Addressing the Invisible Data Quality Issue in Program Business

Addressing the Invisible Data Quality Issue in Program Business

Addressing the Invisible Data Quality Issue in Program Business

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

Do you have a data quality issue?

If your company is spending thousands of hours and allocating a substantial amount of resources and capital to process bordereaux data, then the answer is probably yes.At the start of our client engagements, we've noticed that many companies are aware that their current processes are inefficient.

What they don't realize is how their program business's potential growth and profitability become constrained because of how they orient towards data. Most companies are treating data as a cost center and not the essential asset that can unlock tremendous value.

Program business has been one of the fastest-growing sectors in the insurance industry.

According to a 2019 State of Program Business Study from TMPAA, premiums rose 12.2% in 2018, reaching $40.5 billion, outpacing the property/casualty industry. This growth is bound to plateau due to technology and capacity limitations all sectors of the insurance industry currently face. But demands for new acquisitions will only continue to increase as companies look to double the size of their program business. To achieve this growth, companies need first to understand the data quality issues they're facing before changing the way they operate and think about data.

One of the significant challenges for this market continues to be the onboarding and management of new and existing programs.

The complexities alone, of managing existing programs, is overwhelming to companies. Numerous reallocated resources spend significant energy manually preparing and processing data sourced from various partner relationships. There is this "scramble" at the end of every month to close the "month-end process." These processes, many critical and important, include feeding essential financial and reinsurance systems, providing month-end information to capital and capacity providers, or monitoring profit and volatility. So why is there a scramble?

The problem begins very innocently.  Companies write a few programs, and they create simple processes, data flows, and reports to manage the business and partner relationships. Over time more programs are written, and business requirements become more complex as they expand into new classes, lines, and coverages.

Eventually, what they created is no longer fit for purpose.  The addition of the new business requirements and processes increases the complexity and required resources.  What was once a few people dedicating time to the process is now a team of people across finance, technology, and other areas of the business allocating a substantial portion of their time to the "month-end process."

Data ends up becoming a huge operational expense that reduces the overall profitability and efficiency of the business. These inefficient processes introduce data quality challenges that manifest as unnecessary risks and uncertainty.  To address these problems, companies typically explore traditional technologies and approaches and declare a victory if they achieve a two-times (2x) lift in operational efficiency.  Unfortunately, that will not be enough lift to enable companies to grow, manage existing programs, and prepare for the future.

Companies should be exploring what it would take to achieve an order of magnitude improvement (a 10x lift) to the business.  Traditional approaches will not enable companies to operate ten times more efficiently.  But imagine what a company could do if things that took seven days took 0.7 days?

Whether they are a carrier, program administrator, capital provider, or agency, each desperately requires access to a pool of consistent, high-quality data.  Quality data is needed to effectively communicate with stakeholders, measure and monitor the business, accurately feed downstream processes, and expand into new, profitable niches.  But high customer acquisition costs and limitations with traditional technologies make this difficult.  Maximizing data quality and consistency starts with the immediate collection and processing of data as soon as any new business enters their organization.

In the next part of this series, we will be sharing examples of how Quantemplate has helped customers with their program business needs across specialty, fronting, and fleet auto. We'll show how embracing Machine Learning and automation has substantially increased data quality and processing efficiency for these customers, helping them uncap their true growth potential and the added benefits they're seeing.

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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