Text-Based Data for Insurance Market Insights

A McKinsey article on use of AI in the insurance industry calls data and analytics capabilities “table stakes” in the sector in Europe and North America. The article notes that “External data are the ‘fuel’ that is unlocking the value of artificial intelligence.”

While we may think that they are referring to numbers-based hard data, text-based data can have much more of an impact on insights for the insurance market. In this article, we review what text-based data is, how you can use it, and tools that will make data analysis and connection easy.

What is text-based data?

Text-based data is any data that is unstructured and not numeric. Typically, text-based data provides more context than numerical data and can be pulled from a variety of sources. Four of the most common sources are:

• Current and historical news data and social commentary
• Market and industry data
• Company and financial data
• Legal and regulatory data

Here are four ways that third-party data can bring more insights to decision-makers across the business.

Enhance risk assessments with targeted text-based datasets

Several sources of text-based data can enable analysis that improves risk awareness.

By analyzing news data, for example, insurers can identify emerging risks, such as natural disasters or social unrest, and adjust their underwriting practices accordingly.  In addition, ESG-specific news can provide insights that help your organization stay aligned with ESG commitments while also revealing potential reputational risks if third parties you rely on turn up in news mentions related to poor ESG performance.

Company and market data also add value to risk analysis. By analyzing data related to the financial performance of your own company, as well as other companies in your portfolio, you can identify areas of potential risk and proactively address them.

Regulatory and legal data analysis remains a top need, as well. As with other organizations across Financial Services, insurance companies are subject to a host of global regulations against financial crime, bribery and corruption. Just last year, the UK’s Financial Conduct Authority announced a settlement in excess of $9.4M with a UK-based subsidiary of a multi-national insurance brokerage for financial crime control failings.

From conducting risk assessments to undertaking third party due diligence and monitoring, datasets covering sanctions, watchlists, and politically exposed persons can help you accelerate risk management processes to identify regulatory risks sooner.  Furthermore, by monitoring legal data related to regulatory changes, you can keep your policies and practices up-to-date even as laws evolve. Similarly, by analyzing legal data related to enforcement actions or penalties, you can pinpoint areas of potential risk to inform strategic decision-makers.

MORE: 6 Ways Risk Managers Benefit From Using Data as a Service

Improve underwriting with third-party data

Deloitte notes that underwriters today “… are being challenged to move from hindsight, where underwriting decisions are evaluated after the fact, to foresight, where portfolios are actively monitored, to understand the impacts of risks added to their books of business in real time.” This pressure to anticipate risk and optimize performance demands looking beyond internal data for insights.

High-volume company and market data—along with global news (particularly adverse media)—can power predictive analytics, enabling you to spot emerging threats associated with insuring particular industries or companies.

By analyzing data related to market trends, financial performance and other relevant factors, you can gain much-needed insights to adapt products options to reflect the level of risk associated with a particular industry or company.

MORE: Using Data on Trends, Technology and Executives to Improve Financial Services

Manage claims more effectively with text-based data analysis

Monitoring news data related to incidents that may lead to claims, such as natural disasters and severe weather events, gives you the foresight mentioned earlier so you can deliver the right level of assistance and support when the need arises.  In this case, historical news data can be particularly useful. Analyzing media coverage of a past hurricane, for example, can help you predict what claims may be filed in a current incident.

Legal data can also be used to help insurers manage claims more effectively. By monitoring legal data related to court decisions or settlements in cases like those being handled by your organization, you can better predict the outcome of claims and adjust settlement strategies accordingly. Likewise, integrating legal data related to fraud or other deceptive schemes into analytics applications can help you detect and prevent fraudulent claims.

MORE: What is Unstructured Data?

Uncover game-changing competitive intelligence in text-based data

News, company and market data can also be used to help insurers better understand their competitors and the broader market. By analyzing data related to competitor market share, pricing, marketing strategies and product offerings, you can anticipate competitor moves, identify new opportunities for growth and expansion and set yourself up for a competitive advantage.

For example, with news and social commentary data you can conduct trend and sentiment analysis to gain valuable insights into emerging consumer needs and perceptions. This allows you to fine tune your strategies based on changes in consumer behavior or preferences as they evolve. Legal data related to emerging risks, such as cyber threats or climate change, can also inspire new products and services to address those risks.  

Where can you find the variety and volume of data sources you need? Talk with us to learn why our users say Nexis Data+ is the one-stop-shop for text-based data.