Underwriting is the process of an individual or an institution taking on financial risk for a fee. This process is a core part of the financial services industry, especially in the insurance, loans, and investments sectors. In insurance, underwriters evaluate the likelihood of a claim and set premiums accordingly; in lending, they judge a borrower's creditworthiness to decide whether—and on what terms—to approve a loan; and in securities, they gauge market demand and risk before buying and reselling shares or bonds, as in an initial public offering (IPO).

Across all three sectors, strong underwriting keeps risk in check and keeps pricing aligned with reality. This guide will explain how underwriting works and how businesses can improve underwriting performance.

What’s in this article?

• How does underwriting work?
  
• How to improve the underwriting process
  
• Key metrics for measuring underwriting performance
  
• How Stripe Radar can help
  

How does underwriting work?

Underwriting works differently in each industry, as risks vary depending on the situation. Here’s how it works for insurance, loans, and securities.

Insurance underwriting

Insurance underwriting evaluates the risk and exposure of potential clients to determine whether they can be insured and what their premium rate should be.

• Risk assessment: Underwriters gather extensive data about the potential customer including personal information, health records, financial status, occupational hazards, and other relevant factors.

• Actuarial analysis: Underwriters use actuarial science to analyze historical data and statistical models and estimate the likelihood and financial impact of claims. They calculate the probability of events and their potential costs, adjusting for factors such as location, age, and lifestyle.

• Policy pricing: Underwriters set premiums based on the assessed risk to ensure profitability, balancing competitive pricing with adequate risk compensation and compliance with regulatory frameworks.

Loan underwriting

Loan underwriting assesses the creditworthiness of an applicant to minimize default risk and maintain the financial sustainability of the lending institution.

• Credit scoring: Underwriters review credit scores, credit history, and past financial behaviors to gauge repayment risks. This includes analyzing debt-to-income ratios, past defaults, and payment histories.

• Income verification: Underwriters conduct a thorough verification of the borrower’s income sources to confirm financial stability and sufficiency of funds to make loan repayments.

• Loan structuring: Underwriters structure the terms of the loan, including interest rates, repayment schedules, and loan covenants, based on the calculated risk. The goal is to protect the lender’s interests while providing fair conditions to the borrower.

Securities underwriting

Securities underwriting evaluates the viability of issuing new securities to the public, a necessary process for companies trying to raise capital through debt or equity.

• Market analysis: Underwriters conduct a market analysis to determine the demand for the new issuer, considering economic conditions, industry performance, and investor sentiment.

• Financial due diligence: Underwriters conduct a thorough review of the issuer’s financial statements, business model, and future forecasts to assess the financial health and prospects of the issuing entity.

• Risk pricing: Underwriters set the initial offering price by balancing investor interest with the issuer’s capital needs, often underwriting the securities at a committed price and assuming the risk of selling to investors.

How to improve the underwriting process

Underwriting tends to be complicated, and technological and analytical techniques such as automation and predictive modeling can speed up the process. Here’s how to improve the underwriting process.

Prioritize data integrity

The following techniques can improve the accuracy, consistency, and reliability of data throughout the underwriting process.

• Data verification tools: Implement advanced software solutions that automatically verify and cross-reference incoming data against external and internal databases to check for inconsistencies or errors. These types of tools are useful for tasks such as cross-referencing applicant information against identity or credit databases.

• Regular data audits: Schedule periodic audits of data used in underwriting to confirm it remains accurate and relevant, adjusting data collection methods as needed based on audit findings.

• Data entry protocols: Establish strict protocols for data entry, including double-entry verification or mandatory confirmation steps for important information.

User broader data sources for risk assessment

The following techniques can improve the underwriting process by integrating broader, global market data for risk assessments and pricing strategies.

• Big data analytics: Implement big data tools to analyze large amounts of data from global sources, providing a more comprehensive view of risk factors influenced by international trends.

• Cross-market collaboration: Facilitate information sharing and collaboration across different markets and geographies within the organization, yielding more diverse insights and expertise.

• Advanced predictive modeling: Use sophisticated modeling techniques that incorporate global economic indicators, political risk factors, and market dynamics to refine risk assessment models.

Prevent fraud

The following techniques can minimize the potential for fraud throughout the underwriting process, protecting against financial losses and reputational damage.

• Fraud detection algorithms: Deploy fraud detection tools powered by artificial intelligence (AI) to identify patterns and anomalies that could indicate fraudulent activities.

• Customer verification: Implement multifactor authentication and biometric verification during the application process to confirm the identity of applicants.

• Continuous monitoring: Use continuous monitoring to watch for suspicious activities throughout the lifecycle of the financial product.

Perform underwriting quality checks

The following techniques can help maintain high standards of underwriting quality and facilitate consistent, reliable, and fair outcomes.

• Peer review: Establish a peer review process where underwriters regularly review each other’s work to maintain a high standard of quality and compliance.

• Quality assurance: Develop specific metrics and benchmarks for underwriting quality and monitor these metrics regularly to identify areas for improvement.

• Regular training and development: Provide ongoing training and professional development opportunities for underwriters to keep up with the latest technology, trends, and regulatory changes.

Use technology approaches across all underwriting types

Across all sectors, underwriters can tap into advanced techniques and technologies for efficient results:

• Artificial intelligence and machine learning (ML): Use these technologies for predictive modeling. They can improve the accuracy of risk assessments by identifying subtle patterns and correlations that traditional models might miss.

• Big data analytics: Analyze large datasets to gain deeper insight into risk factors and make more informed decisions.

• Automated underwriting systems (AUS): Use these systems, which provide quick preliminary risk assessments, reducing human error and processing times that can take up to 60 days to complete manually. They still require final approval by human underwriters in complex cases.

Standardize and automate processes

The following techniques can promote consistency in underwriting decisions and improve efficiency through standardization and automation.

• Standard operating procedures (SOPs): Develop and enforce standard operating procedures for all common underwriting tasks to create consistency across all operations.

• Process re-engineering: Revise underwriting workflows to remove redundant steps and improve the sequence of operations. This might include consolidating tasks, changing the order of operations, or integrating functions.

• Automated underwriting systems: Implement automated underwriting systems that can make standard decisions without human intervention, reserving manual review for complex or high-risk cases. Robotic process automation (RPA) software also automates repetitive, rule-based digital tasks like data entry and documentation verification quickly and accurately. A 2022 study found that more than 65% of American insurers expected to see an increase in RPA spending in the insurance sector.

• Digital submission and processing: Encourage the use of digital forms and electronic document submission to speed up data collection and reduce manual handling errors.

• AI and ML: Use artificial intelligence and machine learning to improve decision-making processes, enable real-time data processing, and create adaptive risk assessment models.

Key metrics for measuring underwriting performance

Tracking the right metrics helps businesses gauge how efficiently and accurately their underwriting process is running, and pinpoint where to improve. Here are five key metrics to monitor:

• Underwriting cycle time: The average time it takes to move a submission from initial application to final decision.

• Loss ratio: The percentage of premiums paid in claims, used to gauge whether risk is being priced accurately.

• Approval rate: The share of applications that result in approved coverage, credit, or issuance.

• Data error rate: The frequency of inaccuracies or missing information in submitted applications and supporting documentation.

• Straight-through processing (STP) rate: The percentage of applications that are fully processed by automated systems without manual intervention.

How Stripe Radar can help

Stripe Radar helps detect fraud and unlock growth, using AI trained on data from Stripe’s global network. Radar helps protect your business from fraud throughout the customer lifecycle before it affects your bottom line, while helping you approve more legitimate customers and payments.

Radar can help your business:

• Prevent fraud losses: Radar’s AI learns from more than $1.9 trillion in annual transactions across Stripe’s global network, helping it identify and block fraud patterns that individual businesses may not catch on their own.

• Unify protection across evolving fraud attacks: Radar brings protection against transaction fraud, account fraud, and customer abuse all into one solution, so you gain unified protection across major fraud types.

• Adapt as fraud evolves: Radar continuously adapts to changing fraud patterns, from first-party abuse to agentic transactions, helping protect your business against emerging threats.

• Work with your existing tech stack: Use Radar with Stripe payments with no integration required, or access Radar’s intelligence through programmable APIs, whether you process payments on Stripe or not.

Learn more about Stripe Radar, or get started today.