How to Underwrite an Insurance Policy: In-Depth Guide

This guide explores how modern underwriting works, the steps involved, common challenges, and how technology solutions like Sapiens can enhance underwriting operations while ensuring profitable, risk-appropriate growth.

In today’s dynamic insurance environment, underwriting has evolved far beyond its traditional role. With customer expectations rising, risks growing more complex, and regulatory scrutiny intensifying, insurers need a more agile, data-driven approach. A recent report from McKinsey & Company revealed that 40% of underwriting work can now be automated using existing technologies, improving accuracy, consistency, and speed while freeing underwriters to focus on higher-value cases like those involving high-risk professions or unique property exposures.

How Insurance Underwriting Works 

Insurance underwriting is the process insurers use to assess the level of risk a potential policyholder presents and decide whether to accept, modify, or decline that risk. It directly influences premiums, policy terms, coverage limits, and exclusions. More importantly, underwriting safeguards an insurer’s financial health while ensuring fairness and transparency for applicants. 

The underwriting process applies to every line of insurance, from life and health to auto, property, and commercial coverage. Each decision impacts both the insurer’s bottom line and the customer’s experience, making it a pivotal operational function. 

Four Key Steps in the Underwriting Process 

While underwriting workflows can vary by product type and insurer, most processes follow a structured progression.  

1. Application Review where underwriters assess the details provided by the applicant to ensure accuracy, completeness, and initial eligibility. According to a LIMRA study, 30% of life insurance applications in the U.S. are now processed through accelerated underwriting programs that integrate predictive models and external data sources, reducing reliance on manual reviews and medical exams.

Example: A 35-year-old applicant submits a life insurance application online through a major U.S. insurer. Instead of waiting weeks for manual document checks and a medical exam, the system pulls data from external sources — like prescription history, motor vehicle records, and credit-based insurance scores. The underwriter then reviews the application and moves on to the next step.

2. Risk Assessment, in which insurers evaluate data such as medical records, credit histories, claims reports, or property valuations to determine the applicant’s risk profile. Accurate underwriting depends on the ability to gather, verify, and analyze relevant applicant data. For life insurance, that might mean reviewing medical records, lab results, and financial data. In property and casualty, factors like property condition, business operations, or geographic risk come into play.

Example: A small business owner applies for commercial property insurance for a warehouse in a coastal area. The insurer evaluates the building’s age, maintenance records, local crime rates, and flood zone data. A recent inspection report shows the roof was replaced last year, and the business has no prior claims. Using geographic risk models and property valuation tools, the underwriter determines the location has moderate flood risk but low theft risk.

3. Risk Analysis. Based on this analysis and established underwriting guidelines, the insurer then decides whether to accept, modify, or decline the risk and sets appropriate policy pricing and terms. Standardized underwriting guidelines are essential for maintaining consistency and objectivity. These guidelines define acceptable risk thresholds, documentation standards, and decision-making criteria for different products and applicant profiles. They help insurers protect profitability, manage risk exposure, and comply with regulatory requirements while ensuring fair and transparent treatment of applicants.

Example: Using standardized underwriting guidelines, an insurer evaluates whether a new house in the suburbs meets acceptable risk thresholds. The home is in good condition, but it’s located in an area with a slightly elevated burglary rate and lacks a security system. The analysis is that a higher deductible may be necessary with a higher premium for theft coverage and the recommendation that a home security system be purchased by the owner.

4. Policy Issuance. If the application is approved, the final step is policy issuance, where terms and premiums are clearly outlined, and the policy is formally issued to the customer. Insurance customers can now use AI tools to get a clearer understanding of what is included and excluded in their policy by asking tools such as Chat GPT to summarize the entire policy. This also makes the coverage more transparent for the insured.

Example: A 50-year-old applicant is approved for a life insurance policy. The insurer finalizes the policy terms, including coverage amount, premium, exclusions, and riders, and issues the policy digitally. The customer uses ChatGPT to help understand the fine print. They paste the policy text into the chat and ask for a summary of key coverage details, exclusions, and renewal terms. The AI tool breaks down the legal language into plain English.

Modern underwriting increasingly enhances these steps with predictive models, external data sources, and advanced analytics to supplement traditional risk factors. This data-driven approach helps insurers make faster, more consistent decisions while improving risk accuracy and operational efficiency.

The Role of Technology in Underwriting 

Challenges in Insurance Underwriting 

Even with technological advances, underwriters still face significant challenges. Expanding data volumes require integrating multiple, often fragmented sources while maintaining accuracy and relevance. At the same time, customer expectations have shifted, with applicants demanding faster, more transparent decisions and less manual paperwork. Regulatory pressures continue to mount, as insurers navigate evolving legal frameworks while ensuring fair, compliant underwriting. 

Risk profiles are also changing, with new occupations, extreme hobbies, and cyber-related exposures forcing underwriters to update risk criteria. The rise of adventure tourism, gig economy jobs, and drone businesses, for example, presents underwriting complexities traditional guidelines weren’t built for. To address these issues, insurers are adopting decision automation, advanced analytics, and centralized business rule management to streamline workflows and improve risk accuracy. 

Regulatory Considerations in Underwriting 

Regulatory compliance is integral to underwriting, especially in sectors like life, health, and commercial insurance. Laws such as the U.S. Fair Credit Reporting Act (FCRA) and the EU’s Solvency II directive govern how insurers collect, store, and use applicant information, and how they document underwriting decisions. 

Insurers must ensure fair treatment of applicants, prevent discriminatory practices, and maintain detailed audit trails for regulators, adding complexity to underwriting workflows that technology can help manage. 

Improving Underwriting Efficiency with Sapiens 

For insurers seeking to modernize underwriting, Sapiens delivers advanced solutions purpose built for today’s operational realities. Sapiens’ underwriting solution integrates seamlessly with policy administration, claims, and data management systems, giving insurers centralized decision control while enabling straight-through processing for routine applications. 

With AI-driven risk scoring, configurable decision logic, and business-friendly rule management, Sapiens empowers underwriting teams to manage complex risks efficiently, automate decisions at scale, and reduce time-to-issue without compromising accuracy or compliance. 

Meet Sapiens 

Sapiens is a trusted global technology partner to the insurance industry, with four decades of experience helping insurers transform core operations. From underwriting and claims to data management and digital engagement, Sapiens equips insurers to scale effectively, optimize customer experiences, and improve profitability in a rapidly changing market. 

FAQs 

• What is the primary purpose of insurance underwriting? 
  To assess applicant risk, determine appropriate policy terms and premiums, and protect the financial health of the insurer while offering fair coverage. 

• How do underwriters assess risk in policy applicants? 
  Through a combination of applicant-provided information, external data sources, medical records, financial data, and predictive models, guided by standardized underwriting guidelines. 

• What role does technology play in modern underwriting? 
  Technology streamlines underwriting by automating routine decisions, integrating real-time data, and improving risk accuracy with predictive analytics and AI tools. 

• What are common challenges in the underwriting process? 
  Managing expanding data volumes, meeting heightened customer expectations for speed and transparency, evolving risk profiles, and navigating increasingly complex regulatory frameworks. 

• How can Sapiens’ solutions improve underwriting efficiency? 
  By automating routine underwriting tasks, centralizing business rules, integrating data sources, and enabling straight-through processing for faster, smarter underwriting decisions. 

Summary 

Insurance underwriting remains the cornerstone of risk management and profitability in the industry. As risks evolve and customer demands rise, insurers must embrace modern, data-driven underwriting solutions to stay competitive. By combining advanced analytics, AI, and integrated decision management tools, underwriting teams can deliver faster, fairer, and more accurate decisions. 

With flexible, AI-powered underwriting solutions, Sapiens helps insurers accelerate underwriting cycles, reduce operational costs, and drive sustained, profitable growth.