Exploring Role of Automation in Various Underwriting Types

What are the Types of Underwriting?

There are three different types of underwriting, let’s discuss them in brief.

1. Loan Underwriting

   Loan underwriting involves assessing the risk associated with lending money to potential borrowers. This process relies on four primary factors: the borrower’s income, property appraisal, credit score, and the assets they possess. While loan underwriting is often automated, in cases like mortgages, a human, known as a loan underwriter, is required. Mortgage underwriting involves evaluating the borrower’s income, credit score, financial liabilities, and savings to make an informed lending decision. Typically, mortgage underwriting takes about a week to complete.

   How Automation Helps:
   Automated underwriting systems can instantly pull data from credit reports, salary slips, and bank statements to assess a borrower’s financial health. For instance, AI-powered systems can analyze a borrower’s debt-to-income ratio in seconds, significantly reducing the TAT from weeks to minutes. This ensures faster loan approvals without compromising accuracy.

   A leading bank implemented underwriting automation to streamline its mortgage approval process. The automation system reduced the average approval time from 10 days to just 2 days, improving customer satisfaction and loan disbursement rates.

2. Securities Underwriting

   Securities underwriting is commonly associated with Initial Public Offerings (IPOs) and serves to evaluate the risk and fair pricing of specific securities. Investment banks, predominantly, are the investors involved in securities underwriting to determine the profitability of an investment. The process starts with an investor selecting potentially profitable securities offered by a company going public. These securities are then sold to other market buyers, allowing the investor to make a profit.

   Underwriters involved in securities underwriting often form an underwriter syndicate, purchasing securities after assessing the risk and then reselling them to other investors or brokers who, in turn, sell them to buyers in the market. Securities underwriting is crucial as it ensures that an IPO raises the necessary capital and provides underwriters with a premium for their services.

   How Automation Helps:
   By integrating AI-driven underwriting and predictive analytics, financial institutions can evaluate the profitability of securities in real time. Automated systems analyze market trends, historical data, and company performance to determine the fair pricing and risks associated with the securities.

   An investment bank used predictive analytics in underwriting to assess IPOs for tech startups. The automated system helped forecast market demand and optimize pricing strategies, increasing the bank’s IPO success rate by 20%.

3. Insurance Underwriting

   Insurance underwriting is the process of evaluating potential policyholders for various types of insurance, including life, health, property, and more. In the past, health insurance, for example, involved a medical assessment to determine the premium based on an applicant’s health status. Insurance companies could also refuse coverage based on an individual’s health condition. Insurance underwriting helps assess the risk of policyholders filing frequent and substantial claims and calculates the extent of coverage to be provided. It’s important for gauging the risk and coverage percentage for insured individuals.

   How Automation Helps:
   Insurance underwriting automation uses intelligent document processing and AI to extract, verify, and analyze applicant data. This not only reduces errors but also accelerates the issuance of insurance policies. Additionally, AI algorithms predict risk based on historical claims data, enabling underwriters to make more informed decisions.

   A health insurance company adopted AI in underwriting to automate the medical assessment of applicants. The system analyzed medical histories, lifestyle factors, and demographic data, reducing policy issuance time by 40% while maintaining accuracy.

Automated Underwriting System for Quick Decision-Making

Underwriting automation addresses the inefficiencies of manual processes by leveraging AI-driven underwriting and automated underwriting systems. Let’s discuss what can be automated in insurance underwriting using automation and AI solutions.

1. Data Intake

   To assess credit risk, underwriters have to collect data like policy documents and loan details from multiple sources and formats. According to McKinsey, “anywhere from 30 to 40 percent of underwriting’s time is spent on administrative tasks, such as re-keying data or manually executing analyses.” And getting all the data in one place is a time-consuming exercise for underwriters.

   Here automation with intelligent document processing capability can handle this process and can do a lot better. Intelligent document processing in the underwriting process can easily extract data like loan and insurance documents and accumulate them in one system. From there, underwriters can check, analyze, and verify the data before making a decision.

   A global insurer can implement underwriting automation to process policy applications. The system extracts data from scanned documents, emails, and online forms, reducing data collection time by 70%.

2. Risk Assessment

   As per a report, analysts spend up to 40% of their time validating data relevant to their analysis before making any conclusions. Using an underwriting automation system reduces the need for humans to collect data, underwriters get a full picture of data in one place. This enables underwriters to analyze large volumes of data in real time and assess the risk factors like credit score, underwriting documents, and customer behavior to make accurate decision-making. Moreover, machine learning algorithms can be trained to get historical data that can further help underwrites predict future risks and losses.

   A financial institution can use AI-driven underwriting to assess credit risks for small business loans. The system evaluates customer profiles and flags high-risk applicants, reducing loan default rates over 15%.

3. Fraud Detection

   When there is a large volume of data to analyze and verify, human error is evident. With high-volume records, underwriters are often unable to detect fraud before it occurs. But that’s not the case with automation. Using machine learning and AI technologies with automation, underwriters can identify the inconsistency in the information provided and flag suspicious activities. This further reduces the chance of fraud in the business and saves the business from any financial losses.

   An insurer can use automation and AI to detect inconsistencies in claims data. The system flags suspicious activities, saving the company millions in potential fraudulent payouts.

4. Regulatory Compliance Management

   The underwriting process must be in sync with the regulatory compliance offered by the financial institution. Any errors or discrepancies lead to financial risk and process inefficiency. Also, performing this process manually leaves the chances of manual error. Here, an automated underwriting system can be programmed to adhere to regulatory requirements and compliance standards and reduce the risk of non-compliance and associated penalties.

   A bank when automates its underwriting process compiles with new lending regulations. The system ensures all loans met compliance standards, reducing the risk of audits and fines.

Conclusion

The shift to automated underwriting systems is no longer optional—it’s a strategic imperative for financial institutions seeking to thrive in a competitive market. By leveraging insurance underwriting automation and AI in underwriting, organizations can overcome traditional challenges, reduce costs, and enhance customer satisfaction. As financial institutions continue to adopt AI-driven underwriting, they are poised to unlock new levels of operational excellence and future-proof their businesses in an ever-evolving financial ecosystem.