Insurers have always been ahead of the curve in using data for the customer-facing side of the business. Increasingly complex personal or business data for actuarial models help assess and respond to risk.
However, when it comes to internal operations, many large-scale insurers fail to leverage data to quickly gain insights and drive them to action.
Here are four ways firms can better harness data analytics to improve their internal operations:
1. Focus analytics on fundamental operations to improve efficiency and meet customer demand.
In an industry where organic growth is difficult to come by, most insurers can benefit significantly from analytics projects focused on getting the basics right. Deeper insight around core functions such as underwriting, workflow management, policy creation, and claims management will drive substantial operational efficiency and a better understanding of customer demand. This data efficiency allows for a more nimble organization that responds to changes in a heavily regulated environment more quickly.
Below are a few examples of how the application of analytics in fundamental operations has been proven to drive high impact for insurers:
A leading insurance services firm employed the same level of effort and resources to process all transactions, regardless of complexity. We worked with the firm to link activity-based analytics to operational and financial data, and found that a significant percentage of the transactions were unprofitable. By diverting the handling of low complexity transactions to a separate, more streamlined process, the firm was able to better align revenue with cost to serve.
For an executive of a global firm, we combined financial and operational data sets to create a normalized view of cost efficiency (e.g., cost per policy), allowing the leader to accurately compare performance across the firm’s country IT teams for the first time. This revealed the inefficiencies of maintaining disparate 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 require high amounts of re-entering and re-formatting. We found an opportunity to automate the process through technology (a sentiment analysis engine) that extracts data from a variety of sources and auto-populates the forms and systems.
2. Build data management capabilities by collecting, mapping, and aligning data from disparate systems (everything from HR to claims systems) to run faster and better analytics.
Many insurers have grown through decades of acquisition, leading to quality issues that stem from legacy data and systems ‘stitched together’ in one large ecosystem. These patchwork systems often lead to more than one source of truth, which causes confusion and makes running fast analyses difficult.
A master data-management strategy, with adequate time spent upfront on data mapping, is essential to reduce the sources of inaccuracies. Often, this is a large project, but provides an essential foundation for future data analytics endeavors.
As technology advances, companies should also look for better ways to incorporate external sources of data. Refined geographic and topological data can provide higher resolution of risk in areas such as flooding and other natural disasters. Analytics from wearables and connected personal devices could provide additional ways to ensure efficient medical recovery, thus preventing fraud and driving higher profitability from personal policies.
Identifying the many ways advanced analytics can drive competitiveness for your firm is only the first challenge. (Photo: Shutterstock)
3. Institutionalize ‘management by data’ in your culture and execution framework.
Firms need to ensure data insights become actionable by institutionalizing the management of data. Often, a firm’s legacy culture of managing by instinct can be hard to overcome.
Insurers need to instill new quantitative skills in senior management and clearly define new roles, responsibilities, and performance measurements in a way that benefits from increased data usage. This requires a shift in culture, processes, procedures, rewards and recognition.
In the new culture, leaders should be those who believe in managing with data and make decisions based on insights derived from a new world of analytics to take the business forward versus acting on instincts from the past.
4. Use analytics to grow revenue.
We have experienced an increased focus on integrating customer data with operations to drive revenue growth, typically by identifying sources of attrition and cross- or up-selling. By analyzing customers’ behavior patterns, models can predict inorganic causes of customer churn as well as the propensity to purchase additional products such as additional or higher tier coverage.
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 connect with customers.
Financial services firms, particularly in the wealth management space, use attribution models to compare behavior of similar clients to identify those at risk of withdrawing investments or those likely to purchase additional investment products. In such examples, customer analytics has helped streamline operations and onboarding procedures within the firm, and has been integrated into a ‘feedback loop’ to proactively spot risks and opportunities.
Similarly, data science and the application of advanced analytic techniques can also disrupt traditional actuarial disciplines. Some insurers have used new models (or existing measurements) for pricing leverage due to better risk assessment, thus attracting new customers. For example, Progressive Insurance’s use of credit scores (now an ‘insurance score’) assesses insurability, resulting in an ability to offer prices lower than its competitors.
How to implement ideas quickly
Of course, identifying the many ways advanced analytics can drive competitiveness for your firm is only the first challenge. The second — and often harder — task is figuring out how to implement new ideas quickly, efficiently, and with immediate impact. Some leaders see investing in new data systems and infusing data into employees’ ways of thinking as tedious and expensive and often prioritize other projects.
However, we have recently seen efficient approaches and technologies to rapidly drive this change without an exorbitant expense. It’s up to leadership to ensure that organizations have the right alignment, execution infrastructure, skills, capabilities, and culture to leverage these advances for transformation. Technology and data analytics implemented across the enterprise — coupled with action — will characterize relevant companies in a fast-evolving space.