By James Hargreave, Moody's

Data is the lifeblood of the insurance industry, enabling insurers to design products, price risks, underwrite policies, process claims, and manage assets. However, many insurance companies struggle with data management due to inconsistent, inaccurate, or untimely external data from third parties. Moreover, vast amounts of structured and unstructured data are often stored in siloed systems and managed by different functions, an issue worsened by the highly acquisitive nature of the industry.

These challenges can result in poor data quality, which can lead to major consequences for insurers and insured alike, including: 

• Unsatisfactory customer experience due to errors, delays, and dissatisfaction.
• Missed opportunities for insights, trends, and patterns that could improve products, pricing, marketing, and risk management; in some instances, companies can miss new business opportunities due to low entity resolution rates or lack of required firmographic data points. 
• Impaired underwriting processes leading to higher costs, lower profitability, or increased exposure to fraud or losses.
• Slowed claims settlements causing higher expenses, lower customer satisfaction, or legal issues.
• Regulatory pressure exposing the insurer to scrutiny, sanctions violations, or fines.
• Reputational damage harming the insurer’s competitive edge, credibility, or trustworthiness.

To address these issues, we encourage insurance companies to:

• Adopt a strategic and holistic approach to data management, reducing the effort and cost of repeated tactical data-quality fixes; this approach should align data quality with business goals and customer needs.
• Invest in modernizing their data infrastructure and platforms, integrating and standardizing their data sources and processes.
• Leverage data and technology to enhance their analytical capabilities and innovation potential. 

Developing a strategic approach to data management, modernizing infrastructure, and enhancing analytical capabilities can help the insurance industry create value for its customers and stakeholders.

One effective way to improve data management is by creating a framework that ensures data sources’ accuracy, integrity, and consistency across different systems and applications.

Why is a proper data governance and management framework important?

Entity validation

This critical first step in creating a company record in systems like underwriting workbenches or customer relationship management platforms is often reliant on manual data entry, leading to inefficiencies and errors. For instance, Lloyds reported that 50% of inbound leads were entered incorrectly the first time. [1] A robust data framework using matching and enrichment API's helps effectively create and resolve entities in systems, improving the quality and reliability of data used for underwriting, claims, risk management, and analytics.
 

Customer experience

A complete view of customer data, known as Customer 360, is essential for driving personalized customer experiences. Connected third-party master data enables effective account-based marketing (ABM), which targets accounts with higher expansion opportunities and can lead to greater revenue growth.

Firms using ABM find new prospects via lookalike search capabilities and automated targeting processes that require firmographic data for more accurate and efficient targeting. Well-managed master data also helps firms grow existing customer relationships by identifying more cross-sell and upsell opportunities, further amplifying monetary growth.
 

Data insight

Data is crucial for decoding risk and unlocking opportunities, but the market often deals with limited data granularity for certain company datasets. Required data insights include industry codes, corporate hierarchies, financials, and other risk-related data attributes. Accurate, complete, and up-to-date data is essential for informed decision-making and developing models to identify factors contributing to high loss ratios. Machine learning and artificial intelligence (AI) can drive data insights, but without a robust data management framework, gaining the desired benefits from these technologies is challenging.
 

Regulatory compliance

The insurance industry is highly regulated, with requirements varying by jurisdiction. Robust know –your customer (KYC) and anti-money laundering (AML) measures are key to maintaining integrity, preventing fraudulent activity, and minimizing financial and reputational risk. AI and machine learning can help detect fraud patterns, identify sanctions exposure, and drive risk mitigation strategies.

The value of master data management in the insurance industry

The insurance industry is increasingly recognizing the value of data management and is ramping up its investment in this area. According to Deloitte Digital, “76% of carriers want better integration between IT and business units.” [2] Effective data management is crucial for the harmonization of IT and operational processes, providing insurers with an opportunity to differentiate themselves and create value for their customers and stakeholders. By improving process speed, reducing the number of required fixes, and facilitating easier interactions, insurers can capture more of the market. 

Investing in data transformation initiatives allows insurers to leverage their data and technology for a competitive edge in a changing market, ensuring they are well-positioned to maximize the potential of future markets shaped by the internet of things and generative AI.

Footnote:

¹ Lloyds Blueprint Two Interactive Guide Second edition January 2022 - https://www.velonetic.co.uk/sites/default/files/2024-04/Blueprint%20Two%20Interactive%20Guide%20second%20edition_PPS.pdf

² Deloitte Digital - https://www.deloittedigital.com/us/en/hub/insurance-sector.html