Addressing the Invisible Data Quality Issue in Program Business – Part 2 (Specialty Admitted Programs)

Addressing the Invisible Data Quality Issue in Program Business – Part 2 (Specialty Admitted Programs)

Addressing the Invisible Data Quality Issue in Program Business

Part 2

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

In part one of this series, we discussed how data quality and inefficient processes directly impact programs business.  

In part two, we delve into the benefits of exploring alternative solutions using a customer engagement as a backdrop.

The programs market is confronted with a multi-dimensional problem when it comes to data... Not only is the distribution of data received from various partners and channels, but the resolution and format of data vary dramatically, making it challenging to run and operationalize efficiently. Complicating matters, different parts of the organization may receive the same data, but at different resolutions and formats, further increasing the operational challenges. As an example, actuarial and finance both require claims data for their monthly activities. However, actuarial receives statistical data to process claims for trending and finance year-to-date data for payment and reinsurance. Two separate groups, starting different processes, making different assumptions on basically the same data. 

The programs market is confronted with a multi-dimensional problem when it comes to data...

Data quality challenges begin at this juncture, and they compound themselves with the introduction of new programs and additional sources of data. The compounding effect becomes crippling from a resourcing and technology perspective, making it challenging to consider onboarding new programs and even more difficult to manage existing programs. Are there alternative approaches? If resources focus on the statistical data, could both finance and actuarial utilize the same data for different uses? What is getting in the way?

Alternatives approaches will enable companies to operationalize these complex challenges. New technologies and approaches will be the key to resolving the data quality issues and operational inefficiencies present in the program market today. They will enable companies to approach these challenges differently and adapt to the market changes and business challenges. Below we explore the benefits a specialty programs writer was able to achieve in a relatively short period of time with these new approaches.

Working With A Specialty Programs Writer

In mid-2019, we partnered with a carrier writing programs and fronting business. The carrier desperately needed to accelerate the month-end process for its dozens of programs, and enhance the customer experience.

For the month-end data process, the carrier had a cross-functional team of highly skilled employees allocated to data preparation and processing. The complexities required people that understood the business as well as the complicated financial and reinsurance systems they needed to update each month.  The data preparation process took several weeks each month before the next month-end process would start again. Because the process was so manual and lacked the appropriate controls and validation rules, they hired an outsourced service to verify the monthly results, adding additional operational burden to the organization.

Alternatives approaches will enable companies to operationalize these complex challenges.

The carrier was seeking alternative approaches and technologies to help solve these problems, relieve the operational challenges, free up critical resources, remove the reliance on outsourced services, and enable the company to grow.  

With Quantemplate, an insurance-focused, machine-learning cloud-based technology, they were able to achieve:

  • A five-times lift in operational efficiency in a relatively short period of time
  • Reduced the monthly data preparation process to days enabling the company to close the month-end process quickly
  • Hundreds of hours of critical resources time reallocated to more value-added activities
  • Eliminated the need for outsourcing  

Automation and machine learning helped streamline the processes, create the operational rigor and validations they were looking to achieve and increased the number of views and accessibility of data to the organization to provide the necessary insights.

In the last part of this series, we will explore how Quantemplate has helped fleet auto customers address their data quality concerns.

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

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