How to Harness Decision Intelligence for Smarter Commercial Underwriting

Commercial and specialty underwriting have long relied on data from multiple sources to drive decisions. Over the past 5 to 10 years, the volume and variety of data have increased exponentially due to the explosion of digitalization, Internet of Things (IoT), and telematics. Meanwhile, the risk landscape is evolving rapidly—regulatory hurdles, catastrophic (CAT) risks, the expansion of Excess & Surplus (E&S) insurance lines, and the rising cyber threats demand new approaches to underwriting.

It’s no longer just about managing more data; insurers must determine what data to use, where to find it, and how to connect it. While this data wealth holds great promise for further improving underwriting accuracy and automation, much of it remains underutilized. Accenture reports that 72% of underwriters cite the lack of integration of key data sources as a significant challenge, highlighting the data-decision gap—where insurers have vast amounts of information but struggle to extract actionable insights.

Challenges in traditional underwriting

Despite advances in technology, several obstacles still prevent the full resolution of the data-decision gap:

• Inherent complexity: Commercial underwriting often deals with multifaceted risks, diverse coverage needs, and unique one-off scenarios. Many risks lack reliable historical data or involve entirely new products. While AI can support pre-bind decisions for well-defined risks (e.g., parametric insurance), most commercial risks remain too complex for full automation, often defaulting to basic rules-based approaches.

• Inconsistent, siloed data: Underwriting data remains fragmented despite digital transformation efforts like Lloyd’s of London’s Blueprint Two. Rather than just centralizing data, the real challenge lies in data quality and understanding relationships across commercial entities. For example, underwriting teams may have multiple versions of “PEPSI” in systems, such as PepsiCo, Pepsi Global, or fail to recognize that a smaller entity is part of a broader corporate structure, leading to missed exposures and inefficiencies.

• Disconnected systems: Insurers often face the challenge of integrating various systems, such as policy administration systems, business intelligence tools, and cloud technologies. Mergers, acquisitions, and new product launches can further exacerbate these disconnects. While each system aims to enhance data usage, their lack of integration often creates silos rather than a unified solution.

• Regulatory hurdles: Increasing regulatory requirements demand that insurers provide the right coverage, at the right price, and under the right terms, but outdated regulatory frameworks often limit their ability to adapt. For example:

  • In the UK, the Insurance Distribution Directive (2018) and ongoing challenges in commercial insurance pricing demonstrate the growing accountability placed on insurers.

  • In the US, state regulations affect risk assessment. For instance, California mandates pricing based on historical loss data, preventing insurers from using forward-looking climate risk models, forcing them to shift risks to E&S lines for more flexible pricing.

Turning data challenges into opportunities

Despite these challenges, emerging technologies, particularly decision intelligence, offers a powerful solution to bridge the data-decision gap.

What is decision intelligence?

Decision intelligence is a process that uses composite AI to unify, cleanse, and contextualize data, turning raw, siloed information into actionable intelligence. Instead of fragmented, low-quality data, underwriters gain a holistic view of risk and opportunity, enabling smarter, faster, and more transparent decisions.

The role of decision intelligence in transforming commercial underwriting

1. Creating a holistic view of risk and opportunity

   Decision intelligence solves the challenges of inconsistent data and disconnected systems. It breaks down silos by integrating data from internal and external sources, including claims history, financial records, credit reports, industry classifications, corporate registries, news, sanctions, and watchlists. This connected customer and risk-centric view enables commercial underwriters to assess risks comprehensively, leading to better-informed decisions.

2. Enhancing efficiency through real-time risk assessment

   Decision intelligence streamlines the inherently complex underwriting process by reducing manual effort and accelerating decision-making. According to Accenture, underwriters spend 40% of their time on administrative tasks, such as manually gathering data from internal systems and external sources, and then cross-referencing the information. Decision intelligence solutions unify these datasets automatically and leverage advanced analytics and transparent AI to deliver real-time insights. For example, in commercial underwriting, insurers can instantly access comprehensive risk information by integrating internal claims data, business credit scores, and corporate registries, significantly enhancing underwriting efficiency.

3. Ensuring compliance and transparency

   Decision intelligence helps insurers navigate regulatory hurdles to ensure compliance and transparency. With explainable AI models, underwriters gain a clear, data-driven rationale for risk selection and pricing, allowing them to confidently demonstrate that their decisions are grounded in reliable and well-documented data. This transparency not only streamlines compliance audits but also enhances regulatory filings, reinforcing a structured, evidence-based approach to risk assessment and justification.

4. Identifying growth opportunities through business relationships

   Beyond risk assessment, decision intelligence empowers insurers to uncover hidden opportunities by analyzing business relationships and connections. This allows insurers to:

   Identify cross-sell opportunities within a corporate structure, such as bundling commercial property and liability coverage for companies within the same group, or expanding sales to additional entities within the group

   Highlight upsell potential—for example, if an insurer identifies a subsidiary of a major engineering firm that handles shipping and disposal of environmental waste, they may offer specialty products tailored to environmental hazards and reputational risk

Case study: transforming underwriting with Quantexa’s Decision Intelligence Platform

Background and challenges: A top 10 U.S. insurance carrier offering both personal and commercial lines faced significant challenges in their underwriting operations:

• Siloed data: spread across more than 20 lines of business and over 10 million customers

• Prolonged policy issuance: taking weeks to gather and validate information

• Disparate corporate and financial data: leading to inaccurate risk assessments

Solutions: These challenges were addressed with Quantexa Decision Intelligence solutions, featuring Entity Resolution, to create a single, unified view across diverse lines of business, achieving remarkable transformation in underwriting operations. Quantexa's Decision Intelligence Platform made the following improvements:

1. Unified & de-duplicated data: It created a single view of policyholders, prospects, portfolios, and relationships by consolidating data from internal and external sources across multiple business lines, and removed redundancies across external registries like D&B and Experian

2. Automation of business-personal linkages: It streamlined the recognition of business and personal associations, as well as corporate hierarchies

3. Advanced link analysis: It revealed both direct and indirect connections to known intelligence for deeper insights

4. AI-driven risk assessment: It enabled real-time risk scoring using advanced analytics

Outcomes achieved:

• Improved data quality: The new processes eliminated data silos and achieved a 98% match accuracy rate, up from ~60%, empowering analytics and data science teams

• Accelerated and effective underwriting: They reduced policy issuance time from weeks to mere hours, 10-30x faster

• Sales opportunities: They unlocked new high-value cross-sell opportunities by connecting data across commercial lines, group benefits, and global specialty businesses

• Cost savings and efficiency gains: They enabled 75% automation in pre-population and analytics modeling, reducing manual effort

• Transparency in models: They delivered open-box configurations and explainable AI models, ensuring clear decision-making processes and regulatory compliance

• Enhanced fraud detection: They supported fraud investigations and special investigation units (SIU)

The results were transformative. According to the Chief Data Officer: "Underwriting is having great success. Work that previously took over 40 hours across two weeks can now be done in seconds by this system and minutes by the team."

The path forward

The journey to smarter, more efficient underwriting starts with leveraging decision intelligence to unify data, enhance risk assessment, and accelerate decision-making. But where should insurers begin, and how can they quickly realize value?

1. Start with high-impact use cases

   Begin by identifying underwriting areas where manual processes slow down decisions or where data gaps create risk blind spots. This includes property and casualty, where combining historical claims, geospatial, and alternative data can enhance pricing accuracy, as well as specialty lines, where risk factors require deep relationship insights beyond traditional financial and claims data.

2. Establish a strong data foundation

   By leveraging decision intelligence platforms, insurers can unify internal and external data sources, create a 360-degree view of businesses and individuals, and integrate AI-driven insights to improve risk segmentation and pricing.

3. Prioritize quick wins for immediate value

   Rather than waiting for a multi-year transformation, insurers can deploy decision intelligence in phases, focusing on quick wins such as automating manual data collection to cut underwriters’ administrative time, enhancing risk visibility to reduce reliance on incomplete records, and accelerating quotes to improve customer conversion. Most insurers see measurable efficiency gains within 3–6 months, with broader business impact scaling in 12–18 months, as decision intelligence integrates across underwriting workflows.

4. Scale for competitive advantage

   Once initial success is achieved, expand decision intelligence into adjacent areas, such as such as claims, personalized customer experience, and servicing, to drive sustained growth. You can learn more about how decision intelligence is revolutionizing the entire insurance value chain here.

Take the first step today

The underwriting landscape is evolving rapidly, and insurers that embrace decision intelligence now are gaining a competitive edge. The time to act is now—start by identifying quick-win opportunities, exploring decision intelligence solutions, and transforming underwriting.

Want to see it in action? Request a demo of Quantexa's Decision Intelligence Platform or speak with our experts to explore how you can embed data-driven decision-making into your digital strategy.