Risk and rewards for insurers

The role of insurance is to bring some predictability, manageability and stability in what is in essence, a chaotic and uncertain world.

So as we head into 2016, what are the big issues for insurers in the next 12 months?

Below is just a selection of some of these issues:

  • Ensuring that customers are at the center of everything an insurers does.
  • Developing new, competitively priced products
  • Integrating delivery channels, especially digital channels with traditional channels
  • Enhancing operational efficiency and effectiveness with analytics
  • Maintaining relations with governments and regulators
  • Retaining strong capital positions and achieving good investment returns
  • Strengthening risk management
  • Implementing new technologies
  • Understanding the value of big data
  • Final countdown to Solvency II for European Insurers

Over the next 12 months the Analytic Insurer blog will discuss these issues and more as technology and analytics transforms the insurance industry.

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


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The era of smart insurance is dawning

Side view of smart carWhat if a reckless driver adopted a more responsible approach because the car insurance pricing was based on driving habits? What if the senior from next door had the insurance payments based on kilometres driven, resulting in significant savings? This may be reality sooner than you think.

The Internet of Things will revolutionise the insurance business to the benefit of customers and insurers alike. From an insurance perspective, IoT is based on sensors that collect information on risk items, conveying the data to the insurance company. By analysing the elicited data, the insurer is able to make assumptions that allow for a more accurate evaluation of risks. In some cases the sensors can help prevent accidents, or, at the very least, minimise their impact. The use of IoT encourages customers to adopt patterns of behaviour that are conducive to health and well-being. This may also lead to positive changes in attitudes. In other words, the implementation of smart insurances can benefit the entire society.

Risk-free driving habits bring insurance payments down

I would forecast that the car insurance sector will be among the first to see major changes. Octo Telematics, an Italian partner of SAS, is already offering alternative, sensor data-based insurance models for insurance companies in several countries. For example, the Octo Box, which is installed into a vehicle, can measure mileage, speed, accelerations and deceleration, even lane switches. This allows the insurer to apply pricing based on mileage-based or a wider range of data on driving habits. Drivers adopting a safer, proactive approach may achieve savings on their insurance payments in contrast to their neighbour with less safe and economical driving habits.

In England, a large portion of car insurances are so-called user-based insurances, typically based on mileage. Currently in Finland, car insurance pricing is based on only the features of the car and the owner's accumulated bonus points, or years without accidents. The customer is forced to pay the same amount regardless of the car's actual usage.

The new pricing model can provide a competitive edge to the insurer in a highly competitive business environment. The technical prerequisites for the change are already there. Many modern car models are already equipped with sensors, or the possibility of installing sensors. The insurance company only needs the tools to collect and evaluate data and for risk modelling. Our mission at SAS is to assist in analytics.

Proactive approach reduces water damages

Sensors are making their way into domestic and business environments. I myself installed a simple sensor system in my house a year ago. The system monitors waterflow within the piping, stopping the flow automatically once water has flowed continuously for a predetermined time. For example, if a fault occurs in the laundry or dishwashing machine, this safety feature can significantly reduce any damages. Once the insurance company found out about the precaution, my insurance payments were reduced.

It would also be possible to adopt a more systematic approach and transfer the data from the sensors directly to the insurance company's database. In addition to water pipes, households are filled with areas that could be monitored with sensors in order to collect data for evaluating and preventing damages. For example, voltage spikes are known to cause short circuits in domestic electrical systems, occasionally leading to fire damage. Sensors could help identify faulty household appliances before the problem is too late to address. In the future, sensors could conceivably be used to measure indoor air quality. The technique could contribute to the well-being of the residents by enabling the early detection of any changes in the air quality with potential affects on health.

In terms of property insurance, IoT will allow insurers to transition from reactive damage assessment and compensation to a proactive approach that facilitates damage prevention. I firmly believe that this is the smartest direction to take for residents, owners and insurance companies.

Sensors detect well-being and related risks

Many of us are already using active life trackers, or sliding a sleep analyser sensor underneath the mattress. A recent smart life insurance project piloted by LähiTapiola found that around 80 percent out of 2,000 participants improved their habits with an electronic health inspection, self-coaching programme and active life tracker incorporated into their insurance. In other words, IoT is encouraging a proactive approach with risk elimination even in the life insurance business. Our habits contribute at least as much as our genes and environmental factors to the risk of falling ill.

By agreeing to use an active life tracker, the customer provides the insurance company valuable health data for risk assessment. With less pressure on compensation matters, the company is able to offer reduced insurance payments or other benefits. Predictive modelling can help determine the best possible customer experience.

In Finland we are highly capable of implementing new IoT-based techniques due to our familiarity with mobile technologies and the use of smartphones. Smart insurances are only a small step away, and the change can be kicked off very quickly. The technical prerequisites are already in place. Now it is up to us to make bold decisions.

Interested in knowing more? Visit our pages with more information on SAS solution models for insurance companies.

This post was originally published on Hidden Insights, our blog for the Nordic region.

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Unromancing the crime – Using analytics to pinpoint suspicious activities

Similar to claims fraud, money laundering is seen as a victimless crime, and often glamorized in movies and books. Think “The Wolf of Wall Street” and “Scarface”.

But money laundering is a SERIOUS problem. According to a 2013 report, the United Nations Office on Drugs and Crime estimates that $1.6 TRILLION was laundered in 2009. These are staggering numbers with huge consequences not only to the financial services industry but often to national security.


Organized crime rings, drug cartels and terrorist organization are becoming more sophisticated in the process of hiding the illicit origin of their money. In response to the growth in money laundering and terrorist financing activities around the globe, regulators have stepped up compliance mandates. No longer is it acceptable that undetected money laundering activities as part of doing business.


But progress towards meeting these anti-money laundering requirements is being hindered by a number of challenges. For example, data is typically scattered across different systems, so there’s no single source of truth readily available for analysis and investigation. Complicating matters further is the fact that there’s simply too much data to analyze using traditional anti-money laundering tools; the process takes too much time andto get the job done. By the time anomalies indicating emerging risks are detected, the damage has already been done.

Like cybercrime, no organization or person is immune to money laundering. Of course banks are obvious targets for laundering money. But in recent years FIFA, several insurance companies and even golfer Phil Mickleson have been implicated in money laundering schemes. One life insurance company that takes this issue very seriously is ERGO Insurance of Belgium, a subsidiary of ERGO Insurance Group. Despite being a mid-sized insurer, ERGO identifies potential criminal and terrorist activities with a system that verifies and validates all transactions and documents within their entire database every 24 hours. With SAS Anti-Money Laundering ERGO manages enormous amounts of data and uses fuzzy logic analysis and a business rules approach that does not set off time-consuming false alerts. Read more on this fascinating case study.

It’s not easy to detect sophisticated and ever changing money laundering techniques. Today, insurance executives are under a great deal of pressure to eradicate money laundering from their organizations. SAS Anti-Money Laundering helps insurance companies take a proactive approach to monitoring transactions for illicit activities, comply with counterterrorist financing regulations and safeguard its reputation.

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


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Putting customers and analytics at the centre

Customer intelligence will help an insurance company deepen its customer insights, choreograph its customer interactions and continuously improve its marketing performance. Unfortunately for all the progress made in recent years on customer relationship management and getting to know customers better, insurance companies are still not focusing on customers as much as they should be. To change this philosophy, insurers need to consider data, analytics and embrace an omni-channel strategy.

CustomerFirstly, to gain insight into what customers will do in the future, a company must understand what they have done in the past. Analytics can be used to manage historical customer data and understand the behavior patterns of not just their best, but also understand who are their worst – and unprofitable – customers.

Secondly, customers want to feel that their insurer understands them. They expect to be properly communicated with and treated consistently. Marketing efforts, therefore, must be well-orchestrated and synchronized across multiple channels.

But better customer intelligence is just one part of the customer-centricity story. To make it a winning story, insurance providers must also have robust strategies and processes in place to retain existing customers and acquire new ones; find new ways to maximize customer profitability through effective sales channels; and have more precise segmentation and better communications.

In other words, they must fully embrace customer relationship management, and deploy it within a business analytics framework. Such a framework will allow the insurer to maximize customer intelligence to get the best return from campaigns; optimize customer campaigns and channels by automatically tracking each campaign element; implement complex customer interaction strategies, such as multichannel and event-triggered campaigns; and create, deliver and track high-volume, opt-in, personalized e-mail marketing campaigns based on a thorough understanding of the customer.

One insurance company that understand the value of customer intelligence is the insurance aggregator website, Confused.com. SAS Analytics has enable them to better understand and segment millions of customers contacts resulting in higher acquisition rates, improved retention, lower marketing costs together with an opportunity to optimize campaigns across multi-distribution channels. Read more about the Confused.com case study

A truly customer-centric organization that improves the customer experience will reap the rewards. Putting customer and analytics at the centre will result in increased loyalty, revenues and profitability.

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


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The final countdown… and beyond

It’s rather appropriate that the rock band Europe recorded the hit “The Final Countdown”, because today, September 22nd, represents 100 days until the much anticipated (and delayed) European insurance legislation Solvency II will come into effect on January 1st 2016.

Designed to introduce a harmonized, EU-wide insurance regulation, Solvency II demands a more comprehensive approach to risk management. To not only ensure Solvency II compliance, but to help insurance companies anticipate the regulatory and risk changes ahead and deal with them efficiently and proactively, these essential steps are recommended:

Step 1 – Data Management
Data management or more precisely, managing the quality and consistency of data – is fundamental to Solvency II compliance. A good data management strategy is a prerequisite for meeting Solvency II regulations. However, an excellent strategy not only ensures Solvency II compliance, it can also help increase risk awareness and improve business decision making processes throughout the organization.

Step 2 – Risk Calculations
Insurers must develop a central risk calculation “engine” that will analyze risks and calculate capital requirements that are in line with both Solvency II and company strategy, taking into account all quantifiable risks that an insurer may encounter, including underwriting risk, market risk, credit risk, liquidity risk and operational risk.

Step 3 – ORSA
Own Risk and Solvency Assessment (ORSA) is a relatively new concept. It’s a forward-looking assessment aimed at enhancing insurer awareness and understanding significant risks and their interdependency. With an integrated environment, users can align business planning processes and capital projects with income statements and balance sheets.

Step 4 – Reporting
Greater transparency, through public disclosure and reporting requirements, is one of the central foundations of Solvency II. Insurers will be expected to produce more reports than ever. Managing separate reporting systems for regulatory and management purposes will not be practical or cost-efficient. Insurers must save time and money by integrating their existing reporting system with their Solvency II implementation to produce consistent, timely and relevant information for all stakeholders.

The SAS approach to Solvency II and risk management is a flexible framework leading companies through the minefield of data, risk models and reporting. Our framework includes the solutions SAS Firmwide Risk for Solvency II and SAS Capital Planning and Management.

The lyrics of Europe’s “The Final Countdown” include the lines “Will things ever be the same again? It’s the final countdown.” There is no doubt, ensuring Solvency II compliance has been a long and difficult journey for many insurance companies. However January 2016 should not represent the finish line; it’s just the beginning of a new risk management playbook and things may never be the same again.

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


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Flipping the data equation

Big_Data_For_Small_CompaniesBig Data has become a technology buzzword. But how is Big Data changing insurance?

Historically, insurance companies have used SMALL data to make BIG decisions. Today, insurers are using BIG data for SMALL decisions.

What does this mean?

Traditionally, insurance companies have aggregated data to group risks into broad categories based on basic factors, such as gender, age, martial status etc. For example, let’s consider auto insurance. They have assumed that all young, single male drivers are reckless and that middle-aged, married, female drivers are more cautious and priced their products accordingly. While the law of averages might back up this conclusion, we all know that each individual driver is different.

Fortunately things are changing.

With modern technology insurers are able to gauge risk more precisely down to a micro-level. For example, insurance companies are now using telematics data to assess each driver based on driving behavior such as hard braking, acceleration, speed and distance traveled. But the usage of big data is not exclusive to auto insurance. Many life insurers are beginning to use the data from wearable devices as an extra rating variable. While property insurance companies are using data from sensors (Internet of Things) and working with vendors like Nest to recognize potential claims and hopefully prevent losses.

To learn more about how insurance companies are flipping the data equation and moving from experimentation to innovation download the research paper “Big Data in Insurance”.

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


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Is good…good enough?

“Garbage in, garbage out” is more than a catchphrase – it’s the unfortunate reality in many analytics initiatives. For most analytical applications, the biggest problem lies not in the predictive modeling, but in gathering and preparing data for analysis. When the analytics seems to be underperforming, the problem almost invariably lies in the data.

Entity resolutionInsurers typically have multiple legacy transactional systems often for each line of business. Hence when an insurers has an individual with profiles across multiple systems, it needs to be able to identify that as the same person and resolve different data variations into a single entity. In many cases entity resolution can be solved using simple business rules. Based on data items like date of birth and phones numbers. But this is not always sufficient. Insurers are now using advanced analytical techniques such as probabilistic matching to determine the actual statistical likelihood that two entities are the same.

However when it comes to data quality, there is one small anomaly. That is when the data is being used for fraud analytics. Insurance companies should be careful not to over-cleanse their data. In some cases, an error such as a transposition in a phone number or ID number may be intentional; that’s how the fraudster generate variations of data.

To learn more download the white paper “Fraud Analytics: Data Challenges and Business Opportunities”.

Insurance companies should not under estimate the amount of work required for data management. It’s not uncommon for over 50 percent of the implementation effort dedicated to data integration and data quality. However they should not over think the problem. While poor data will deliver poor results, perfect data is an unrealistic expectation. In most cases good data is good enough!

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


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How insurers can take a giant leap forward with big data

Oh, how times have changed during my 20-plus years in the insurance industry. Data wasn’t a word we used much back in the 80s and 90s, unless of course you worked in those arcane and mysterious IT data centres.

Even amidst the computerisation of the insurance industry in the 80s, many policy documents and related files were still paper-based. In those days the data being captured came from the company’s own staff and was keyed into a mainframe terminal. The most sophisticated ones received a bordereaux file from their insurance brokers, transferred through a kind of electronic data interface or courier process – and more often than not this was just a floppy disk received in the post.

Data Center

Today, insurance companies have more data than they can realistically handle. And the sources are numerous, including: in-house generated, aggregators, fraud bureaus, government agencies, telematics boxes and of course social media sites. This much data presents a pretty significant headache for insurance companies: where do you store it all, how do you access and understand what that data can tell you – and do so quickly. Most important of all, how do you profit from it?

What are the questions around “big data” that insurers today are grappling with?

Storing big data
First of all, lets define the size of the problem. We know that many insurers and insurance brokers produce anywhere between 300,000 and 700,000 quotes per day for the UK aggregator sites, and that telematics boxes can generate hundreds of data items per second. Deciding how much of this data to store and where to store it cheaply has become a serious issue for insurers. Cost is the primary challenge, but any storage also has to meet regulatory compliance needs, so you can’t just store this data anywhere.

Accessing and analysing big data
Storing all this data effectively and cheaply is only worthwhile if the business users can access it quickly. Only then can they can run the analysis and reports that they need. How do you do that when you have terabytes worth of data and many millions of records? Traditional data warehouse systems and Excel (the typical analysts’ tool of choice) struggle with these volumes, the speeds required and storing the growth in unstructured data. As such, insurers need to look at new ways to do this, such as Hadoop.

Visualising big data
For end users, one of the biggest problems with big data is simply being able to see the data fields and structures themselves. A few insurers have invested the time and effort to build good data dictionaries that help users to understand the data and associated metadata. But for everyone else, clearly labelled tables, fields and data values are extremely important if you want to get fast insights.

However, knowing the question is only half the problem. We also need to look at what insurers can do to address these issues. The first thing to know is that the answers are here today and are available for all insurance companies.

The data can now be quickly and cheaply stored in Hadoop and, with tools like SAS Visual Analytics, can be easily accessed and analysed by anyone with a mouse and a web browser. The ability to review and analyse vast quantities of data received from insurance aggregators in real time makes data insight immediately actionable. If you wanted to improve your quote to policy conversion, you could reduce your price by a percentage point. Or increase your price if, for example, you’ve reached your quota for young drivers. With technologies like SAS Event Stream Processing, this is a reality.

In the world of data, times have changed a great deal, but users need not fear the terms “big data” or “Big Data Analytics”. In those immortal words “we have the technology…”

If you want to know more about how to properly exploit big data using Hadoop, follow the eight-point checklist set out in this report.

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No more excuses.. Analytics IS a game changer

Good news...an analytics survey last year found that 72% of insurance executive agreed that analytics is the biggest game-changer in the next 2 years. Bad news...compared to other industries the adoption rates of analytics in the insurance has lagged other industries.

To reverse this trend and help insurers travel down the path to becoming an Analytic Insurer they must consider four things:

Business Strategy

- What data is accessible and obtainable?
- What type of analytic tools are needed and available?
- What type of resources are required? What are the right skill sets?
- What best practices or processes will facilitate the implementation of an analytic strategy.


Organizations new to analytics should start with small proof of concept and pilot projects and build on success to gain support throughout the organization. This will lead to buy-in from senior executives, increased funding and more ambitious projects. A win-win for everyone.

There is no “one size fits all” when it comes to analytic execution. To find out how you can transform your organization into the Analytic Insurer download a copy of the white paper “Building a strategic analytics culture: a guide for the insurance industry”.

To survive and thrive in the competitive insurance industry, carriers must implement analytics.

There are no more excuses.

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

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Help wanted

The insurance industry is heading for a crisis. Depending on which report you read the insurance industry is facing a shortfall in job vacancy from anything from 40,000 to nearly half million in the next few years. Baby boomers in specialized jobs like underwriters and claims adjusters are retiring and insurers are struggling to replace these employees.

Insurers face a classic good news / bad news scenario. On the positive side the industry is financially stable, has great career paths, good benefits and is almost recession proof. On the negative side the perception of insurance is an industry of laggards. The decision makers at the top are old-school, they struggle to relate with the millennial generation graduating from college. Insurance companies have got to be able to connect with these individuals and look cool because that is the “new normal”.

In an early Analytic Insurer blog I write how Data Scientist is considered the sexiest job of the 21st century. Ironically, the insurance industry is no stranger to the importance of analytics and has historically been success in attracting mathematicians and statisticians to the industry to work as actuaries.  But as more and more industries and companies develop data-driven initiatives the demand for these individuals with analytical skills has increased.  Therefore, attracting and retaining employees with such specialized capabilities remains a significant hurdle to overcome and a potential differentiator for those that do.

One insurance company that reversing this trend is XL Group. To get a better handle on pricing, XL Group created an analytical team led by SVP of Strategic Analytics, Kimberly Holmes to work on predictive, multi-variant analytics using internal and external data. After four years, XL has seen its claims per dollar of premium written fall significantly in those lines of businesses which were early adopters of predictive models. To read more about this case study and how analytical talent is driving a competitive advantage download the MITSloan report "The Talent Dividend".

The question for all insurers concerned with building their analytics capabilities is this: What is you plan for cultivating analytics talent?

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


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