Business Intelligence: Putting the Power of Analytics to Work to Improve Results

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1 Business Intelligence: Putting the Power of Analytics to Work to Improve Results

2 Business Intelligence: Putting the Power of Analytics to Work to Improve Results You Can t Afford to Do Nothing: Why You Need Insurance Analytics Now Do you remember Mom and Dad saying, Just because everyone else is doing it doesn t mean you should? Well, when it comes to analytics in insurance, just because everyone else is doing it, you must do it as well. Who s it for? Small to medium-sized insurance companies that want to use analytics to gain the insights needed to maximize business results. Summary: Your competitors are using analytics and so should you, no matter the size of your organization. The smart use of data is critical to improving decision making and avoiding shoot from the hip decisions and associated losses. This ebook can help you learn how to identify the most valuable metrics, how to use visualization to dig even deeper into cause and effect, and use your data to improve long-term results. For years now, the large insurance companies have been all over analytics and big data (even before we started using those two words), using it to improve business decision making. According to a study by Earnix, 100 percent of billion-dollar insurance companies are using predictive analytics in personal lines, and 88 percent of them are using that science in commercial lines. The perception may be that analytics and Big Data are the domain of large companies; they have the money and the data. True, but with today s technology, small and medium-sized companies can do a lot. Many smaller insurance companies believe they don t have enough data or their data isn t good enough to do analytics. Not true, and furthermore it s a dangerous assumption that can lead to the dreaded trap of adverse selection. How? Well, let us explain. Everyone in this industry knows Progressive Insurance, but do you know it took Progressive 50 years to become a $1 billion company, only seven more to get to $2 billion, and just two more years to pass $3 billion? They grew, at least in part, because of their ability to classify auto risks better than anyone else. Let s assume the fictitious Analytic Insurance Company has invested the time and effort in analytics, while the fictitious We ll Wait Insurance Company has not. Through its analytics, Analytic Insurance has learned it is charging too little related to the higher-risk client group but too much for a lower-risk client group. The company adjusts pricing accordingly for each risk group and ultimately sees many of its higher-risk clients leave, while retaining a very high percentage of its lower-risk clients. Those higher-risk clients move their policies to We ll Wait, because it is 2

3 offering a much lower rate than it should because of its lack of data analysis. In addition, We ll Wait sees its lower-risk clients bail and go to Analytic, since their rates are now much more favorable. At the end of the day, Analytic Insurance is better off, while We ll Wait has fallen victim to adverse selection. Which company are you? Which one do you want to be? Now, adverse selection is but one example one reason, if you will, to get in the analytics game. The more you know about your insurance risks, claims, and clients, the smarter you become. And the smarter you become, the better decisions you make. Don t let the fact that you re a small or mediumsized insurer prevent you from investing in analytics. Your projects don t have to be the same scale or price of the big companies. Reasonably sized projects can provide much insight. There are many analytics solutions available that can fit whatever size investment you think you may be able to make. Take some time, have some conversations about what options may be available for you, and keep in mind it s a long-term investment in your business. The benefit of doing analytics on your data is far too important. You should do what you can afford, but ultimately you can t afford to do nothing. Analytics Leads to Logical Thought, Not Hip-Shooting After insurance companies spend all kinds of time and money on business intelligence (BI) projects, why do they often question, if not lament, the investment and start redirecting future IT investments elsewhere? The primary reason is that nothing changed after implementation. The BI system produces all kinds of interesting information, but it s only looked at now and then and discarded before the decision maker gets on with making those decisions! Why is that? Bad planning, bad execution, or just bad luck? Or does it go deeper than that? Two types of thought that impact decisions In his book Thinking, Fast and Slow, Nobel Memorial Prize in Economic Sciences winner Daniel Kahneman describes two modes of thought: System 1 thought, which is fast, instinctive, and emotional, and System 2 thought, which is slower, more deliberate, and more logical 3

4 One could argue that a lot of insurance decisions require good System 2 thinking. So what gets in the way? Well, complex problems and related decisions typically require probabilistic thinking. These aren t simple or binary choices. Think of the weather: We don t say it s going to rain tomorrow or not rain tomorrow; rather, we speak in terms of the probability of rain and then make our plans accordingly. Breaking down a business problem and thinking of probabilistic alternatives, however, is really hard for most of us. So we typically turn to heuristics (individual evaluation) to make complex decisions. While heuristics can be a simple, efficient, and reasonable way to make decisions (e.g., using today s weather to forecast tomorrow s), it often leads to errors in judgment or cognitive biases. What are some of these biases? The weather forecast is good example of Recency Bias, when we tend to assign too much weight to the most recent events. Like maybe losses? With Attentional Bias, we tend to place too much weight on a specific event or criterion. Say you are a state manager focused on your state s loss ratio. When evaluating a restaurant risk, you may give the relativity to the state too much weight versus the class of risk. The Gambler s Fallacy Bias is the tendency to think future probabilities are altered by past events, when in reality they are unchanged, as in, We have to get off this risk; it s due for a loss! Related is the Hot Hand Fallacy Bias, the belief a person who has experienced success has a greater chance of further success in additional attempts. It s not true in basketball, and it s not true in underwriting. And one more, the Focusing Effect Bias. This is the tendency to place too much importance on one aspect of an event, that one bad loss which inflates the loss ratio. But maybe we should be more concerned about frequency and not severity? The list of biases goes on and on, but Kahneman best summarizes biases this way: We can be blind to the obvious and we are also blind to our blindness. 4

5 So how do we counter biases? How do decision makers stay the course of logical, System 2 thinking? Through some probabilistic help we need some math! It takes embedding this math, also known as analytics, into our systems and processes to make those deliberate, logical decisions. Add Power to Data With Visualization So it s time to embark on your BI journey. Where do you begin? Start small with data visualization. Most people are visual, and seeing well-designed charts and graphs allows them to more quickly absorb information than they could with a traditional report. Take a look at the example dashboard below. The direction and color of each arrow tells you two things: whether the trend is up or down and whether that s good or bad. Charts and graphs deliver a message much more effectively than data alone. Think about the spreadsheets or the paper reports you use. Do they really speak to you? Looking at the dashboard, it is really easy to see that Claims Paid is up (see the 33 percent and the arrow), and it is equally easy to see that this is a bad thing (arrow is red, not green). And what about that tall blue bar in the graph at the bottom? It kind of jumps out at you. If you were looking at a traditional claims paid report by line and state, with a total section, do you think you would catch the June 2014 anomaly in that 30-page report? Maybe, but if it were a race, you would see it more quickly with this data visualization. 5

6 Visualization provides illumination Perhaps you ve heard the following statement before: Statistics are often used like a drunk uses a lamppost; for support, rather than illumination. Andrew Lang ( ) is credited with the original quote: An unsophisticated forecaster uses statistics as a drunken man uses lampposts for support rather than for illumination. Think for a moment about this quote in the context of traditional reporting. Traditional reporting is all history. It therefore supports past actions and decisions, but doesn t provide much illumination. You may ask, How does simply visualizing that data add any illumination? Just like the dashboard example above, it adds a lot of insight, and more quickly. But if we stop with simply taking year-over-year (YOY) premium and graphing it, it doesn t add much. The key is getting out of Excel and into one of today s data visualization tools. It is then a short step to get from that bar graph of YOY premium to YOY premium by state, then by line of business, by agent, and on and on. Another big advantage of visualization is that answering one question usually leads to more questions such as, So I see Claims Paid is up 33 percent, but where and why? With today s visualization tools, you can easily drill into the data, so when one answer leads to additional questions, you can probably answer those too (if the visualization is designed well). The BI journey is a fun one and, like any journey, it starts with the first step. Don t get left behind by the other companies that have started. Get to the Right Data by Starting in the Right Place We have talked about insurance companies spending all kinds of time and money on BI projects, then questioning that investment after the fact. Time and again, the primary reason we see for questioning the investment is that nothing changes after the project. The BI system produces all kinds of interesting information that is looked at now and then then discarded before the decision maker gets on with making those decisions! The issue is that BI projects often move from right to left. That is, we start with the data we have available; we ensure it is nicely modeled, defined, 6

7 and inspected; we extract it into a user-friendly and performant BI platform; then we present it to our business area with appropriate fanfare (and some training) and await the accolades we truly deserve for all this hard work. And of course, we hear nothing Moving left to right The solution is that BI projects need to move from left to right. That is, we need to start with the decision maker and identify the decisions or actions they make and take and which decisions, if made poorly, will cost the most money. From there we determine the questions to be answered or metrics to be collected to support such decisions. And from there we identify the data and sources and all that systems stuff we are getting good at doing. Now this sounds simpler than it is. We have to ferret through all the information the decision maker will say he or she wants, but the reality is not all of it is truly used to make a decision. For example, we know how much information underwriters will ask for given a blank canvas, but when you examine the actual decision-making process, don t be surprised to find some of it falling into that interesting but discarded pile. A couple of examples Example 1: An insurance company wants to assign its more experienced claims adjusters to its larger, more complex claims. That decision is supported by several factors, including the knowledge of an attorney engaged by the claimant, and the source of that knowledge is an indicator in the claims system. So ensuring that single attribute finds its way into the claims manager s dashboard would be valuable. Example 2: A claims manager understands the loss notice-to-settlement journey begins with the first interaction between the adjuster and policyholder; therefore, the claims department sets a goal to touch every claimant within 24 hours. The decision the claims manager wants to make is which adjusters could use some more coaching. An important factor is which adjusters are not making that first contact within 24 hours. That data is sitting in the claims system in the form of the date/time of the first notice of loss and the date/time of the first activity entry, so this is another important attribute to get into the dashboard. 7

8 Begin with the end in mind and work left to right, starting with the decision maker. All your hard work getting the data, cleaning the data, and loading the data into that BI technology will allow the proper accolades to roll in. Predictive Analytics: Where to Put Your Focus When an insurance company decides to get into the predictive analytics game, how does it decide what to focus on first? At the risk of providing just a me too perspective, we can look at what other companies are doing. The best glimpse of this is a survey done jointly by Earnix and ISO. This survey represents a nice mix of personal lines and commercial lines and a book-size range of the expected very large carriers, but also a healthy mix of smaller carriers (about 60 percent of respondents from companies with less than $500 million premium). When asked which areas of respondents companies always or frequently use predictive modeling, the results came back like this: Pricing/Rating 81% Underwriting 52% Marketing 27% Claims 21% Reserving 18% So should this be your order of attack for implementing predictive modeling? As you think about this question, let s take a look at a football analogy (sorry if you re not a fan). Do you remember the emergence of the read option à la Russell Wilson a few years ago? Lately, it seems as though every team feels the need to find out how to get the read option implemented in its offense. So sometimes me too is necessary simply because the bar has been raised, but you need to put your business in the lens first. How and where will predictive analytics give you lift? We have talked about prioritizing those decisions that, if you get wrong, cost you a lot of money (or better, if you get the decision right and make a lot of money). Analytical marketers long have been referring to this as the lift you get from making a change or an investment, in this case an investment into a predictive model. 8

9 Let s dig deeper into some of these areas and think about the lift: In the pricing/rating area, the main use cases are loss cost modeling and tiering. This is expected because that s where our actuaries have been living for many years. An emerging use case is customer demand modeling, where we get into the specific customer factors that can impact their behavior. In other words, we can have a good price, but how elastic is that price to our customers? Do you know if you are leaving money on the table? In claims, the big three use cases are fraud investigation, loss/reserve forecasting, and triaging. So where s your best lift? Is your product and policyholder book prone to fraud? Are you at a surplus point where you just need to get reserving under control? Is your claims work force full of wily veterans near retirement with lots of new hires? In marketing, the major use cases are customer retention, cross-selling, agent performance management, and lead generation. Again, examine these use cases using your specific business lens and where the greatest lift might be. Are you leaking customers when your differentiator is supposed to be customer intimacy? Are you a multi-line carrier with too many policyholders holding just a single product? Are your bottom agent performers staying flat and not improving? Are you (and your agents) spending lots of time to generate quotes only to find they don t fit your ideal risk profile? The good news is that predictive analytics is within reach for your company. It s no longer the province of only the big boys. The applications beyond insurance s actuarial heritage are doable for the vast majority of insurance companies. The key is to put your time and money on those use cases with the biggest lift for you. Think others may be interested? Share this ebook: 9

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