How Banks and Insurers Can Turn Quantities of Data Into Quality Insights

Adapting to the shifting needs of the customer has always been a driver for change in banking and insurance—and that's no different in a pandemic-impacted landscape. But in the past, meeting the needs of customers largely focused on providing digital tools, such as mobile apps and self-service kiosks. 

In response to COVID-19 disruption, transformation in financial services must now center on anticipating customer needs and developing solutions that mitigate the disruption—and for banks and insurance companies to do that, they’ll need to tap into data sources that have been historically underutilized.

Bracing and Remaining Agile on the Road to Recovery

Banks and insurance companies have needed to remain agile while bracing for looming threats. For example, banks faced the high possibility that millions of their customers could default on loans when the initial government lockdowns forced many businesses to close and consequently, many people lost their jobs. In response, many banks quickly put in place borrower relief programs, such as loan forgiveness and extended grace periods. But the uncertainty around the pandemic and ongoing unemployment continues to make rising loan delinquencies an ongoing high risk in the foreseeable future. 

For insurers, automotive insurance carriers took quick action by refunding as much as $14 billion of insurance premiums to policyholders for driving roughly 40% less during the first three months of the pandemic compared to the same period a year ago. But with many people continuing to work from home, the decrease in driving may be a permanent change, at least for the foreseeable future, and insurers will have to assess the impact of behavioral shifts on their premium revenue. What’s more, they’re bracing for how other pandemic-related factors—such as high unemployment rates, ongoing business interruptions, and uncertainty around medical costs—will affect their premium revenue. 

Clearly, ongoing uncertainty will continue to impact consumer behavior in unprecedented ways, and as a result, banks and insurers will need to quickly adjust their business in response to the sudden and unprecedented changes in consumer behavior—and then forecast the long-term implications of these behavioral shifts.

Many banks and insurance companies have the data to do this kind of modeling—but all too often, their technology lacks the capability to analyze the data, and as a result, they underutilize the data they have. 

Go From Data Quantity to Quality Insights

Big data has always been big in the financial services industry. Every transaction—bank deposits, credit cards, insurance policy sales, insurance claims, and more—generates data, which makes the financial services industry among the most data-intensive industries. Given the massive amounts of data tied to every business transaction, financial services companies may seem like they already have the tools to anticipate the needs of their customers. However, like many businesses, banks and insurance companies struggle to create forecasting models or gain meaningful business insights because they are unable to extract the data, whether that’s in a data warehouse, another database, or an operational system. By some estimates, as much as 73% of data at a company goes unused. 

The underutilization of data for analysis can be mistaken for data hoarding, meaning the mass storage of excessive data. Memory storage is inexpensive, which makes data warehouses appear cost effective for storing and organizing the plethora of information drawn from operational and core processing systems. In addition, financial services companies preserve large amounts of operational and historical data for several reasons, including tax law requirements, regulations, or other needs of the business. However, the nature of a data warehouse architecture—as storage—makes the technology a cost center, not an investment that furthers the power of data. Data warehouse architecture is inflexible for conducting advanced analysis of multiple, disparate data sources. 

So the problem with banks and insurers isn’t so much data hoarding, but rather, it’s the lack of an intelligent data foundation. The capabilities to easily ingest high volumes of operational data, add simple or complex calculations to enrich that data, and create associated accounting reports when required while still tied to the source transaction—all are necessary to gain data-driven insight into how disruptions are impacting customer behavior, and consequently, how that impacts the business. 

These capabilities are only possible by establishing—or transforming—the rules and actions of a data foundation to allow banks and insurers bring together operational data from other systems and create a picture with greater context. Those insights help banks and insurers develop products and offer services that benefit customers and also help the business navigate the pandemic and beyond.

Using Data to Navigate a Path Forward

Getting back to business in a pandemic-impacted landscape will be anything but usual. Banks and insurers can no longer just rely on past trends of one data set to forecast demand and impact. They’ll have to blend a plenitude of data types, including data across multiple systems—such as regulatory reporting data or customer transaction data—and analogous events, such as national unemployment rates, to create a complete picture of the path forward.

For example, Deloitte’s research methodology in their report, “The Path Ahead: Navigating Financial Services Sector Performance Post-COVID-19,” is an example of how blended data creates a big-picture forecast. According to the report, the research team at the Deloitte Center for Financial Services looked at “the statistical relationships among national unemployment rate, the homeowners’ unemployment rate, and the 30-day and 90-day delinquency rates for the past 17 years,” to forecast the impact of the COVID-19 pandemic on mortgage delinquencies from 2020 to 2024. 

Likewise, blending data sources across systems and sources is exactly what banks must do to understand and respond to the short- and long-term impact of pandemic-driven behavioral trends. For example, by blending financial data and operational data—such as customer demographic data and FICO scores—banks can identify borrowers more likely to face financial difficulties, and provide those borrowers with personalized loan modifications or other solutions.

Same goes for insurance carriers. Deloitte Insight report researchers gave this advice for workers’ comp insurers to mitigate risks and respond to market shifts: “Carriers should look to shore up their risk-selection standards and pricing models. Underwriting profitability could be paramount to remain viable, particularly in this low interest rate environment, when investment income is likely to be impacted as well.” One way for insurers to measure the effectiveness of their underwriting discipline—the process of evaluating risks, pricing, and coverage—is by analyzing a blend of policy, claims systems, and insurance premiums data, which are coming from a blend of financial and operational systems.

Amid all the uncertainty brought on by the pandemic, one thing is for certain: Staying on the pulse of changing consumer behavior will be a constant need. Banks and insurance companies have the data to understand the short- and long-term impact of disruptions, and with the right technology capabilities, they can blend operational and finance data for the insights to do more than move forward—they can blaze their own trail. 

Learn more from Workday customer Federal Home Loan Bank of Dallas (FHLB Dallas)  about how unlocking financial insights drives more confident decision-making.

To learn more, read part two in this series: "It’s All in the Details: How Data-Rich Subledgers Fuel Financial Agility."