In the data-driven world of insurance, clean information is king. Accurate data underpins everything from risk assessments to pricing models, ultimately impacting the profitability and success of an insurance company. But here’s the sobering reality: many insurance companies struggle with data quality issues.

1. Data Consolidation Challenges:
2. Legacy Systems Hinder Progress:
3. The Path to Clean Data and Profitability:
4. Investing in Data Quality: A Sound Investment
5. The Bottom Line:

“The greatest challenge facing our industry’s data leaders is the quality and the integrity of data. A significant contributor to that will be which external and enrichment data sources are being used.”

Chris Wyard, Head of Technical Data at Allianz Insurance

The High Cost of Low-Quality Data:

• Misleading Risk Assessments: Inaccurate data can lead to a distorted view of risk, potentially resulting in underpriced policies and financial losses.
• Lack of Confidence: A mere 24% of insurers feel “very confident” about their data accuracy. In essence, most are operating with a nagging uncertainty about the information they rely on.
• Outdated Information: Timeliness is key. Outdated data makes it difficult to assess current risks and emerging perils.

Data Consolidation Challenges:

• Fragmented Data Sources: Consolidating data from diverse sources like customer applications, external databases, and legacy systems can be a complex task.
• Accuracy and Integrity Issues: Ensuring data is consistent and free of errors across different sources is critical for reliable risk assessments.

Legacy Systems Hinder Progress:

• Outdated Technology: Legacy systems and software often create roadblocks for efficient data management. This leads to:
  • Duplicate Data: Multiple entries for the same information can distort the overall picture.
  • Outdated Information: Stale data provides an inaccurate view of risks.

The Path to Clean Data and Profitability:

The good news? Insurers are recognizing the importance of data quality and are actively seeking solutions. Here are some key strategies:

• Modern Data Technology: Investing in modern data management solutions can improve data organization, accessibility, and overall quality.
• Data Quality Automation: Only 26% of senor insurance executives in a 2023 Arizent and Digital Insurance survey regard manual data cleansing to be effective, while leading organisations are automating data cleansing processes to reduce errors and improve data consistency.
• Self-Service Data Management: Empowering users with self-service tools for data access and correction can further enhance data quality.

Investing in Data Quality: A Sound Investment

In response to economic challenges and changes in consumer spending, businesses need to make confident and quick decisions.

Leading companies rely on data to guide their growth, especially during uncertain times. Having access to reliable data becomes even more important for making decisions, like:

• Accurate Risk Assessments: Clean data translates to accurate risk assessments, leading to fair pricing and informed underwriting decisions.
• Improved Decision-Making: Confident decision-making based on reliable data empowers insurers to navigate market opportunities effectively.
• Compliance with Regulations: Maintaining high data quality ensures compliance with industry regulations and avoids potential penalties.

The Bottom Line:

Data quality is not just a technical challenge; it’s a strategic imperative for the insurance industry. By implementing effective data quality solutions, insurers can unlock the true potential of their data, driving profitability, customer satisfaction, and a secure future.