In the insurance industry, underwriting is one of the most intricate processes, both from a logic and data input perspective, to the underwriters who determine whether or not a risk is worth taking. Also in the last few years manual insurance underwriting has suffered multiple setbacks right from higher turnaround time, fragmented customer journey, high cost of operations, and lowered decision making.
That’s the reason insurers are now looking for competitive benefits technologies and tools that can accelerate the insurance underwriting process. In our earlier set of blogs, we discussed what is underwriting automation and the types of underwriting to automate like loan underwriting, insurance underwriting, and security underwriting. Let’s explore more about automated insurance underwriting in detail in this blog and how it can benefit underwriters and insurers.
Manual Insurance Underwriting & Challenges
Insurance underwriting is the process of evaluating the risk associated with insuring someone or something and setting the price (premium) for that coverage. It’s like a balancing act between protecting the insurer from excessive losses and providing fair coverage to the insured.
Here’s a simplified breakdown:
What underwriters do:
- Analyze data (medical history, driving record, property details, etc.)
- Assess potential risks (accidents, illness, theft, etc.)
- Calculate the likelihood and cost of claims
- Determine the appropriate premium
- Decide whether to accept or reject the application
According to a report, only about 25% of submissions insurance companies receive turning into written policies. Some 60% never get reviewed as insurers simply lack sufficient capacity. Additionally, insurers spend a lot of time performing underwriting processes like data extraction from diverse resources including policyholders’ records and claims data. As per McKinsey, underwriters spend 30-40% of their time on administrative tasks leading to delayed decisions.
Despite the automation of technologies, multiple insurers are unable to scale the insurance underwriting process, and technology has made no changes for them.
Also, human underwriters, while skilled, may face limitations in consistently assessing risk factors across diverse portfolios. And, as insurance fraud costs insurers USD 6 billion annually, risk assessment becomes a crucial factor to take care of. With these constant challenges insurance underwriting requires a solution that can take care of the process and enable insurers to focus on decision making.
Automated vs Manual Insurance Underwriting
Automated insurance underwriting utilizes advanced algorithms and software to swiftly analyze a client’s financial or health history, completing the process efficiently. In contrast, manual underwriting represents a significantly slower approach, relying on human assessment and thereby risking potential errors.
Manual insurance underwriting involves a meticulous collection of paperwork including policy documents, tax returns, proof of employment, medical history, and demographic profiles. Once the client furnishes this information, the underwriter embarks on assessing the potential risk associated with providing insurance coverage. Automated insurance underwriting reduces the time and resources required for evaluating applications, enabling insurers to offer quicker turnaround times and improve operational efficiency.
How does Automated Insurance Underwriting Help?
Automated insurance underwriting utilizes technologies like RPA and AI to streamline the insurance process and enable insurers to take care of the decision-making process. Some of the ways automation solutions help insurers are-
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Data Intake
The insurance underwriting process requires a large volume of documents to extract and integrate into the system for a faster decision-making process. While manually this process is time-consuming but leveraging technologies like machine learning and artificial intelligence, insurers can process applications and documents instantly and accurately. This includes structured data (such as demographic information, financial records, and claims history) as well as unstructured data (such as text documents, emails, and social media posts). AI-powered algorithms play a crucial role in data intake by automatically extracting valuable insights from these diverse data sources.
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Task Management
AI-driven task management systems help underwriters prioritize and manage their workload more effectively. These systems use algorithms to allocate tasks based on factors such as urgency, complexity, and resource availability. Automated workflows facilitate collaboration among team members, enabling seamless communication and coordination.
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Risk Assessment
This is one of the most crucial processes for underwriters. With automated insurance underwriting, insurers can analyze historical data related to insurance claims, demographics, and other relevant factors to identify patterns and correlations. With AI predictive models insurers can use historical data to forecast future trends and assess the likelihood of specific events, such as accidents or illnesses. Also, these AI algorithms assign risk scores to individual policyholders based on factors such as age, health status, and occupation, helping underwriters quantify and mitigate potential risks.
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Policy Management
Automated policy management systems in insurance underwriting streamline the issuance, renewal, and administration of insurance policies, enhancing the overall customer experience and improving operational efficiency. These systems provide policyholders with convenient digital platforms to access information, make changes, and file claims. Also, AI chatbots enable insurers to automate renewal reminders and notifications, ensuring that policies are renewed promptly and accurately.
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Compliance Management
AI-driven compliance solutions help insurers automate regulatory checks and audits, ensuring adherence to legal and industry standards. These solutions leverage real-time monitoring tools to alert underwriters to potential compliance issues and facilitate timely remediation. Also, AI-powered monitoring tools track changes in regulations and alert underwriters to emerging compliance risks, enabling proactive risk management and mitigation for insurance underwriting.
