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.

• Insurance underwriting: Insurance underwriting involves assessing the risk associated with providing insurance coverage to an individual or entity. The underwriter evaluates the likelihood that a claim will be made and determines the premiums that should be charged to insure that risk. For example, underwriters in health insurance consider factors such as patient age, medical history, and lifestyle.

• Loan underwriting: Loan underwriting involves a bank or other financial institution evaluating the risk of lending money to a borrower. This includes checking the borrower’s creditworthiness, employment history, financial stability, and other factors to determine if they are likely to repay the loan. Based on this assessment, the lender decides whether to approve the loan and under what terms.

• Securities underwriting: Securities underwriting involves a financial institution (often an investment bank) assessing the risk and market demand for a particular security, such as a stock or bond. In an initial public offering (IPO), for example, the underwriter buys the shares from the issuers and sells them to the public, taking on the risk of not being able to sell all the shares at the anticipated price.

Underwriting helps control the risks taken on by banks, insurance companies, and investment firms and ensures that the pricing of financial products is fair and reflects the actual risk involved. 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
  

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 insuree 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.

• Exclusion and limitation setting: Underwriters decide on the terms of coverage including exclusions and limitations to mitigate potential losses from high-risk scenarios.

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.

• Collateral evaluation: For secured loans, underwriters assess the value and condition of collateral to ensure that it adequately covers the loan amount in case of default.

• 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.

• Regulatory compliance: Underwriters fulfill compliance requirements for relevant securities laws and regulations. This includes preparing prospectuses and other required disclosure documents.

Cross-sector techniques

Across all sectors, underwriters are increasingly using advanced techniques and technologies:

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

• Big data analytics: By analyzing large datasets, underwriters can gain deeper insight into risk factors and make more informed decisions.

• Automated underwriting systems (AUS): These systems 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.

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.

Simplify processing

The following techniques can reduce unnecessary steps, eliminate bottlenecks, and accelerate the overall process without compromising on quality.

• Automation: Use technology to automate routine tasks such as data entry, initial risk assessment, and documentation verification. Tools like Robotic Process Automation (RPA) can execute repetitive tasks quickly and accurately, and a 2022 study found that 65% of American insurers expected to see an increase in RPA spending in the insurance sector.

• 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.

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

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.

• 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.

Incorporate global data insights

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 machine learning algorithms to identify patterns and anomalies that could indicate fraudulent activities.

• Customer verification: Implement multi-factor 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.

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.

• Automated underwriting systems (AUS): Implement automated underwriting systems that can make standard decisions without human intervention, reserving manual review for complex or high-risk cases.

• 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.