Location, Location, Location

If you buying or selling a house. The relator will tell the value of the property is all about location, location, location. For insurance companies location is just as important.

For an underwriter assessing the risk on a property is essential that they consider the location of the property. How close is it to the coastline? Is it a flood plain? Where is the nearest fire station? It is even important to know the types of businesses in surrounding properties. A clothing manufacturing next to a fireworks store is a greater risk than if the neighboring property sold sports equipment. Traditionally underwriters have relied on quotation data from the insured or agent, plus information from local adjusters. Now underwriters are supplementing that information with geo-spatial data.

geo spatial

 

Of course, it is impossible to predict exactly where a natural disaster will hit, but quickly predicting the exposure is essential when catastrophe strikes. When torrential rain inundated the city of Calgary in June 2013, Aviva Canada used SAS Visual Analytics to quickly determine which customers were most affected. By combining policy data with geocoding software, SAS provided Aviva Canada executives with a dashboard to easily monitor the amount of claims coming in. This resulted in speedier claim fulfillment, lower fraud rates and better customer retention. Read more about the AVIVA Canada case study.

 

There are many other business applications for location analytics within insurance. One example is marketing. Insurance companies have always created customer segmentation. However now insurance companies are can create micro-segmentation down to a hyperlocal level.   Overlaying internal customer data with behavioral and demographic information, marketing analysts can identify sales gaps and untapped opportunities.

Another example is using geo-spatial data to quickly find emerging trends in fraudulent and suspicious activities. This video shows how quickly and effectively an insurance company could reveal anomalies in the data. In this instance, by analyzing the data the insurance company was able to discover that the spike in claims were related to unscrupulous, out-of-state, contractors targeting elderly policyholders for unnecessary property repairs.

SAS Visual Analytics and location analysis empowers underwriters, adjusters, marketing specialists to better assess risk, be more efficient and increase revenue. Are you using it?

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

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Customer experience conundrum

Who is your best customer?  The answer to this question can vary dramatically depending on your industry. A retailer’s best customer is someone who comes back to their store over and over again. A gym owner’s best customer could be considered consumer who pays their monthly on time but never uses the gym. For insurance companies it could be argued that their best customer is closer to the gym analogy than a retail customer. The policyholder who pays their premiums and never makes a claim.

Acquiring new customers is expensive, so keeping the ones you have and increasing wallet share is critical to success. It’s a multifaceted challenge that often comes down to customer experience. The conundrum for insurers is how to improve the customer experience when you have little interaction with your best customers.

A key insight is to know your audience; understand your customers and what drives their behavior. Whilst demographic variables give you a good idea of what a customer looks like, many insurers are finding that behavior data is a much better predictor. Examples of behavioral data would be marriage, moving to a new house or having a baby. But just as important as identifying these event-driven variables and who to target, is also knowing who NOT to target. By removing these non-buying customers from marketing campaigns helps increase acquisition rate, reduce marketing spend saving millions of dollars per year.

One organization with a reputation for exceptional customer service is USAA. A financial services company providing insurance to US military and their families. Doug Mowen, Executive Director of USAA’s Data and Analytics Office recently shared his insights with using analytics for improving the customer experience. To read these insights download the conclusion paper “Five Keys to Marketing Excellence”.

Measuring the impact of customer experience efforts is difficult. Most insurance companies struggle to tie customer experience investments to the bottom line. But ultimately insurance companies that embrace analytically driven customer experience management can get an edge on the competition by enabling better customer experiences, which in turn creates value for their organizations.

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

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Time is precious, so are your analytical models

The analytical lifecycle is iterative and interactive in nature. The process is not a one and done exercise, insurance companies need to continuously evaluate and manage its growing model portfolio. In the last of four articles on the analytical lifecycle, this blog will cover the model management process.

Model management is a critical step in the analytics lifecycle. It involves validating a model, selecting a champion model, and monitoring the performance of your models on constant basis to decide whether to retire upgrade current models or create new ones.

Let’s explore some important best practices for model management:
Model validation

Model Validation and Comparison
With advances in modeling software and computing performance, it’s both easy and feasible to build multiple candidate models for use in analyses. By experimenting with different binning definitions, analysts can efficiently select alternate input variables, model techniques, and build multiple models. But before using these models, insurance companies need to evaluate them to determine which ones provide the greatest benefit.

 

Model Monitoring
To be effective over time analytical models need to be monitored regularly and adjusted to optimize their performance. Outdated, poorly performing models can be dangerous, as they can generate inaccurate projections that could lead to poor business decisions. As a best practice, once a model is in a production environment and being executed at regular intervals, the champion model should be centrally monitored through a variety of reports, as its performance will degrade over time. When performance degradation hits a certain threshold, the model should be replaced with a new model that has been recalibrated or rebuilt.

Model Governance
For insurance companies, greater regulatory scrutiny increases the need for model transparency. However, keeping records of the entire modeling process – from data preparation through to model performance – can be time consuming and unproductive. As a best practice, deploy model governance tools that enable you to automate model governance – complete with documentation to create qualitative notes, summaries and detailed explanations, as needed. Having an efficient model governance approval process can also save you time and money, as it can help insurers avoid audits and regulatory fines

To learn more about the model management process, download the white paper “Manage the analytical lifecycle for continuous innovation”.

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

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Putting predictive analytics to work.

Insurance relies on the ability to predict future claims or loss exposure based on historical information and experience. However, insurers face an uncertain future due to spiraling operational costs, escalating regulatory pressures, increasing competition and greater customer expectations.

More than ever, insurance companies need to optimize their business processes. But what does that mean in practice?

In the third part of a series of articles on the analytical lifecycle, this blog focus on an analytics ecosystem for decision making.

Traditionally analytics has been seen as a back-office function. Implemented in silo by different departments and lines of business. Today it is becoming essential that insurance companies embed analytics into real-time business decisions by deploying predictive models into transactional systems like policy administration, claims management or even call centers.

Whether it be underwriters, claims adjusters, risk managers, insurance personal make hundreds and thousands of operational decisions each day that impact an insurance company. For insurance companies that rely on analytical models in the decision process, SAS Decision Manager provides a single point of control to integrate sophisticated analytical models with their business rules. The solution takes data, business rules and analytical models and turns them into consistent, automated actions that drive faster, better operational deci­sions. In addition, these business decisions are now governed, traceable and fully documented which is essential in the heavily regulated insurance industry.

One organization that recognized that to stay ahead of the competition, it had to manage its information as a strategic assets was Australian insurance company, IAG.  They use SAS High Performance Data Mining to analyze its growing database. By using analytical models that used to take hours can be reduced to minutes. Read more about the IAG case study.

Today, insurance companies are becoming data intoxicated as they consume more and more data. But the true value of big data lies not just in having it, but in being able to use it for fast, fact-based decisions that lead to real business value.

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

Demystifying analytics

word cloudThere is no doubt that analytics is an overused and often abused term. So what does really analytics means?

In part 2 of a series of articles on the analytical lifecycle, this blog will highlight some of the common and emerging techniques used to analyze data and build predictive models

 

Exploratory data analysis
The science of extracting insight from data is constantly evolving. But regardless of how much data you have, one of the best ways to determine important information is through exploratory data analysis. Some of the most valuable ways you can do this are:

  • Line graphs
  • Bar and pie charts
  • Scatter and bubble plots
  • Correlation matrices
  • Clustering

Predictive modeling techniques
To gain an edge in today’s competitive market, insurance companies need advanced analytics solutions that reveal hidden patterns and insights. A few of the most valuable predictive modeling techniques are:

  • Regression models
  • Generalized linear modeling
  • Decision trees
  • Forecasting

Emerging analytical techniques
The digital age has brought with it a quantum increase in the amount of data available to companies. The vast majority of new data being created is semi-structured and unstructured data, both of which require new analytical techniques to generate insights. Several of the most important, emerging analytical techniques used to analyze big data are:

  • Link analysis
  • Textual analytics
  • Word clouds
  • Sentiment analysis

Analytics is no longer a luxury. It has become fundamental to the success and growth of insurance organizations worldwide because it supports better decision making about customers, prices, offers and more. The key is turning the massive amounts of data your company has collected – and continues to collect – into timely, trusted insights and competitive advantage. The best way to do this is through proven analytical techniques. To learn more about the available analytical capabilities download the white paper “Demystifying Analytics: Proven Analytical Techniques and Best Practices for Insurers”.

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

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Data is King

In my last blog I detailed the four primary steps within the analytical lifecycle. The first and most time consuming step is data preparation.

Many consider the term “Big Data” overhyped, and certainly overused. But there is no doubt that the explosion of new data is turning the insurance business model on its head. There is more data and more access to data than there has ever been and it’s growing. The challenge for insurers is how do you take advantage of all this data to price better, expand your market and improve the business of underwriting risk and handing claims.

In the past insurance companies have relied on “old data”. Information from policy administration solutions, claims management applications and billing systems, often supplemented by 3rd party data such as census data, motor vehicle records (MVRs), medical reports and Dun & Bradstreet to name but a few. However today, insurance companies are incorporating “new data” such as credit scoring, social media such as Facebook and Twitter, telematics from in-car data recording devices and geo-spatial information like Google maps. Unfortunately many insurers are drowning in this vast amount of data and struggling to digest it for meaningful insight.

Data management

To combat the existing silo approach and to alleviate problems with growing amount and diversity of data, insurers are undertaking enterprisewide data management projects. One organization that successfully implemented an enterprise data warehouse is Zenith Insurance. Due to a period of rapid growth the management team were relying on reports and information pulled from numerous departments and disparate systems. Because of the manual process, inconsistency in data, as well as the time and resources required to collate the reports, was proving inefficient. To resolve this problem Zenith rolled out a coherent data-management strat­egy and brought all the company’s data into a SAS Enterprise Data Warehouse. Read more about the Zenith Insurance success story.

The foundation of a successful analytics operation is quality data, and superior data management. There are many examples of where data defects and inaccessibility to data can result in increased operational costs, potential customer dissatisfaction and even missed revenue opportunities. Clearly there is a business case for creating a single, unified environment for integrating, sharing and centrally managing data for business analytics.

To learn more download the white paper “Data is King”

Unleashing the full power of business analytics should be on the short list for every insurer. According to a recent survey 63% of insurers are planning to increase their spending on analytics in the next 12 months.  The path to maximize the investment in business analytics is data. Data management and data quality are no longer optional components of an analytical environment they are essential.

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 steps to using analytics…successfully

Advances in technology, evolution of the distribution channels, demographic shift, economic conditions and regulations changes. How does an insurer prioritize all these seemingly competing goals and create sustainable competitive advantage. One answer is analytics.

Many insurance companies are just beginning to take steps toward becoming an “analytic insurer” – one that embeds analytics into daily operations to make better decisions that reduce costs, improve pricing, and more. And those organizations with more advanced analytic capabilities are actively seeking to build on previous successes and grow their analytic capabilities. And no wonder, given the increasing volumes of data being produced through enterprise business systems, online interactions, social media, and other channels.

Analytical Lifecycle

Implementing analytics is not as straight forward as it sounds. There are many steps in the analytical life cycle to consider, but essentially it can be broken down into four main sections:

1. Data preparation

2. Analysis and predictive modeling

3. Deployment

4. Model management

 

In a series of four blog articles over the coming weeks each of these areas will be discussed in more detail.

Turning the increasing volumes of data into useful information is a challenge for most organizations, but following these simple steps insurance companies will be able to implement analytics…successfully.

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

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Data governance - the new prodigal child

The old adage is that “Data is the lifeblood of the insurance industry.” However, for many insurance companies, data is like the red-headed stepchild. No one is willing to take care or have responsibility for it. In the past, insurance companies have created data governance programs, but these have often failed or underperformed due to either:

  • Business executives and managers consider data to be an “IT issue.”
  • The return on investment (ROI) for data governance isn’t clear.
  • Linking governance activities to business value is difficult.
  • Organization structures are fragmented with multiple data silos.

data governance

 

Fortunately, today, things are changing.   C-level executives recognize the need to manage data as a corporate asset. In fact,  you will see a Chief Data Officer (CDO) appearing in boardrooms of many organizations. The primary responsibility  of the CDO is to ensure that data will be managed as a shared asset to maximize business value and reduce risk.

 

 

To help achieve this objective, the CDO will create a team of data stewards who are go-to data experts serving at the point of contact for data definitions, usability, questions and access requests. A good data steward will focus on:

  • Creating clear and unambiguous definitions of data
  • Defining a range of acceptable values, such as data types and length
  • Monitoring data quality and starting root cause investigation when problems arise.
  • Understanding the usage of data in the business units.
  • Reporting metrics and issues to the data governance council.

As insurance companies continue to become more data-driven, their success will ultimately hinge on the ability to maintain and use a coherent view of this data. Better data can drive insight and help insurers make better decisions. To learn more about on this subject download the white paper “SAS Data Governance Framework: A Blueprint for Success.”

Today, data governance is no longer an afterthought. With the emergence of Chief Data Officers, insurance companies are now treating data like the prodigal child.

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

 

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Insurance and the rise of the Chief Risk Officer

The role of insurance is to bring some predictability, manageability and stability to a chaotic and uncertain world.  In essence, it is a risk mitigation tool.

The role of the Chief Risk Officer (CRO) is to manage the overall risk strategy for the insurance company. They are responsible for defining the medium to long-term risk strategy for their insurance organization.

This strategy takes into consideration variables such as risk appetite, target market, customer segments, core products, distribution channels and expected return on investments. This is achieved by what is commonly referred to as the capital management and planning process.

The first step in the process requires insurers to identify and model all material risks that can potentially affect their solvency or the long-term value of equity. To have an efficient capital management framework, insurers also need to coordinate the actions of their risk units with their actuarial and finance departments. Planning and budgeting exercises that steer direction for operational actions should be coordinated with a view into risks, profitability and shareholder returns.

The second step is the necessity to align their decision-making process with estimates for how much capital the organization must have on hand in light of commitments and identified risks. This helps business line managers perceive the constraints and opportunities that economic capital presents in the areas of risk-based pricing, customer profitability analysis, customer segmentation and portfolio optimization.

With an effective capital management, insurers should be able to weather extreme internal risk events (e.g., a large operational risk event) and external scenarios (e.g., a catastrophic natural disaster) at an enterprise level. It also helps business line managers create favorable opportunities, as they can generate an optimized risk-return profile of their product portfolios.

ORSA dashboardA new responsibility for many CROs is the emerging Own Risk Solvency Assessment (ORSA) that is required for Solvency II and other insurance regulations. One of the fundamental requirements of ORSA is that companies conduct an annual, forward-looking assessment. The goal is not only to demonstrate that the company’s current capital needs are appropriate, but also that its future capital needs will be met over a specified assessment time frame (usually three to five years). The report also allows regulators to get an enhanced view of an insurer’s ability to withstand financial stress.

With the recently launched SAS Capital Planning and Management solution, CROs can perform the quantitative aspects of an ORSA, taking into account projected balance sheets, income statements and risk appetite with the capability for iterative scenario analysis and stress testing.

To learn more about emerging insurance regulations,  download the white paper “ORSA: The New Kid in Town”.”

Insurance and risk have always gone together.  However,  as insurance becomes more complex and sophisticated,  the rise of the CRO is inevitable. and the importance is undeniable.

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

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Welcome to “data-driven decisions”

Business analytics is about dramatically improving the way an organization makes decisions, conducts business and successfully competes in the marketplace. At the heart of business analytics is data.  Historically, the philosophy of many insurers has been on collecting data, data and more data. However, even with all this data, many insurance carriers are struggling to achieve better analytics and gain business insight simply because many insurers’ systems typically aren’t designed to master the data in such a way to make it useful for analytical purposes.

data driven

 

The best way to maximize the efficiency and effectiveness of data-driven decision making is to focus on determining the sufficient amount and quality of data necessary for satisfying the execution of a business decision. Of course, this decision-making strategy is easier in theory than it is in practice.

 

Data driven decision management comprises three components:

  1. Data requirements - Different decisions will have different data requirements, which include the data volume, variety and velocity necessary for data-driven decision making. Not every decision requires the same amount and quality of data.
  2. Decision criteria - For example, a decision that must be made within 30 seconds has very different data requirements than a decision that should be made within 30 minutes, or a decision that could be made within 30 days.
  3. Decision evaluation - The quality of a decision is determined by the business results it produces, not the person who made the decision, the quality of the data used to support the decision, or even the decision-making technique.

The big data movement has brought with it a host of new technologies and analytical capabilities. However,  these technologies are ineffective without the right questions and talent to create a data-driven culture.

Imagine…What if you could . . .

. . . predict the buying behavior and decision criteria of your customers and prospects weeks before your competition

. . . gain market share by storing and analyzing the explosion of telematics data for improved risk assessment and distinct competitive advantage

. . . price more accurately based on risk attributes, key demographics, competitors rates, demand elasticity models and make adjustments in real-time

It is possible.

To learn how to turn this theory into reality, download the white paper “Return on Information: The new ROI.”

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

 

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