The necessity for insurance companies to appropriately deploy their underwriting resources is becoming more apparent. Any insurance company can boost its efficiency and profitability by simply leveraging relevant technologies to ease out the overburdened, risk-prone processes. Robotic Process Automation (RPA) may help with underwriting leakage by allowing underwriting resources to be used more efficiently.
Let’s investigate the possibilities through this blog.
Underwriting – What is it, and how it works?
Underwriting is the procedure by which a person or a company accepts financial risk in exchange for a fee. Most of the time, this risk is associated with loans, insurance, or investments. The word “underwriter” comes from the practice of having each risk-taker sign their name under the entire amount of risk they were ready to assume for a given premium.
Even though this mechanism has evolved, Underwriting plays a critical role in the financial industry. The entire Underwriting process is based on the risk related to the borrower returning the loan on time or default.
Underwriters assess loans, particularly mortgages, to gauge the possibility of a borrower paying as agreed and the availability of sufficient collateral in the case of default.
In addition, underwriters look at a policyholder’s health and other criteria to distribute the risk as widely as feasible in insurance. Underwriting securities, which are most commonly done through initial public offerings (IPOs), aids in determining a company’s underlying value compared to the risk of funding its IPO.
What are the Problems with Manual Processing?
The lending sector is shifting to new technology-driven loan underwriting systems to optimize the processing time for all sorts of loans.
In the financial sector, Underwriting is an integral part of risk management. The underwriter’s vetting inspection procedure identifies applicants who pose an unacceptable risk and inspects the policyholder’s trustworthiness to decide the premium for providing coverage.
When an underwriter cannot forecast risk and takes too long to underwrite a policy, underwriting leakage develops, resulting in a delayed turnaround time.
Furthermore, manual underwriting incurs a high cost for physical survey and inspection in the event of low-risk policies, increasing expenses more extraordinary than the premium collected. This results in financial loss and inefficient processes.
The amount and type of data accessible for analysis and the technologies used to combine and analyze it are all evolving. Robotic process automation comes into play to seamlessly fix these gaps.
Data may enter in several formats, including emails, photographs, video, and PDF files, as well as from various separate systems and external sources, along the process. As a result, manual risk filing is time-consuming and error-prone, with information about the insured potentially falling through the cracks due to the vast volume of data that must supply.
According to Accenture, the average carrier underwriter spends more than half of their day on basic processing and repetitive duties rather than higher-value work.
The number of hours actuaries spend evaluating and analyzing data to establish premium pricing is also affected by manual processing. Slow, error-prone processing hurts not just the insurance carrier’s cost-efficiency and productivity but also brokers and businesses looking to get coverage quickly.
The traditional lending business is increasingly shifting its focus to automated credit underwriting, which reduces client wait times and helps banks remain competitive and improve customer experience. Therefore, the rate at which the market for Global Lending Software has grown might be used to gauge its expansion. The market was worth USD 2,615.8 million in 2017 and is expected to reach USD 5,579.4 million by 2024, with a CAGR of 11.6 percent over that time.
According to the US Bureau of Labor Statistics, the number of underwriting employment in all industries would decrease by 6% between 2012 and 2022, from 106,300 to 99,800.
Overburdening insurance underwriters with both low- and high-risk jobs may result in human errors resulting from stress. So, organizations will be unable to achieve optimum productivity since they will fail to make the most use of time and trained underwriting resources.
Automated Underwriting vs. Manual Underwriting
Robotic process automation can help to improve the underwriting process by entirely automating low-risk, repetitive operations and partially automating high-risk tasks.
However, according to a report, underwriters are still required to perform their decision-making function and assess intelligence guidelines for high-risk assignments, resulting in a reduction of underwriting positions to 6%, rather than 60% or even higher.
Automated underwriting system types are broadly classified in practically all forms of personal and small company loans, based on the in-depth evaluation of the disadvantages of manual processing.
However, it is most commonly used in traditional loan processes with standardized underwriting procedures and a basic amortization schedule for installment payments. But this is only the beginning.
Startups are increasingly focusing on the corporate loan market and other complex fixed income products that were previously only available through face-to-face “relationship” banking. So, the need to automate underwriting process is a mandate to match up the required pace of digitization.
To understand the underwriting technology elaborately, consider a scenario in which RPA is used to underwrite a policy in the insurance industry.
After analyzing the procedure, it is classified as either low risk or high risk. By decreasing repetitious work and increasing the number of policies issued, fully automating the underwriting process in low-risk guidelines will outperform human underwriters for a more significant percentage of policies.
As a result, robotic process automation improves client happiness while also enhancing the insurance company’s reputation.
Furthermore, in auto underwriting, the knowledge database will use deep learning to utilize and continually auto-update data in a single repository. This provides insight into the policy risk to make swift premium and coverage decisions in the future.
Even for high-risk activities, automation may be used to allocate underwriting resources by employing intelligence algorithms that determine which sorts of risk cases can be assigned to each underwriting step based on their skill set.
Furthermore, the knowledge database delivers more incredible exposure suggestions. This reduces underwriter stress and allows them to focus on more complicated decision-making.
Risk Assessment without Automation is Complex
Risk assessment can necessitate substantial data sourcing, data collection, and data processing. This is when automation comes in handy. However, “The insurance business has historically lagged behind its financial services rivals in the exploration and use of automation,” according to a PWC survey
Commercial insurance lags behind personal insurance in terms of automating procedures, owing to the variety and complexity of risks that must be evaluated, as well as the enormous amount of data collected.
A commercial insurance policy is typically started by a company contacting a broker or agent for coverage.
First, the company specifies the type of insurance it needs. The insurance representative then sends back a questionnaire inquiring about the nature of the business, including the products and services offered, the number of employees, equipment, inventory, building construction, and location.
It’s sometimes a back-and-forth procedure as the agent tries to acquire as much information and paperwork as possible to get a complete picture of the potential insured. Finally, this information is forwarded to the underwriting team at the insurance carriers, who will assess the risk and price the premium if the application is granted.
So, the risk is massive, and larger is the chances of manual error – automation is undoubtedly the savior in this situation!
Intelligent Automation is the Answer
The insurance business, both commercial and personal, now has the opportunity to take advantage of digital transformation thanks to intelligent automation that combines Robotic Process Automation (RPA) and artificial intelligence (AI).
Insurance underwriting can be streamlined in a variety of ways with intelligent automation software bots. They can:
- Data collection and organization of various sorts of data from a broader range of internal and external sources
- Information can be extracted from unstructured data sources like emails and PDF files.
- Automate the entire risk submission process.
Most insurance companies have an excessive amount of data to process. Bots can access, organize, and analyze data to deliver better, more detailed decision-making insights.
Bots can also help insurance companies run more efficiently by automating monotonous tasks and increasing underwriter and actuary efficiency. In addition, they can free up underwriters to collaborate with brokers and other agents in real-time to improve service and speed up insurance placement.
Anyone in the insurance industry can benefit from intelligent automation by having access to a digital assistant that works with them in real-time to handle procedures swiftly and effectively through a simple interface.
And that’s just the beginning of what clever automation can do to improve insurance underwriting and other processes.
How Does Automated Underwriting Work for Various Types of Underwriters
Underwriter of Insurance: Underwriters evaluate the risk of insuring a home, a car, or a driver. They also assess those who apply for life insurance plans. Insurance underwriters are insurance experts, being aware of the dangers and the associated preventive measures. They utilize their risk assessment to determine whether or not to cover someone and, if so, under what terms.
Underwriting is done by an automated method in cases where there are no particular circumstances. A quotation system is comparable to underwriting programming. It can tell if a potential customer fulfills the insurer’s specific coverage requirements.
Underwriter for Mortgages: Mortgage underwriters conduct a detailed risk evaluation in matters of purchasing a home. Following the assessment, the underwriter can determine whether the loan is a feasible project for the applicant, based on his credit score and history and proof of consistent income, debt-to-income ratio, overall savings, and other risk factors.
However, the procedure can be lengthy and often necessitates the overturning of a substantial quantity of evidence. Automation simplifies this process by effectively managing the document scanning process to help the underwriter make the right choice.
Underwriter for a Loan: Loan underwriters assess the risk of providing an applicant a loan, such as an auto loan. The goal is to see if the loan is safe for all parties involved. For assessment, large banks frequently employ a combination of underwriters and underwriting software. A frequent technique among large and small banks is to use a variety of software and an underwriter for accurate results and quicker responses.
Underwriter of Securities: The initial public offerings (IPOs) are frequently handled by securities underwriters as they assess the investment’s risk to determine an IPO’s acceptable price.
A securities underwriter is usually a specialist or an employee of an investment bank. The sales phase is one of the most dangerous aspects of securities underwriting. If a security does not sell for the advised price, the investment bank is responsible for the difference. And, automation helps with such crucial calculations to ensure accuracy.
Automated Credit Underwriting’s Advantages
The following are some critical strategies to support and ease the underwriting process:
- Increasing Productivity
It’s a win-win situation for both lenders and borrowers. The automated credit underwriting approach saves time for both parties, allowing for faster decision-making and fewer steps than traditional Underwriting.
The automated Underwriting also ensures that the borrower’s desire for more rapid processing is addressed while maintaining accuracy that does not jeopardize the lender’s balance sheet.
- More Effective Decision-Making
Algorithms are incapable of making clerical errors. As a result, a human, no matter how efficient, might have a bad day, costing a lender millions of dollars in non-performing loans. However, the automated algorithms are becoming better at forecasting which loans will do better because of machine learning capabilities and increased data on such loans.
- Fraud Detection that is More Intelligent
Loan fraud is on the rise. In and of itself, credit card fraud is a multibillion-dollar business. However, fraud risk is significantly and systematically reduced by automation. How? The robotic operations use powerful predictive analytics to quickly identify hazards associated with disbursing a loan to a customer. Wherever a mismatch is placed, these systems raise red lights, allowing for more accurate fraud detection.
- A customer-centric experience
Loan paperwork may be merely a back-office procedure. However, it’s vital to make sure that all of the rules are followed. With the Government imposing strict measures for banking institutions and charging hefty fines on erring institutions, there are zero scopes of error.
Hence, underwriting and loan disbursement documentation are automated, ensuring a seamless solution for the bank.
- Underwriting Consistency
The bank’s capacity to underwrite, approve, and document credit in a more personalized manner while remaining compliant with the bank’s standards is enhanced by automation. It compensates for the bank staff’s inability to interpret bank policies, which may differ from one employee to the next.
Furthermore, automation considers all loan-risk elements related to loan policies, which may overlook traditional Underwriting yet critical to a loan decision.
- Compliance with Regulatory Standards is Easier and More Effective
The best aspect about automation is that you may update a rule, and it will be applied across the board based on the filters you specify. As a result, regulatory requirements that may be neglected in a traditional system are always checked in automated methods, resulting in improved compliance.
- Improved Auditing
Automation of documenting processes simplifies the documenting and lending operations, allowing for quicker and less time-consuming audits. This is beneficial to traditional banks and credit unions since it improves accuracy and reduces fraud.
- Workflows That are More Consistent and Well-Defined
To put it another way, automation operates through a specified process that makes Underwriting well-defined, resulting in a more consistent and efficient operation. As a result, it overcomes and slashes out all weaknesses of legacy banking systems.
The introduction of computerized credit underwriting has transformed the credit underwriting process yet more, with online lending undermining the traditional lending sector. While automated Underwriting began with loan startups, it has since found its way into the established banking sector.
However, credit underwriting automation does not undermine the importance of humans in determining creditworthiness. On the contrary, it has improved since analysts now have access to powerful algorithms that handle pattern detection and repetitious jobs.
Even more crucially, it’s becoming hard to serve clients if the competitors make a credit decision in five minutes while it does not take more than five days to make an offer. An enormous moat will be required – improved service or even lower interest rates will not suffice. So, it’s time banks prepare for the new banking era, powered by automation and RPA!
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