Navigating the shadows in private entity underwriting: How financial lines underwriters can take advantage of deep data and predictive analytics in a predominantly private world.

In the intricate realm of financial lines underwriting, private entities typically represent around 85% of the underwriting portfolio. Publicly available information on these entities can be poor quality or difficult to come by, which presents unique challenges. These issues are exacerbated by an era of rapid digital growth and continued scrutiny on financial metrics, causing a deliberate shift toward data-centric and analytical approaches. 

With the insurance industry generally slower to adopt new technologies, the task of collecting and processing data grows in scale. Underwriters, therefore, find themselves frustrated by the diminishing time available for selecting risks, negotiating prices, and managing customer relationships. This article explores how data and predictive analytics can enhance efficiency and allow time for these more value-adding activities.

Private entities operate under a layer of privacy that can conceal their internal operations, financial health, and risk factors from public scrutiny. This confidentiality helps these entities maintain their competitive edge, but it makes assessing the risks associated with them, setting premiums, and recommending mitigation strategies challenging. Since financial lines underwriting accuracy relies heavily on data and transparency, these processes require more innovative approaches to risk assessment.
 

Insurance innovation in practice: addressing the challenges in private entity underwriting for financial lines business.

Financial transparency and comparable data

Unlike publicly traded companies, private entities of a certain size or located in certain jurisdictions may not be required to disclose their financial statements; even if they do disclose, annual reporting frequencies can make it difficult to assess their financial stability and creditworthiness. These companies can also have complex ownership structures, involving layers of holding companies or trusts, which may complicate risk assessment on governance and compliance. Private entities might not disclose all operational risks to regulatory bodies, including pending litigations or regulatory investigations, which can significantly affect their risk profile for underwriting financial lines business. 

To navigate these challenges, robust company reference data, industry expertise, technology, and powerful analytics can work together to penetrate the complexity of private entities. Advanced analytical tools, such as predictive analytics and machine learning algorithms, empower financial lines underwriters to draw valuable conclusions from limited data by identifying patterns and potential risks that may not otherwise be immediately evident. For example, these tools can help analyze basic company information to output predictive analytics involving a private entity’s risk profile for defaulting or undergoing a negative credit event.
 

Integrating AI-powered news sentiment analysis into the financial lines underwriting process

Advancements in AI-powered news sentiment analysis power real-time insights directly within existing underwriting workflows and systems. By customizing news sentiment analysis to fit the unique needs and technological ecosystems of insurers, the process becomes a natural extension of the underwriting practice, enhancing decision-making with minimal disruption. Private companies typically submit financial accounts once a year. However, this reporting frequency can hinder an underwriter’s understanding of performance between annual snapshots. In contrast, news is published constantly, offering a more dynamic perspective on an organization’s activities.
 

Streamlined cyber risk assessment  

Cybersecurity risk scores and analytics can help underwriters identify potential cyber risks and incident probabilities and highlight entities with exemplary cybersecurity practices. Integrating these factors into underwriting workflows allows for continuous portfolio security health assessments and empowers underwriters to perform informed policy underwriting based on historical data. It can help underwriters identify emerging threats, avoid high-risk policies, use trends to mitigate losses, and offer a proactive approach to managing cyber vulnerabilities. Cyber risk data can also provide insights into the strength of a board's governance. The effectiveness of the organization’s cybersecurity program can indicate the board’s commitment to understanding and mitigating risks.
 

Enhanced know your customer (KYC) procedures 

Robust KYC processes fueled by company reference data can help underwriters uncover the ownership structures and affiliations of private entities. This information is crucial for assessing governance risks and ensuring compliance with regulations. KYC screening also typically involves adverse media screening, which could highlight companies or individuals involved in events that could change an underwriter’s risk appetite—such as regulatory actions or financial crimes. This can be useful when there is minimal data available about a private company’s directors.
 

Navigating the future with efficiency, confidence, and precision 

The dominance of private entities in financial lines underwriting portfolios presents a complex challenge; one that demands a sophisticated blend of data, technology, analytics, and industry expertise. By embracing these capabilities, underwriters can develop a more nuanced understanding of the risks associated with insuring private entities, helping them navigate challenges with efficiency, confidence, and precision—and allowing them to focus on the value-adding aspects of their role. As the underwriting landscape evolves, the ability to assess the risks associated with private entities will remain a cornerstone of success for financial lines underwriters.

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