Insurers have always been ahead of the curve in using data forthe customer-facing side of the business. Increasingly complexpersonal or business data for actuarial models help assess andrespond to risk.

However, when it comes to internal operations, many large-scaleinsurers fail to leverage data to quickly gain insights and drivethem to action.

Here are four ways firms can better harness data analytics toimprove their internal operations:

1. Focus analytics on fundamentaloperations to improve efficiency and meet customerdemand.

In an industry where organic growth is difficult to come by,most insurers can benefit significantly from analytics projectsfocused on getting the basics right. Deeper insight around corefunctions such as underwriting, workflow management, policycreation, and claims management will drive substantial operationalefficiency and a better understanding of customer demand. This dataefficiency allows for a more nimble organization that responds tochanges in a heavily regulated environment morequickly. 

Below are a few examples of how the application of analytics infundamental operations has been proven to drive high impact forinsurers:

A leading insurance services firm employed the same level ofeffort and resources to process all transactions, regardless ofcomplexity. We worked with the firm to link activity-basedanalytics to operational and financial data, and found that asignificant percentage of the transactions were unprofitable. Bydiverting the handling of low complexity transactions to aseparate, more streamlined process, the firm was able to betteralign revenue with cost to serve.

For an executive of a global firm, we combined financial andoperational data sets to create a normalized view of costefficiency (e.g., cost per policy), allowing the leader toaccurately compare performance across the firm's country IT teamsfor the first time. This revealed the inefficiencies of maintainingdisparate models and processes.

Many insurance processes deal with non-standard inputs (e.g.,requests for quotes from clients via free-form emails) and outputs(e.g., quotes sent from carriers in multiple formats) that requirehigh amounts of re-entering and re-formatting. We found anopportunity to automate the process through technology (a sentimentanalysis engine) that extracts data from a variety of sources andauto-populates the forms and systems.

2. Build data management capabilitiesby collecting, mapping, and aligning data from disparate systems(everything from HR to claims systems) to run faster and betteranalytics.

Many insurers have grown through decades of acquisition, leadingto quality issues that stem from legacy data and systems 'stitchedtogether' in one large ecosystem. These patchwork systems oftenlead to more than one source of truth, which causes confusion andmakes running fast analyses difficult.

Related: How predictive analytics can improve claimsoutcomes

A master data-management strategy, with adequate time spentupfront on data mapping, is essential to reduce the sources ofinaccuracies. Often, this is a large project, but provides anessential foundation for future data analytics endeavors.

As technology advances, companies should also look for betterways to incorporate external sources of data. Refined geographicand topological data can provide higher resolution of risk in areassuch as flooding and other natural disasters. Analytics fromwearables and connected personal devices could provide additionalways to ensure efficient medical recovery, thus preventing fraudand driving higher profitability from personal policies.

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Identifying the many ways advanced analytics can drivecompetitiveness for your firm is only the firstchallenge. (Photo: Shutterstock)

3. Institutionalize'management by data' in your culture and executionframework.

Firms need to ensure data insights become actionable byinstitutionalizing the management of data. Often, a firm's legacyculture of managing by instinct can be hard to overcome.

Insurers need to instill new quantitative skills in seniormanagement and clearly define new roles, responsibilities, andperformance measurements in a way that benefits from increased datausage. This requires a shift in culture, processes, procedures,rewards and recognition.

In the new culture, leaders should be those who believe inmanaging with data and make decisions based on insights derivedfrom a new world of analytics to take the business forward versusacting on instincts from the past.

4. Use analytics to growrevenue.

We have experienced an increased focus on integrating customerdata with operations to drive revenue growth, typically byidentifying sources of attrition and cross- or up-selling. Byanalyzing customers' behavior patterns, models can predictinorganic causes of customer churn as well as the propensity topurchase additional products such as additional or higher tiercoverage.

Organizations with a finger on the pulse of their customers,through data, monitor for critical life events such as marriage,birth, and death and use those moments as opportunities to connectwith customers.

Related: Insurers face several challenges when it comes toinnovation

Financial services firms, particularly in the wealth managementspace, use attribution models to compare behavior of similarclients to identify those at risk of withdrawing investments orthose likely to purchase additional investment products. In suchexamples, customer analytics has helped streamline operations andonboarding procedures within the firm, and has been integrated intoa 'feedback loop' to proactively spot risks and opportunities.

Similarly, data science and the application of advanced analytictechniques can also disrupt traditional actuarial disciplines. Someinsurers have used new models (or existing measurements) forpricing leverage due to better risk assessment, thus attracting newcustomers. For example, Progressive Insurance's use of creditscores (now an 'insurance score') assesses insurability, resultingin an ability to offer prices lower than its competitors.

How to implement ideas quickly

Of course, identifying the many ways advanced analytics candrive competitiveness for your firm is only the first challenge.The second — and often harder — task is figuringout how to implement new ideas quickly, efficiently, and withimmediate impact. Some leaders see investing in new data systemsand infusing data into employees' ways of thinking as tedious andexpensive and often prioritize other projects.

However, we have recently seen efficient approaches andtechnologies to rapidly drive this change without an exorbitantexpense. It's up to leadership to ensure that organizations havethe right alignment, execution infrastructure, skills,capabilities, and culture to leverage these advances fortransformation. Technology and data analytics implemented acrossthe enterprise — coupled with action — willcharacterize relevant companies in a fast-evolving space.

Related: Successful young brokers use data to help theircompanies change

John Rodgers is senior managing director and chiefoperating officer at SSA &Company. Contact himon LinkedIn. Sachin Sachdeva is vice presidentat SSA & Company. Contact him on LinkedIn.