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The Hidden Costs of Bad Data — 5 Ways Carriers Can Overcome Them

Bad data costs carrier 20%

By Murray Izenwasser, SVP, Digital Strategy


Welcome to the fourth edition of the blog series “Under the Hood: Unlocking the Hidden Value in Insurance Data.” Ever considered the long-term costs (and risks) carriers incur due to poor-quality data? In Property & Casualty (P&C) insurance — where risk assessment hinges heavily on data accuracy — having the right data at the right time is crucial to price risk, identify exposure, and reduce costs. This blog post explores how you can avoid the pitfalls of poor data before it becomes a significant problem. 

The Hidden Costs of Bad Data
The costs of bad data add up quickly. In fact, poor data quality can cost as much as 15-25% of total revenue, according to a study conducted by MIT Sloan. Poor data quality increases costs associated with the re-execution of a process due to data errors, correction efforts, and accruing out of lost or missed revenues. Conversely, according to the Sirius Group, quality data can lead to a 70% increase in revenue. With the right tools, carriers can turn the mountain of data they possess into a goldmine of opportunity.

But first, what do we mean by data quality?

Data quality is the measure of how well a dataset meets the criteria for accuracy, completeness, validity, and consistency — crucial to data governance within an organization.

Good data helps organizations make better decisions. If data issues, such as duplicate data, missing values, and outliers aren’t properly addressed, carriers increase their risk leading to less-than-optimal outcomes. According to a Gartner report, poor data quality costs organizations an average of USD 12.9 million each year.

Is the Data You Have the Data You Want?

In the insurance industry, data accuracy is everything. The efficiency of the claims process is contingent upon the adjuster’s ability to verify a claim, which is based on having accurate data. The foundation of claims automation is also accurate data, both structured data from the system and unstructured data from the filing, to determine the appropriate course of action for each claim. The automation system continually reassesses its previous decisions as new information is added. Attempting this process with inconsistent or “dirty” data can lead to erroneous decisions and a poor customer experience.

Carriers wrestle with poor data quality because of the vast amounts of unstructured data that are stored in disparate systems. Many of these are legacy systems, while others are desktop-based actuarial applications. The problem is compounded by newer applications that have been added to the legacy systems, creating multi-layered, redundant IT architectures.

The Pitfalls of Bad Data

  • Insurance underwriters depend on accurate data for risk assessment, which can influence premiums, policy terms, and profitability
  • Poor data quality increases costs associated with the re-execution of a process due to data errors, correction efforts, and lost or missed revenues.
  • Inaccurate, inconsistent data leads to less-than-optimal decisions. For instance, when underwriting and pricing property insurance, carriers often rely on the insured or their agent to provide Construction, Occupancy, Protection, and Exposure (COPE) details about the property. These details enable the carrier to evaluate the potential loss associated with the property and price it accordingly. However, the insured may not always provide accurate information, leading to policy underpricing and increased losses.
  • Lack of trust in data prevents insurance leaders from making the right decisions — sometimes leading to regulatory non-compliance.
  • Carriers must comply with strict regulations around data accuracy, completeness, and appropriateness; failure to do so may result in huge fines and penalties.
  • Many carriers grapple with inaccurate underwriting data, necessitating requests for additional information from agents and customers, leading to longer underwriting turnaround times and lost business.

How to Avoid Them

1. Automate Repetitive Tasks:

Data collection and processing is a repetitive, tedious task prone to human error. These errors could range from incorrectly understood instructions, typos, mismatched names and emails, duplicate records, or simply overlooking certain entries. These errors and an overwhelming amount of incorrect and incomplete data can accumulate and become significant inconsistencies over time. By automating these manual, time-consuming tasks, carriers can reduce error, cut costs, and provide a better customer experience while freeing up employee time for higher-value work.

2. Standardize Processes:

In lieu of standardized protocols for data collection, different teams might adopt different methodologies for the same data. This inconsistency can cause discrepancies when data is combined or compared.

3. Integrate Your Data and Systems:

P&C carriers operate in different regions around the world. With a large global footprint, local and overseas business units tend to have various systems to manage policy and claims data, financial information, and marketing and sales data. However, if this data is not integrated into a common platform and view, it could hamper decision-making over time.

Here’s how a global P&C carrier consolidated its data sources with intelligent automation and reaped the benefits of a standardized platform. Read the full story.

4. Modernize Legacy Systems:

Legacy systems are not equipped to handle newer data types and large volumes or share data across departments, leading to delays and lost opportunities. Compare the standard insurance approval process to a digital model, which allows customers to lodge a claim from anywhere, upload photographs or details of the damage from their smartphone, and where automated underwriting processes can approve the claim almost immediately. The latter is faster, more efficient, and provides a seamless experience for the policyholder. Besides, older systems might lack the safeguards or validation checks present in more modern solutions.

5. Perform Data Quality Checks:

Carriers must perform periodic reviews and cleaning of databases to maintain data quality. If these checks are not carried out regularly, inaccuracies can persist and compound, leading to a deterioration in data quality.

Bad data in all its forms costs the insurance industry more than you realize, from the more easily recognized financial and productivity effects to impacts on customer experience. While investing in comprehensive data integration may seem like a costly pursuit, what carriers truly cannot afford is bad data.

Looking to integrate data from multiple sources so that you can get a complete picture of your business? Contact us.