Tag Archives: data mining

Helping Data Scientists Through Storytelling

Good communication is always a two-way street. Insurers that employ data scientists or partner with data science consulting firms often look at those experts much like one-way suppliers. Data science supplies the analytics; the business consumes the analytics.

But as data science grows within the organization, most insurers find the relationship is less about one-sided data storytelling and more about the synergies that occur in data science and business conversations. We at Majesco don’t think it is overselling data science to say these conversations and relationships can have a monumental impact on the organization’s business direction. So, forward-thinking insurers will want to take some initiative in supporting both data scientists and business data users as they work to translate their efforts and needs for each other.

In my last two blog posts, we walked through why effective data science storytelling matters, and we looked at how data scientists can improve data science storytelling in ways that will have a meaningful impact.

In this last blog post of the series, we want to look more closely at the organization’s role in providing the personnel, tools and environment that will foster those conversations.

Hiring, supporting and partnering

Organizations should begin by attempting to hire and retain talented data scientists who are also strong communicators. They should be able to talk to their audience at different levels—very elementary levels for “newbies” and highly theoretical levels if their customers are other data scientists. Hiring a data scientist who only has a head for math or coding will not fulfill the business need for meaningful translation.

Even data scientists who are proven communicators could benefit from access to in-house designers and copywriters for presentation material. Depending on the size of the insurer, a small data communication support staff could be built to include a member of in-house marketing, a developer who understands reports and dashboards and the data scientist(s). Just creating this production support team, however, may not be enough. The team members must work together to gain their own understanding. Designers, for example, will need to work closely with the analyst to get the story right for presentation materials. This kind of scenario works well if an organization is mass-producing models of a similar type. Smooth development and effective data translation will happen with experience. The goal is to keep data scientists doing what they do best—using less time on tasks that are outside of their domain—and giving data’s story its best possibility to make an impact.

Many insurers aren’t yet large enough to employ or attract data scientists. A data science partner provides more than just added support. It supplies experience in marketing and risk modeling, experience in the details of analytic communications and a broad understanding of how many areas of the organization can be improved.

Investing in data visualization tools

Organizations will need to support their data scientists, not only with advanced statistical tools but with visualization tools. There are already many data mining tools on the market, but many of these are designed with outputs that serve a theoretical perspective, not necessarily a business perspective. For these, you’ll want to employ tools such as Tableau, Qlikview and YellowFin, which are all excellent data visualization tools that are key to business intelligence but are not central to advanced analytics. These tools are especially effective at showing how models can be used to improve the business using overlaid KPIs and statistical metrics. They can slice and dice the analytical populations of interest almost instantaneously.

When it comes to data science storytelling, one tool normally will not tell the whole story. Story telling will require a variety of tools, depending on the various ideas the data scientist is trying to convey. To implement the data and model algorithms into a system the insurer already uses, a number of additional tools may be required. (These normally aren’t major investments.)

In the near future, I think data mining/advanced analytics tools will morph into something able to contain more superior data visualization tools than are currently available. Insurers shouldn’t wait, however, to test and use the tools that are available today. Experience today will improve tomorrow’s business outcomes.

Constructing the best environment

Telling data’s story effectively may work best if the organization can foster a team management approach to data science. This kind of strategic team (different than the production team) would manage the traffic of coming and current data projects. It could include a data liaison from each department, a project manager assigned by IT to handle project flow and a business executive whose role is to make sure priority focus remains on areas of high business impact. Some of these ideas, and others, are dealt with in John Johansen’s recent blog series, Where’s the Real Home for Analytics?

To quickly reap the rewards of the data team’s knowledge, a feedback vehicle should be in place. A communication loop will allow the business to comment on what is helpful in communication; what is not helpful; which areas are ripe for current focus; and which products, services and processes could use (or provide) data streams in the future. With the digital realm in a consistent state of fresh ideas and upheaval, an energetic data science team will have the opportunity to grow together, get more creative and brainstorm more effectively on how to connect analytics to business strategies.

Equally important in these relationships is building adequate levels of trust. When the business not only understands the stories data scientists have translated for them but also trusts the sources and the scientists themselves, a vital shift has occurred. The value loop is complete, and the organization should become highly competitive.

Above all, in discussing the needs and hurdles, do not lose the excitement of what is transpiring. An insurer’s thirst for data science and data’s increased availability is a positive thing. It means complex decisions are being made with greater clarity and better opportunities for success. As business users see results that are tied to the stories supplied by data science, its value will continue to grow. It will become a fixed pillar of organizational support.

This article was written by Jane Turnbull, vice president – analytics for Majesco.

9-Step Model for Data Analysis

When training analysts how to deliver more value, two topics have proved the most popular.

One is training in Socratic questioning techniques, to get to the real business need.

But, as many analysts have “fallen into” this line of work, rather than making a conscious education and career choice, few have been trained in methodologies. With the exponential growth of insight analysts, marketing analysts and data scientists, the emphasis appears to be on just coding skills and software mastery. Where this is the case, too often analysis is an unplanned art, with unreliable timescales and too many “rabbit warrens” being explored. It is perhaps for this reason that the other most popular topic is a high-level structure for analysis.

I call this approach the 9-step model for analysis. It comprises the following steps:

1. Socratic Questioning: getting to real business need

2. Planning & Design: defining approach and gathering resources

3. Stakeholder Buy-In: getting agreement on what will be delivered

4. Data: ensuring the needed quality data and learning from it

5. Analysis: including exploratory data analysis and hypothesis testing

6. Insight Generation: converging evidence to get to deeper insights

7. Stakeholder Sign-Off: support for or refining recommendations

8. Storytelling & Visualization: capturing hearts and minds for action

9. Influencing for Action: ensuring appropriate action is taken

What’s your experience of improving the capability of your customer insight team? Have you focused on developing the skills outlined above or other areas? Please do share your tips, too.

6 Opportunities for Carriers in ‘Big Data’

As insurers increasingly collect “big data” — think petabytes and exabytes — it’s now possible to use new data tools and technologies to mine data across three dimensions:

  • Large size/long duration — Traditional data mining usually was limited to three to five years of data. Now you can mine data accumulated over decades.
  • Real-time — With the advent of social media and the different sources, data pours in at ever-increasing speeds.
  • Variety of types — There’s more variety of data, both structured and unstructured, that are drastically different from each other.

The ability to master the complexities of capturing, processing and organizing big data has led to several data-centric opportunities for carriers.

Personalized marketing

Big data is playing an increasing role in sales and marketing, and personalization is the hot industry trend. Gathering more information about customers helps insurance companies provide more-personalized products and services. Innovative companies are coming up with new ways to gather more information about customers to personalize their buying experience.

One example is Progressive’s Snapshot device, which tracks how often insureds slam on the brakes and how many miles they drive. It lets insurers provide personalized products based on customers’ driving habits. A device like Snapshot captures information from the car every second, collecting data like how often drivers brake, how quickly they accelerate, driving time, average speed, etc. According to facethefactsusa.org, U.S. drivers log an average of 13,476 miles per year, or 37 miles a day. Big data systems have to process this constant stream of data, coming in every second for however long the user takes to travel 37 miles. Even if only 10% to 15% of customers use the device, it is still a huge amount of data to process. The systems have to process all this information and use predictive models to analyze risks and offer a personalized rate to the user.

People are increasingly using social media to voice their interests, opinions and frustrations, so analyzing social feeds can also help insurance companies better target new customers and respond to existing customers. Using big data, insurers can pinpoint trends, especially of complaints or dissatisfaction with current products and services. Getting ahead of the curve is crucial because bad reviews can spread like wildfire on the web.

Risk management 

The wealth of data now available to insurance companies — from both old and new data sources — offers ways to better predict risks and trends. Big data can be used to analyze decades of information and identify trends and newer dimensions like demographic change and behavioral evolution.

Process improvement and organizational efficiency

Another popular use is for constant improvement of organizational productivity by recording usage patterns of an organization’s internal tools and software. Better understanding of usage trends leads to:

  • Creation of more useful software that better fits the organization’s needs.
  • Avoidance of tools that do not have a good return on investment.
  • Identification of manual tasks that can be automated. For example, logs and usage patterns from tools at the agent’s office are important sources of information for understanding customer preferences and agency efficiency.

Automation of manual processes results in significant savings. But in huge, complex organizations, there are almost always overlapping or multiple instances of similar systems and processes that result in redundancy and increased cost of maintenance. Similarly, inadequate and inefficient systems require manual intervention, resulting in bottlenecks, inflated completion times and, most importantly, increased cost.

Using data from internal systems, systems can study critical usage information of various tools and analyze productivity, throughput and turnaround times across a variety of parameters. This can help managers understand inadequacies of existing systems and identify redundancy.

The same data sources are also used to predict higher and leaner load times, so the infrastructure teams can plan for providing appropriate computing resources during critical events. These measures add up quickly, resulting in significant cost savings and improved office efficiency.

Automated learning

While big data technologies now help perform regular data-mining on a much bigger scale, that’s only the beginning. Technology companies are venturing into the fuzzy world of decision-making via artificial intelligence, and a branch of AI called machine learning has greatly advanced.

Machine learning deals with making computer systems learn constantly from data to progressively make more intelligent decisions. Once a machine-learning system has been trained to use specific pattern-analyzing models, it starts to learn from the data and works to identify trends and patterns that have led to specific decisions in the past. Naturally, when more data — along all of the big data axes — is provided, the system has a much better chance to learn more, make smarter decisions and avoid the need for manual intervention.

The insurance and financial industries pioneered the commercial application of machine learning techniques by creating computational models for risk analysis and premium calculation.  They can predict risks and understand the creditworthiness of a customer by analyzing their past data.

While traditional systems dealt with tens of thousands of data records and took days to crunch through a handful of parameters to analyze risks using, for example, a modified Gaussian copula, the same is now possible in a matter of hours, with two major improvements. First, all available data can be analyzed, and second, risk parameters are unlimited.

Predictive analytics

Machine language technology can use traditional and new data streams to analyze trends and help build models that predict patterns and events with increased accuracy and convert these predictions into opportunities.

Traditional systems generally helped identify reasons for consistent patterns. For example, when analysis of decades of data exposes a consistent trend like an increase in accident reporting during specific periods of the year, results indicated climatic or social causes such as holidays.

With big data and machine learning, predictive analytics now helps create predictions for claims reporting volumes and trends, medical diagnosis for the health insurance industry, new business opportunities and much more.

Fraud Detection

The insurance industry has always been working to devise new ways to detect fraud. With big data technology, it is now possible to look for fraud detection patterns across multiple aspects of the business, including claims, payments and provider-shopping and detect them fairly quickly.

Machine learning systems can now identify new models and patterns of fraud that previously required manual detection. Fraud detection algorithms have improved tremendously with the power of machine learning. Consequently, near-real-time detection and alerting is now possible with big data. This trend promises to only keep getting better.

These six opportunities are just the tip of the iceberg. The entire insurance industry can achieve precise and targeted marketing of products based on history, preferences and social data from customers and competitors. No piece of data, regardless of form, source or size, is insignificant. With big data technology and machine learning tools and algorithms, combined with the limitless power of the cloud computing platform, possibilities are endless.

Analytics at the Next Level: Transformation Is in Sight

Although insurance companies are embracing analytics in many forms to a much higher degree than other businesses, adoption by the insurance industry is still only in its adolescent stage. Deployment is broad but inconsistent. The use of analytics may be about to mature considerably, though, based on a recent series of mergers and acquisitions.

Currently, while a majority of large carriers use predictive modeling in one of more lines of business, and mostly in personal lines auto, a smaller percentage use it in their commercial auto and property units. Insurers recognize predictive analytics as a critical tool for improving top-line growth and profitability while managing risk and improving operational efficiency. Insurers believe predictive analytics can create competitive advantage and increase market share.

Fueling even greater excitement – and soon to be driving transformational innovation – is the recent surge of M&A activity by both new and nontraditional players, which have combined risk management and sophisticated analytics expertise with robust and diverse industry database services. The list of recent deals includes:

  • CoreLogic’s 2014 purchase of catastrophe modeling firm Eqecat, following its 2013 acquisition of property data provider Marshall & Swift/Boeckh; a significant minority interest in Symbility, provider of cloud-based and smartphone/tablet-enabled property claims technology for the property and casualty insurance industry; and the credit and flood services units of DataQuick.
  • Statutory and public data provider SNL Insurance’s 2014 purchase of business intelligence and analytics firm iPartners, which serves P&C and life companies.
  • Verisk Analytics’ 2014 acquisition of EagleView Technology, a digital aerial property imaging and measurement solution.
  • LexisNexis Risk Solutions’ 2013 acquisition of Mapflow, a geographic risk assessment technology company with solutions that complement the data, advanced analytics, supercomputing platform and linking capabilities offered by LexisNexis.

Other 2013/2014 transactions that have broad implications for the insurance analytics and information technology ecosystem include:

  • Guidewire Software, a provider of core management system software and related products for property and casualty insurers, acquired Millbrook, a provider of data management and business intelligence and analytic solutions for P&C insurers.
  • IHS, a global leader in critical information and analytics, acquired automotive information database provider R.L. Polk, which owns the vehicle history report provider Carfax. 
  • FICO, a leading provider of analytics and decision management technology, acquired Infoglide Software, a provider of entity resolution and social network analysis solutions used primarily to improve fraud detection, security and compliance.
  • CCC Information Services, a database, software, analytics and solutions provider to the auto insurance claims and collision repair markets, acquired Auto Injury Solutions, a provider of auto injury medical review solutions. This transaction follows CCC’s acquisition of Injury Sciences, which provides insurance carriers with scientifically based analytic tools to help identify fraudulent and exaggerated injury claims associated with automobile accidents.
  • Mitchell International, a provider of technology, connectivity and information solutions to the P&C claims and collision repair industries, plans to acquire Fairpay Solutions, which provides workers’ compensation, liability and auto-cost-containment and payment-integrity services. Fairpay will expand Mitchell’s solution suite of bill review and out-of-network negotiation services and complements its acquisition of National Health Quest in 2012.

Based on these acquisitions and the other trends driving the use of analytics, it will be increasingly possible to:

  • Integrate cloud services, M2M, data mining and analytics to create the ultimate insurance enterprise platform.
  • Identify profitable customers, measure satisfaction and loyalty and drive cross/up-sell programs.
  • Capitalize on emerging technologies to improve pool optimization, create dynamic pricing models and reduce loss and claims payout.
  • Encourage “management by analytics” to overcome departmental or product-specific views of customers, update legacy systems and reduce operating spending over the enterprise.
  • Explore external data sources to better understand customer risk, pricing, attrition and opportunities for exploring emerging markets.                       

As the industry is beginning to understand, the breadth of proven analytics applications and the seemingly unlimited potential to identify even more, coupled with related M&A market activity that will drive transformational innovation, indicates that the growing interest in analytics will be well-rewarded. Those that are paying the most attention will become market leaders.

Stephen will be Chairing Analytics for Insurance USA, Chicago, March 19-20, 2014.

US Insurers Must Contend With Federal Overseers

Since 1851, when the first state insurance regulator was established, the US insurance industry has had to comply only with the laws of a regulatory system that is state-based. However, that changed when the Dodd-Frank Wall Street Reform and Consumer Protection Act (the Dodd-Frank Act) passed into law on July 21, 2010. The Dodd-Frank Act, which is the US Federal Government’s response to the 2007-2008 financial crisis, created several entities including the Federal Insurance Office (FIO) and the Financial Stability Oversight Council (FSOC). Both of these entities are authorized to be involved in the insurance regulatory system, albeit with different degrees of authority and oversight.

Ovum’s recently published report 2013 US Insurance Regulatory Landscape discusses the strengthening presence of the federal government in US insurance regulation, four interdependent initiatives that US insurers need to implement to comply with regulations, and the expanding role that technology can play in supporting US insurers as they prepare for regulatory compliance.

Federal Presence In The US Insurance Regulatory System Has Strengthened
State-based insurance regulators can be forgiven for believing that the regulatory system they have in place, and are continually reshaping to align with market realities, has continued to prove worthy to both consumers and insurance companies. Be that as it may, the Dodd-Frank Act is now law and the FSOC and the FIO are now active participants in the US insurance regulatory system. Both entities have authority and responsibilities that could transform the US insurance system. Only time will tell whether their existence is a net positive for insurance companies domiciled in the US and international insurers conducting business in the US.

Insurers should familiarize themselves with the roles and responsibilities of the FSOC and FIO. The FSOC will identify and respond to threats to the financial stability of the US and promote market discipline. The FIO has a number of responsibilities, including: recommending to the FSOC when an insurer (and its affiliates) should be designated a “systemically important financial institution” (SIFI), thus making it subject to additional capital requirements set by the Federal Reserve; representing the US in matters relating to international insurance regulation; monitoring the extent to which traditionally underserved communities, consumers, minorities, and those of low-to-moderate income can access affordable insurance products; and assisting the Secretary of the Treasury and other officials in administering the Terrorism Risk Insurance Program.

Insurers Must Implement Four Interdependent Initiatives To Enable Readiness To Comply With State And, Potentially, Federal Regulations
Insurers should create and continue to strengthen four interdependent initiatives to ensure their readiness to comply with regulation, which encompass monitoring, management, analysis, and reporting.

  • Monitoring initiatives include monitoring and capturing: any legislative bills available for public comment; discussions from the insurance legislators in each state, the NAIC, the FIO, the FSOC, the various influencer groups, and online trade press articles and commentary concerning legislative issues impacting the insurance industry; and existing regulations and proposed and actual changes to these regulations for each state in which the company conducts and wants to conduct business.
  • Management initiatives include storing, cleaning, tagging, and otherwise preparing the primarily unstructured content captured above, for analysis and preliminary preparation of regulatory compliance initiatives.
  • Analysis initiatives include analyzing the captured content’s potential impact on existing company regulatory compliance initiatives or the resources needed to create new initiatives. The analysis is likely to encompass financial analysis and modeling if the regulatory discussion impacts the amount of capital reserves the insurance company will need, or alters the investments it can make or the mix of risks it can insure. It also includes the creation of interactive dashboards that enable insurance executives and legal, compliance, and other insurance departments to track compliance with state and, where necessary, federal regulations.
  • Reporting initiatives include creating reports for internal insurance company use, for each state insurance commissioner’s office for the states in which the company conducts business, and, where necessary, for the FIO and the FSOC.

Technology Has A Growing Role To Play In Enabling Insurers To Comply With Regulations
To remain knowledgeable about what is happening, be prepared for any changes to requirements, and comply with existing regulations, insurers should use:

  • Text data mining/semantic technology to create a tagged and searchable repository of existing and pending regulations.
  • Master data management (MDM) applications to establish, maintain, and update a repository of existing and proposed industry regulations.
  • Analytics, including predictive analytics, to measure the company’s capital adequacy and ensure it complies with state and, where necessary, FIO and FSOC requirements, and to model and project the company’s current and projected density of risk (i.e. total exposure across all insurance lines of business that the insurer is selling for all or specific geographies).
  • Data visualization to create dashboards to track the company’s alignment with regulatory deadlines and capital requirements, and its progress toward adopting insurance regulatory initiatives (e.g. uniform producer licensing).
  • Database technologies to create, store, and manage producer demographic, insurance experience, training, and licensing information for every insurance company producer (i.e. agent/broker/financial advisor) for each insurance line of business, for every state (or jurisdiction) in which the agent is legally authorized to sell insurance.
  • Collaboration and communications technologies within the insurance company, including the agent/broker/financial advisor intermediaries, to discuss progress toward regulatory compliance including concerns or problems and potential solutions if the company believes it is non-compliant on certain issues.
  • Reporting capabilities to create compliance reports and send them to internal insurance departments, to each state insurance commissioner’s office for each state in which the company conducts business, and, where necessary, to the FIO and the FSOC.