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Make Rate Filing Easier by Streamlining Data Processes

Updating state rate filings can reduce loss ratios and improve market share. However, many insurers face bottlenecks in their technological processes.
  • Tim King
  • August 2021
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According to the old insurance industry adage, any risk can be profitable if it's appropriately priced. Price too low and fall victim to adverse selection. Price too high and lose revenues to competitors.

With the industry beleaguered by slow data processes, tedious rate filing reviews and a lack of agreement among team members on which systems to use, insurers are inhibited from using agile ways to competitively price products.

Given the inherent internal business buy-in and external regulatory hurdles to setting rates by updating state rate filings, speed to market is key—and the industry's data speaks for itself. Take Allstate: After declaring a renewed focus on rate filings, the company showed sustained year-over-year improvement in its loss ratio relative to the industry while holding market share nearly constant. And so the verdict is in—rate filings matter and insurers must take a hard look inward to understand the bottlenecks in place that stop them from achieving that goal.

If excellence in the rate filings process is so important, then why do carriers struggle with it so much? The answer comes down to three major realities, the first of which is analytical complexity. The seemingly straightforward task of creating model-ready data takes up time needed for modeling decisions and reduces the ability to verify and discuss different outcomes. Also, as the prevalence of machine learning techniques like gradient boosting expands, the traditional generalized linear model (GLM) approach to pricing is increasingly commoditized. Whether a carrier chooses to augment its GLMs with automated algorithms or embrace a broader ensemble of machine learning techniques, the net impact is increasing computational demands.

The second hurdle is competitive intelligence. This entails the laborious activity of sifting through mountains of competitors' rate filings. Despite common industry solutions, insurers still manually comb through thousands of pages looking for the few nuggets most relevant to pricing a given line of business and coverage. Analytically driven operational improvements can speed up this task.

Acknowledging the need for multi-genre analytics, a robust text analytics capability is required to digest and leverage competitor rate filings.


The third hurdle is to develop the requisite trust with internal decision-makers and state regulators. Trust is required to overcome inherent skepticism of analytics, machine learning and artificial intelligence, and it comprises three pillars:

  • Explainability: Make the AI-generated solutions clearly understandable to all involved.
  • Fairness: Prove to state, provincial, or national level jurisdictions that the new pricing is not discriminatory.
  • Lineage: Users of any analytical, model or AI output must have a clear line of sight into the data used to generate the desired outputs and action-supporting insights.

To realize the profitability and market share benefits of more agile pricing strategies, insurers must have the appropriate pricing platform in place to align with the aforementioned key challenges. Acknowledging the need for multi-genre analytics, a robust text analytics capability is required to digest and leverage competitor rate filings. The property/casualty segment of the insurance industry alone generates 20,000 rate filings per month, and, while there is no shortage of non-applicable data in this massive amount of documentation, tremendous value hides in plain sight once the relevant competitor filings are found.

To deliver the requisite transparency and build trust with everyone involved, data movement must be reduced if not outright eliminated. Chronic data movement, defined as a perpetual replication of the same data by disparate stakeholders, erodes faith in data quality while soaking up time and valuable resources to complete data validations and checks. A unified pricing platform reaches the data it needs while minimizing replication so that most of the effort is spent on business strategy rather than underlying mechanics.


Best’s Review contributor Tim King is an industry consultant, charged with defining Teradata’s value-led account strategy for a portfolio of insurance clients in North America. He can be reached at tim.king@teradata.com.


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