles are fitted with sensors that monitor driver behavior, to keep track of when, where and how the vehicle is in motion. The insurance premium is primarily determined on the basis of driving behavior, rather than proxy variables like vehicle make, model, and year. Enhancements to claims processing will likely be the next major improvement area for auto insurance – with automated notifications from these same telematics sensors for accident occurrence or non-typical driver profiling, helping carriers pinpoint events like accidents or theft. 

Property insurance

It’s expected that property insurance will see an increased adoption of IoT in this decade. Smart homes, offices, commercial buildings and industrial installations can be fitted with sensors and devices that generate real time data on hazards from overheating malfunctions to building material strength. Not only can any accidents or breakdowns be immediately recognized, but gradual deterioration can be monitored to avoid substantial damages - bringing insurers into proactive service for property customers.

Home, office and industrial equipment can all be remotely monitored for ongoing situational assessment. Any measured factor leading to malfunction or damage can trigger notifications to repair shops or manufacturers for prioritized service. Smarter carpets that detect falling incidents can notify medical assistance, reducing staged accidents and liability claims. The scope of pragmatic applications is just beginning to emerge. 

Insurance analysis in IoT 

For this next generation of insurance applications to be successful, analysis of the sensors and all other streaming data will be necessary – to understand existing conditions and to postulate future scenarios accurately. How will this work?

The stream of incoming data first should pass through an initial set of rules and algorithms devised for assessing the data, normalizing it as necessary and evaluating risk, continuously. The risk patterns are already defined to recognize when conditions are met in real-time. How? The pattern resides in the system as data is continuously streamed through – calculating the conditions to new events. When conditions are met, a reaction is triggered, sending the appropriate person or system the instructions to take the next action. For example, if vehicle monitoring indicates speeding based on location-based limits with frequent acceleration followed by sudden breakeage, an alert can be sent to the driver to reduce speed.

The high-volume and high-velocity granular data captured and transmitted from sensors and devices and attached to insured risks needs to be cleansed, organized and stored in a multi-parallel processing (MPP) data warehouse with effective history management. The stream of incoming data first should pass through an initial set of business rules and algorithms devised for continuous monitoring of risk. Any trigger of such rules should be able to send an appropriate feedback or control to the customer through state-of-the-art event stream processing system. The incremental data needs to be extracted from the warehouse and loaded into analytical input datasets for both modeling claim frequency and claim severity. The MPP environment supports high performance advanced predictive analytics and is used to discover any new significant rating parameter(s). The newly generated algorithm is then registered in a model management system and operationalized in the dynamic pricing engine.


The flow of sensor data for analysis in the insurance industry.

Unlike the current industry adoption of analytics in limited parts of the business, this new, IoT driven, big data revolution will require mainstream adoption of analytics across the entire insurance value chain. Every part of the business will be supported by sophisticated algorithms – assessing the current risks and calculating the differential value. Insurance organizations will be on the forefront of technology adoption to invest in scalable and robust high performance platforms.

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What keeps an insurance CEO awake at night?

I cannot speak from experience, but predominately an Insurance CEO has three primary objectives:

  1. Grow the business
  2. Reduce expenses
  3. Ensure compliance.

Let’s individually consider each of these objectives in more detail.

 Grow the Business

  • How does an insurance company grow from a $2bn to a $3bn organization? Essentially, insurance has a defined market size. Who needs a second home insurance policy or another life policy? Therefore to grow the business means increasing market share. To help achieve this objective, insurance companies are turning to analytics to improve pricing accuracy, create new products (telematics etc.) and enhance customer experience.

Reduce expenses

  • An insurance company has many different expenses. There are operating expenses such as employee salaries and infrastructure costs, underwriting expenses, and commission payouts. But by far the biggest expense within a property and casualty (P&C) insurance company is claims. Claims payouts and loss-adjustment expenses can account for up to 80 percent of an insurance company’s revenue. Adding analytics to the claims life cycle can deliver a measurable ROI with cost savings and increased profits; just a 1 percent improvement in the claims ratio for a $1 billion insurer is worth more than $7 million on the bottom line.

Ensure compliance

  • Insurance companies around the world are facing a host of new regulations. European insurers are consumed with implementing Solvency II, which will take effect in 2016. Many other countries are closely following events in Europe and seeking to implement the equivalent of Solvency II in their own regions. In the United States, the National Association of Insurance Commissioners (NAIC) has introduced the Solvency Modernization Initiative (SMI). To ensure compliance, insurance companies are implementing a comprehensive risk management frame work that includes data management, analytics and reporting.

In today’s highly competitive market, it is vital for insurance companies to minimize inefficiencies and reduce losses to protect profitability. By using analytics, insurance CEOs more easily achieve their objectives and sleep at night.

To learn more about how analytics can help, download the white papers “The Analytical P&C Insurer” and “The Analytical Life Insurer.”


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The secret to successful underwriting

It is clear from many of the comments arising from this year’s Insurance Times Broker Service Survey that brokers value underwriters who are open-minded and willing to adapt wordings and terms where possible to accommodate clients’ individual needs. They also value a close dialogue and partnership with underwriters who are approachable.

It is the insurers with empowered and decisive underwriters, with the capability to consider complex or difficult-to-place risks, that are most successful in retaining renewal business and winning referrals. Those able to adapt to changes to risks mid-term are also valued, particularly by brokers with fast-growth clients.

The antithesis to this is also clear from verbatim comments in the survey. Underwriters who are difficult to approach and/or reluctant to deviate from a rigid set of underwriting criteria are those that received negative feedback.

Some comments indicate that a reduction of underwriting expertise and flexibility has occurred as a result of restructuring or M&A. Here, the loss of a dedicated underwriter or representative can have a profound impact on relationships within the broker channel. It is particularly important therefore to ensure continuity following such periods of change.

When Markerstudy acquired Zenith Insurance, SAS was able to work closely with the company to bring together over five disparate data systems into a single-user friendly platform. A clear data-quality strategy and auditable structure was also necessary to prepare for upcoming Solvency II regulations.


Startups deal with a myriad of data analysis issues.

By improving data quality and offering management a clearer view of the business, it meant that swift and informed underwriting decisions continued to be made as the two businesses integrated. Looking ahead, as the business expands, improved data quality will offer underwriters and other decision-makers a clear overview of how the different lines of business are performing at a group level.

Download our white paper on Data is King: the benefits of an insurance data model and find out more about how SAS Analytics can help you improve business performance through more efficient use of data at

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There’s a need for speed, but with an eye on fraud

42-25869075 (1)According to broker’s “best” and “worst” verbatim responses in this year’s Insurance Times Broker Service Survey, insurers who act quickly to verify and settle claims are their preferred partners. This year’s importance scores reveals brokers are placing a stronger emphasis on claims in comparison to previous surveys. Those instances where straightforward claims are processed quickly and a cheque is issued in a matter of days, without quibbling or delays, foster both customer and broker loyalty.

However, insurers ability to process and settle claims is frequently put to the test, not least as a result of the high levels of fraud prevalent in personal lines claims. The Association of British Insurers (ABI) estimates claims fraud costs the industry £2billon a year and within motor alone, £466million. The industry has been fighting back, sharing data via initiatives such as CIFAS (the UK's most comprehensive databases of confirmed fraud data) and the Insurance Fraud Register (IFR).

In the future, access to these fraud databases will help insurers and brokers flag suspect individuals at the point of quote. It will allow them to quickly identify individuals who may have been associated with previous claims fraud or who have been dishonest in other ways, e.g. committing application fraud. Research suggests that individuals who misrepresent themselves at the application stage are more likely to commit claims fraud.

Claims “triage” or decision-tree management is helpful in determining which claims can be approved speedily while isolating those that require further investigation (potentially because they have been made by an individual who misrepresented themselves during the application phase, for instance). As well as being used in fraud detection, such systems are also effective during peak claims periods, such as in the aftermath of winter storms and floods earlier this year.

Between December 1 and February 19, the wettest winter on record in the UK, brokers and insurers received £6.7million a day in insurance claims from customers hit by flooding. Sixty per cent of the 18,700 flood claims have now been fully settled, according to the most recent ABI figures.

SAS recently carried out research - Insurance Companies: Are You Equipped to Successfully Combat Fraud?” - which showed that, despite a rise in global fraud, two-thirds of European insurers saw the volume of detected fraud increase by less than four per cent. The online survey revealed that those insurers that do not use automated detection, or only use ‘business rules’, saw significantly lower levels of detected fraud than their peers using advanced analytics.

SAS Analytics offers insurance companies a suite of solutions to improve fraud detection and enable claims handlers to improve business performance throughout the claims lifecycle through the more efficient use of data. Find out more at

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Can I Quote You on That? - 2014 in review

Earlier this year, I was speaking with an insurance executive and he said something that turned out to be my favorite quote of 2014: “Premium revenue is like heroin.” While this seems like an unlikely analogy  or simile?. The point this executive was trying to make an interesting argument.  Insurance companies are so focused on increasing revenue that they often neglect the fundamental principles for successful pricing – “the only badly priced risk is an underpriced risk”.  It’s better to decline the business than accept a bad risk.

Theoretically this statement makes sense.  However, in reality, it is rarely the case. If you promised an insurance CEO that you could double profits but revenue would fall by 25%, very few would accept the proposition. However one insurer that was willing to reject unprofitable new business for the long term gain was AIG. This case study details how they grew their executive liability business by $14m over an 18-month period by declining potential loss of $75m from certain executive liability accounts.

This wasn’t the only great quote I heard this year.  Here are a few of my favorites from 2014:

  • “Common sense is not so common.”
  • “Nothing is more dangerous than an executive who has read an airline magazine!”
  • “Data, at the end of the day, is just history.”
  • “Do not let perfect get in the way of very good.”
  • “What is telematics? – Where data meets the road.”
  • “It’s not about the amount of data.  It’s about the accuracy of the end result.”

As I look back on 2014, I am reminded of the infamous quote by baseball player, Yogi Berra: “It’s like déjà-vu, all over again.” Despite all the hype about the Internet of Things and big data, 2014 was a quiet year and not much has changed for insurance companies. However, as we look forward to 2015, there is considerable reform in the insurance industry with initiatives such as Solvency II and we are likely to see more change.  Or, as Yogi Berra would say:   “The future ain’t what it used to be.”

Finally, I would like to wish all my readers a Happy New Year and I am looking forward to spreading the word about analytical innovation in insurance in 2015.

I’m Stuart Rose, Global Insurance Marketing Principal at SAS. For further discussions, connect with me on LinkedIn and Twitter.


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Many Faces of Insurance Fraud

What do the following have in common?

  • A homeowner inflates the value of his home entertainment equipment stolen during a robbery.
  • A parent states they are the primary driver for their child’s car.
  • A doctor charges for a non-existent procedure.
  • A construction company underreports payroll or misclassifies an employee’s duties.

Answer: Insurance fraud

Insurance fraud comes in many shapes and sizes, and technology plays an important part in prevention. In fact, a recent study by Coalition Against Insurance Fraud found that 95% of insurers are using anti-fraud technology. However, most insurers have found that no single technology is sufficient. A combination of techniques is required to identify both opportunistic and organized fraud. The first line of defense continues to be automated red flags / business rules (81%). However, as fraud patterns and behavior become more sophisticated, insurers are deploying more advanced analytical techniques. The next top five technologies were link analysis (50%), anomaly detection (45%), predictive modeling (43%), text mining (43%) and data visualization (40%). The figure below shows the investment in these technologies compared  to the investment shown in a similar survey undertaken in 2012.

Fraud survey

Other key findings from the report included:

  • More than half surveyed indicated that suspicious activity had increased over the past three years.
  • Two-thirds of insurers responded that they use anti-fraud technology developed by a software vendor.
  • Most cited benefits of fraud-detection solutions including  receiving more and better referrals, uncovering organized fraud, and improving investigator efficiency.

Read the full report “The State of Insurance Fraud Technology” to understand how and to what extent insurers are using anti-fraud technology.

The full scale of insurance fraud is unknown. Since the crime is designed to go undetected, the fraud-fighting community can only guess at the extent of crimes committed and dollars lost. Whilst many policyholders still view insurance fraud as a victimless crime, it impacts not only every insurance company but every policyholder due to increased premium rates. The FBI estimates that insurance fraud costs more than $40 billion per year or between $400 and $700 annually in extra premiums for every American family.

CNA and Allianz are just two insurance companies who no longer view insurance fraud as cost of doing business. They have implemented SAS Fraud Framework for Insurance to increase detection rates resulting in reduced loss ratios, competitive edge and lower premium rates for policyholders.

I’m Stuart Rose, Global Insurance Marketing Principal at SAS. For further discussions, connect with me on LinkedIn and Twitter.

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Insurers Sharing Best Practice in Analytics

Last week, I was fortunate enough to attend the Insurance Networking News Analytics Symposium. This great event had several engaging speakers. As analytics becomes more prevalent within insurance, it was refreshing to see that many organizations discuss their successes and share best practices in this essential aspect of the business.

A common theme with the presentations was how data, analytics, technology and people are changing the insurance industry. Specifically three presentations caught my attention:

  •  Competing on Analytical Talent. In this session, Peter Hahn, Head of Predictive Analytics at Zurich North America, talked about how his organization was able to hire a team of 15 new data analysts despite intensive competition from other industries. While it is important that salary and benefits are competitive, specifically it came down to convincing the applicants that insurance is cool and detailing the type of exciting analytical projects like catastrophe modelling.
  •  Using Data and Analytics to Fight Claims Fraud. In this presentation, Tim Wolfe, AVP Special Investigation Unit, discusses how CNA was able to dramatically improve its fraud detection program by using analytics, thus saving the organization millions of dollars. Also, he spoke about the intangible benefits such as improving investigator efficiency and the reputation of CNA in the agency community by protecting its customers against the growth in claims fraud.
  •  Increasing Use and Leverage of Predictive Models for Mid-Sized Insurer. Mike Rowell, VP Business Analytics at Alfa Insurance, highlighted how his organization was able to compete with the tier 1 insurers by assessing the customer lifetime value of its policyholders. In particular, how Alfa Insurance improved persistency rates and influenced the profitability of its customer base. Interestingly, Mike Rowell, also shared with the audience that not everyone within his company had bought into the value of analytics. Rather than view analytics as complimenting their skill set, some knowledgeable employees saw it as competition even referring to as Voodoo Math or ass-alytics!

My take away from the conference is that analytics, despite a few naysayers, is now a strategic initiative of every insurer. Insurance companies of all shapes and sizes are finding innovative ways to use data to increase revenue or improve efficiency. To learn more about how analytics can help your organization download the Analytical P&C Insurer or Analytical Life Insurer white papers.

I’m Stuart Rose, Global Insurance Marketing Principal at SAS. For further discussions, connect with me on LinkedIn and Twitter.

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