Addressing the Invisible Data Quality Issue in Program Business – Part 3 (Fleet Auto)

Addressing the Invisible Data Quality Issue in Program Business – Part 3 (Fleet Auto)

Addressing the Invisible Data Quality Issue in Program Business

Part 3 (Fleet Auto)

By Scott Quiana, Head of Products Marketing and Partnerships, Quantemplate

In part three of this series, we will explore a fleet auto carrier investigating ways to increase operational efficiency and improve the value-added services provided to its customers.

Data challenges exist across the programs market, and fleet auto presents its own unique challenges. Fleet auto has been expanding into new countries and markets, and as the number of countries increases, so do the complexities carriers, and risk managers must consider. Think about it, if coverage is offered in 13 countries, and data is received in 13 different languages, how do carriers and risk managers absorb any changes or identify inconsistencies in the data from month to month? How do they identify new usage patterns, grow into new countries, and consume telematics data as it becomes more pervasive and available in the market?

How do carriers and risk managers easily identify new usage patterns, grow into new countries, and consume telematics data?

To remain competitive in this market, carriers have to provide value-added services that differentiate themselves from their competitors. This includes frequent updates to risk managers to help manage volatility as the business and usage changes. As well as insights to help effectively underwrite and manage the changing business. It requires carriers to be flexible and adapt to the needs and realities of the market. On paper, this sounds easy, but companies have significant hurdles to overcome to deal with these dynamics.  The current fit-for-purpose systems and processes were not built with the future in mind. They were not developed to handle the changing market or the increasing data requirements needed to meet business demands.  

To remain competitive in this market, carriers have to provide value-added services that differentiate themselves from their competitors.

Our experience shows that most companies try to solve these challenges through traditional approaches, including adding resources to solve the problem. Whether onshore or offshore, they resources are added to support the changes needed to prepare data and provide insights, but these approaches are not scalable. While a 2 times lift in operational efficiency is achievable, they quickly realize that the flexibility needed to meet the required growth and value-added services is not possible with these approaches.

In 2019, we were approached by a leading P&C carrier that writes fleet auto.  They wanted to grow the business aggressively, but to execute, they had to implement a solution that they believed was necessary to compete in the market, including:

  • measuring the profitability and volatility of the business,
  • providing risk managers with claims insights to manage retention,
  • distribute consistent information to the customer, broker & underwriters.

Their fleet auto business covered large and mid-size multinational companies in over 200 countries and included over 700,000 vehicles. In addition to the legacy risk management system that held over 2 decades of claims data, they received new data from 30+ fronting partners in 40+ formats and 25+ languages each month.  The current legacy system and the existing manual processes needed to prepare data was not sufficient.  They were not built to provide the information at the speed, frequency, and accuracy necessary to manage and grow the business or the insights and value-added services pertinent to their customers.

Through the engagement, the carrier was able to capitalize on Quantemplates insurance-focused platform and implement a solution in less than two months.  They utilized the machine learning capabilities to streamline and automate the data preparation processes and harmonize the legacy system data with the monthly feeds across each country.  Resulting in data that was clean, validated, and available to both internal and external stakeholders.  The flexibility of the platform prepared the carrier for the future, positioned them for the expected growth into new countries, consume new data sources, and provide the information to support customer engagements.

Through the engagement, the carrier was able to capitalize on Quantemplates insurance-focused platform and implement a solution in less than two months.

The data quality issues programs faces stems from inefficiencies, something that can be managed and significantly improved with new technologies ad approaches. Treating data as an important asset, the moment it enters the organization, increases the operational lift and positions oneself to grow and enhance the experience with customers. The program market needs to take a hard look at current processes that are proving to be costly and inefficient and explore alternatives that can help change the trajectory of the business.

If you missed Part 1 or Part 2 of this series, view them here.

To learn more, please contact us here: https://www.quantemplate.com/contact or follow us on LinkedIn https://www.linkedin.com/company/quantemplate.com.

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