Tag Archives: agent

Moving to Real-Time Risk Management

In insurance, sales are usually periodic, but risks are continuous. In personal lines, for example, annual or semiannual automobile renewals are automated, and a customer may not speak with an agent or a representative of an insurer for an extended period. Insureds do not receive continual rick consultation, because it is high-touch and high-cost, and can unintentionally retain risks. This is especially true during times of change. New activities, conditions or locations often increase exposure.

This post explores how technology can be combined with a customized service proposition to deliver continuous, real-time risk management. In the process, digital technology can reshape patterns of engagement between insurers and their customers that have existed for decades (or centuries).

Look at what happens every day as teenagers become drivers. As learners, their skill level is low. As drivers, they make poor decisions and crash more often. Parents try to supplement the teaching of driving schools but with mixed results. High loss frequency and severity for the 16- to 20-year-old age group, particularly males, drives insurance premiums to unaffordable levels. More significantly, many people are injured or are killed in accidents involving youthful drivers.

Now look at the approach taken by Ingenie, an insurance broker founded in the UK in 2010. The founders observed the safety and affordability issues in the UK motor market and set out to design a proposition to address both issues. At that time, telematics solutions were just beginning to take shape. However, Ingenie intended to go beyond a simple black-box-in-a-car approach. It partnered with the Williams Formula 1 team and used its racing experience and data to build sophisticated algorithms that analyzed driving patterns and predicted the behaviors that were most likely to result in an accident. Ingenie also engaged psychologists at Cranfield University to understand the specific emotional and physical characteristics of youths. With insights from these sources, Ingenie built an engagement approach focused on this age group.

Ingenie’s founders were veterans of the insurance software industry and had the technological skills to build a platform that blended social media, call center technology and an online app. The objective was to provide real-time feedback to influence driving behavior by communicating at appropriate intervals and in the most effective manner. As the telematics device in the vehicle reported the driving details, if the data showed that a young driver was performing better (safer) than her peers, she received a discount on her insurance. If the driving was not as safe as it could be, the driver received a text outlining what driving behavior could be improved, with a link to training videos and other multimedia sources. If the actions were severe, the driver was contacted directly by a call center employee of Ingenie. The company employs psychology majors from local universities, usually young men and women in their early 20s, in the service centers to counsel the youths and to speak to them on their own terms.

The model is proving successful. Between 2012 and 2013, behaviors improved such that average premiums dropped 23% for 17-year-olds and 10% for those who were 18. The broker has earned rapid growth in the UK market — 2013 premiums were more than $80 million. In 2014, Ingenie expanded into Canada.

By going beyond pure telematics, Ingenie delivers continuing risk control that previously had not been possible or affordable. Going forward, digital technologies will continue to provide similar opportunities across other lines of business – increasing both the efficiency and effectiveness of risk management.

What Comes After Predictive Analytics

Historically, “analytics” has referred to the use of statistical or data mining techniques to analyze data and make inferences. In this context, analytics typically explain what happened (descriptive analytics) and why (diagnostic analytics). If an insurer saw its customers moving to its competition, it would analyze the characteristics of the customers staying or leaving, the prices it and its competitors offer and customer satisfaction. The analysis would help determine what was happening, who was leaving and why. In contrast, predictive analytics focuses on what will happen in the future.

“Predictive analytics” has a fairly broad definition in the press but has a specific meaning in academic circles. Classical predictive analytics focuses on building predictive models where a subset of the available data is used to build a model using statistical techniques (usually some form of regression analysis — linear, logistic regression etc.) that is then tested for its accuracy with the “holdout” sample. Once a model with sufficient accuracy is developed, it can be used to predict future outcomes. More recent predictive analytics techniques use additional machine learning techniques (e.g., neural network analysis or Bayesian probabilistic techniques).

Insurers have used predictive analytics for almost two decades, but, despite its usefulness, it has two main drawbacks:

  • Focus on decision versus action: Predictive analytics can tell you what is likely to happen but cannot make recommendations and act on your behalf. For example, a predictive model on the spread of flu can determine the prevalence and spread of flu but cannot tell you how to avoid it. Similarly, a predictive model of insurance sales can determine weekly sales numbers but is incapable of suggesting how to increase them.
  • Reliance on single future versus multiple alternative futures: While we can learn from the past, we know that it may not be a good predictor of the future. Predictive models make linear predictions based on past data. They also make certain assumptions that may not be viable when extrapolating into the future. For example, regression requires the designation of a dependent variable (e.g., insurance sales), which is then described in terms of other independent variables (e.g., brand loyalty, price etc.). While this method can help predict future insurance sales, the accuracy of the numbers tends to decrease further into the future, where broad macro-economic and behavioral considerations will play a greater role in sales.

Prescriptive Analytics

In response, there are a number of firms, authors and articles that propose “prescriptive analytics” as the next stage of the analytics continuum’s evolution. Prescriptive analytics automates the recommendation and action process and generally is based on machine learning techniques that evaluate the impact of future decisions and adjust model parameters based on the difference between predicted and actual outcomes. For example, insurers could use prescriptive analytics for automatically underwriting insurance, where the system improves its conversion ratio by adjusting price and coverage on a continual basis based on predicted take-up and actual deviations from it.

However, while prescriptive analytics does address the first of predictive analytics’ drawbacks by making and acting on its recommendations, it usually fails to address the second shortcoming. Prescriptive analytics relies on a single view of the future based on historical data and does not allow for “what if” modeling of multiple future scenarios. The critical assumption is that the variables used to explain the dependent variable are independent of each other, which in most cases is not true. While the analysis can be modified to account for this collinearity, the techniques still fail to use all of the available data from domain experts. In particular, prescriptive analytics does not take into account the rich structure and influences among all the variables being modeled.

Complexity Science

In addition to prescriptive analytics, we believe that complexity science is a natural extension of predictive analytics. Complexity science is an inter-disciplinary approach to understanding complex systems, including how they form, evolve and cease to exist. Typically, a system that consists of a few well-known parts that consistently interact with each other in a way we can easily understand is a “simple” system. For example, a thermostat that can read (or sense) the temperature and reach a given target temperature is a simple system. At the other end of the spectrum, a system with a very large collection of entities that interact randomly with each other is a “random” system. We often use statistical techniques to understand the behavior of the latter. For example, we can gain an understanding of the properties of a liquid (like its boiling point) by looking at the average properties of the elements and compounds that compose it. The fundamental assumption about such systems is that its parts are independent.

In between simple and random systems are “complex” systems that consist of several things that interact with each other in meaningful ways that change their future path. For example, a collection of consumers watching advertisements, talking to others and using products can influence other consumers, companies and the economy as a whole. Complexity science rejects the notion of “independence” and actively models the interactions of entities that make up the system.

Complexity science identifies seven core traits of entities and how they relate to each other: 1) information processing, 2) non-linear relationships, 3) emergence, 4) evolution, 5) self-organization, 6) robustness and 7) if they are on the edge of chaos. Unlike a random system, the entities in a complex system process information and make decisions. These information processing units influence each other, which results in positive or negative feedback leading to non-linear relationships. As a result, properties emerge from the interaction of the entities that did not originally characterize the individual entities. For example, when a new product comes on the market, consumers may purchase it not just because of its intrinsic value but also because of its real or perceived influence on others. Moreover, the interactions between entities in a complex system are not static; they evolve over time. They are capable of self-organizing and lack a central controlling entity. These conditions lead to more adaptive behavior. Such systems are often at the edge of chaos but are not quite chaotic or entirely random.

Two parallel developments have led to complexity science’s increased use in practical applications in recent years. The first is the availability of large amounts of data (or big data) that allows us to capture the properties of interest within each entity and the interactions between them. Processing the data allows us to model each entity and its interactions with others individually, as opposed to treating them as an aggregate. For example, a social network is a complex system of interacting individuals. We can use complexity science to understand how ideas flow through the social network, how they become amplified and how they fade away.

The second development accelerating complexity science’s use is the inadequacy of classical or statistical models to adequately capture complexity in the global economy. Since the financial crisis of 2007/8, a number of industry bodies, academics and regulators have called for alternative ways of looking at the world’s complex social and financial systems. For example, the Society of Actuaries has published a number of studies using complexity science and a specific type of complexity science called agent-based modeling to better understand policyholder behavior. In addition, health insurers are building sophisticated models of human physiology and chemical reactions to test adverse drug interactions. As another example, manufacturers are modeling global supply chains as complex interacting entities to increase their robustness and resiliency.

Agent-based modeling is a branch of complexity science where the behavior of a system is analyzed using a collection of interacting, decision-making entities called agents (or software agents). The individual behavior of each agent is modeled based on available data and domain knowledge. The interaction of these agents among themselves and the external environment can lead to market behavior that is more than just the aggregate of all the individual behaviors. This often leads to emergent properties. Such models can be used to evaluate multiple scenarios into the future to understand what will happen or what should happen as a result of a certain action. For example, a large annuity provider has used individual policyholder data to create an agent-based model in which each one of its customers is modeled as an individual software agent. Based on specific policyholder data, external socio-demographic and behavioral data, as well as historical macro-economic data, the annuity provider can evaluate multiple scenarios on how each annuity policyholder will lapse, withdraw or annuitize their policy under different economic conditions.

In conclusion, as companies look to capitalize on big data opportunities, we will see more of them adopt prescriptive analytics and complexity science to predict not just what is likely to happen based on past events but also how they can change the future course of events given certain economic, political and competitive constraints.

Agent: What’s Your Plan This Year?

“What do you want to be when you grow up?” I used to get that question all the time. I would say I wanted to be a doctor, a lawyer, a successful businessman. I always had an answer, but I never had a plan. I would have benefited a lot from having the person follow up and ask me, “How are you going to get there?”

“How did you get into the insurance industry?” If you ask 10 insurance agents that question, nine times you get the same answer, “I just fell into it. I had no plan to be in the insurance business.”

I’ve spent most of my insurance career working and dealing with agents, and, while they have action items to grow their businesses, almost all of them don’t have a formal planning process. Instead, they react to issues the day they are confronted by them.

It’s natural to wait and react. But the best organizations in any industry always have a plan. They don’t react; they act with discipline and focus.

This article can provide you with a road map for designing your current-year business plan and your long-term plan.

A plan has to be something basic that you can live by during the year — not a 25-page document that gets put in a desk drawer and forgotten. Instead, it’s a short document that sets forth the path you want to take for your agency in a given year. Plans change. They always do, based on what actually happens. But having a plan allows you to be in control of your business.

Is it too late to have a plan this year? No. There is still plenty of time. Here’s a model I’ve used to develop several successful business plans.

1.         Start with an assessment of your business year-to-date. How’s your year going compared with last year? Is production up? How about profitability? Spend time analyzing your book of business and understand the difference between your results for the year-to-date period this year vs. last year. This shouldn’t take too much time.

2.         Identify your gaps. Profitability might be up but new business production down. Why is new production down? Is it taking more leads to generate a sale? Is a new competitor pulling business away from your agency? Understand your situation. Focus on the big issues.  Nothing is ever going to be perfect, including your business.

3.         Develop solutions. This is the toughest part of any planning exercise. It’s usually easy to identify a problem. It takes a lot more thought to come up with a solution, especially one that requires you to change the way you conduct your business. Try to identify little changes you can make. Pick a new lead source and experiment with it first. If it works, then incorporate it into your day-to-day operations. Implement several small changes at once. I call them “initiatives.” They are more like experiments. If they don’t work perfectly, that’s okay, because can always learn something new about your business that you can apply to your next initiative.

Let’s say new business production has fallen. It is taking your agency more leads to close a sale. One way to increase production is to increase the number of leads. That will probably increase costs because you have to purchase more leads or need additional staff to generate new leads. That will hurt your agency’s profitability.

Yet, that’s what most people do. I call it the “Do What You’re Doing, Just Do It Better” strategy. It typically fails.

Instead, focus on new tactics. Change the way you are conducting your business. Experiment, experiment, experiment! Try different initiatives. You will typically know if they are working fairly quickly. Don’t be afraid to stop doing something if it is not working. Move to the next idea and continue to iterate.

In our example, a new initiative might be to target a specific group of potential customers based on criteria you develop that makes them attractive customers. Another initiative might be to develop an affinity group that you can then target for new business. If the initiative works, you can incorporate it into your business. If it doesn’t — and you will typically know within 30 to 60 days — move on.

4.         Create check points. You can’t expect what you don’t inspect. Track your agency’s results on a daily, weekly and monthly basis. Meet with your staff consistently. You want to create a culture of accountability.

5.         Be transparent. You need to share your plan with everyone at your agency. Make sure your team is incorporating the overall plan into their day-to-day duties. Have you properly communicated and delegated specific initiatives to your staff? Is your customer service rep up-selling? Is your receptionist setting appointments when the office is quiet? If people don’t know what you are trying to do, they will just do what they think you want them to do.

6.         Stay focused. Plans fail when people lose focus. Your job as the leader of the organization is to keep the organization on the right path. A well-defined plan provides the framework to make sure you are staying the course. It enables you to make sure everyone is doing what needs to be done.

Nothing lasts forever. Yet it is surprising how few agency owners have a long-term plan for their business. Most agencies die a slow death, keeping the agency owner a prisoner of her own business as the staff leaves and she tries to hold on to renewal commissions as long as possible.

I attribute this common situation to the fact that most agency owners don’t have a long-term plan for their agency and for their personal life. In the early years of an agency, everything is focused on producing new business. As the agency matures, the service requirements of operating a P&C agency create daily challenges that keep the agency owner’s attention occupied. It’s easy to procrastinate until it’s too late.

Stop reading this article. Grab a pen and paper and answer the following question: How do you want to leave your business? As a thriving organization that survives you? A business you can pass on to your children? To your junior partners? A business that you can sell? What’s your vision for the future of your agency?

Spend some time today and put together your plan for the long-term future of your agency. Knowing where you want to be tomorrow, today, will make it more likely you will end up where you want to be.

Friday Tip For Agents & Brokers: Your Best 30 Seconds

In this video, Tom Searcy — an expert in large account sales and the founder of Big Hunt Sales — shares with agents and brokers three keys to maximizing their influence when first meeting with potential clients.

Recent research has revealed these three baseline concepts:

  1. Information = Confidence. At your first meeting, your level of confidence will rise with the amount of information you have about the people with whom you are meeting. Doing your research before the meeting (through LinkedIn, Google, their website, etc.). Knowing background information on your potential client's representatives will give you something to talk or ask about.
  2. Body Language. Eye contact, smiling, and handshake are common elements of the first meeting. But do you know which of the three should last the longest?
  3. Say Something Specific.Use your research to introduce a question or comment that will help you frame future conversations.

Watch Tom's video above to learn more.

Outside Looking In

I found myself arguing an extremely silly point with an agency owner at a conference.  Everyone but the agent saw the silliness of his argument.  I explained the point every way imaginable, to no avail.  I could see from the looks on others’ faces, they were tiring of him not getting the point either.  If he had been one of the audience members watching someone else argue, he probably would have seen the errors in his thinking, too.  But, sometimes you just have to be outside looking in to see a point.

As a consultant, I very often find myself facing this type of situation.  The four points below are the most common positions held by agency owners that create immovable and serious roadblocks to their agencies’ success.  If any of these sound familiar to you, take a step outside of the situation and look back in.  You might see your position in a different light.

1.  We must write small accounts because you never know which one will turn into a large account. This commonly expressed position presumes an inability to identify clients with great potential versus those with no potential.  This means agencies believing in this philosophy should write absolutely as many small accounts as possible. 

An average agency abiding by this philosophy has at least 1,000 small accounts and maybe one, over 10 years has grown big.  But let’s say there are two accounts that grow big.  So out of 10,000 renewals, two get big.  Can the agency write enough large accounts to cover the 1,000+ small accounts that soak up huge amounts of time, effort and expense?  If so, this may be a great strategy.  If not, it is time to rethink the agency’s strategy.

2.  We do not use coverage checklists because we might leave something off.  The belief here is that if you don’t have a list, you can’t leave coverages off.  This presumes nothing is left off when a checklist is not used.  So if an insured does not get the correct coverage because the producer does not use a list and the absence of a list means the coverage wasn’t necessary to offer, then by default, the customer could not have needed the coverage and therefore, the uncovered claim is just a figment of their imagination.  Right?  If you believe this, then keep on going without using coverage checklists.

Another perspective is that if the agent does not use a coverage checklist, there is no need to recommend coverages a customer needs.  In other words, if I don’t know the customer needs a coverage, I don’t have a responsibility to offer the coverage.  For a peddler of insurance, this makes perfect sense because peddlers only take orders.  Why pay commissions to peddlers?  Web sites are quite capable of taking orders and issuing policies.

3.  We do not need to hold our producers accountable.  The reasons given for not holding producers accountable are numerous and include that accountability might make them angry.  What is the price of an angry producer?  In some cases, say $500,000 commission producers, not making them angry might be a good strategy.  But is the price reasonable for not making $100,000 producers angry?  An incompetent producer may leave the agency or become a good producer through accountability.  Either way, the agency may find itself way ahead by enforcing accountability.

Other common reasons given are that they are nice guys and that they have never been held accountable so it is unfair to do so now.  That is fair enough.  But to be really fair, if the producers are not held accountable, why hold anyone accountable?  Why hold the customer service representatives (CSRs) accountable?  Why hold new producers accountable?

Another reason given is that by holding them accountable, the ultimate outcome is that they would be fired and the emotional trauma of firing a producer is too much.  That makes sense.  Of course, if you are not going to fire a producer, how can you fire a CSR?  Is their trauma any less?

Then there are the producers that should hold themselves accountable negating the need for management to do so.  How well is that working in your agency?

4.  All agencies have the same value as a multiple of sales or EBITDA.  I am often asked, “How much are agencies worth today?”  This presumes that all agencies are alike, all agencies are commodities and nothing is special about any agency.  Is this correct?  Is there nothing special about your agency?

Let’s assume some common multiple applied to all agencies.  If one agency is losing 10 percent of its commissions annually and another agency is growing by 10 percent, then they should have the same multiple.  The same goes for the agency that has a 25 percent profit margin versus the agency that has a -5 percent profit margin.  Even the agency that has $1,000,000 of extra cash on its balance sheet versus the agency that has spent $500,000 of trust monies will have the same value. 

The question presupposes such material differences do not exist.  It’s like someone is asking, “What’s the value of a 2005 Ford F-150?”  They expect I can look up the blue book, ask how many miles the agency has on it, the condition of the body, and whether it has any extra features.

Quite often, the agency owners who ask this question have problematic agencies and the reason they ask the question this way is because they do not want their problems taken into consideration in the valuation. 

I do not believe any reader likes the logical result of these incredibly common beliefs and practices.  I’m not going to argue these ideas are wrong.  If you share these beliefs, take a step outside and look back in.  Think through the complete concept and if you still believe in it, then go for it 100 percent!