Almost two-thirds (62%) of insurance executives think artificial intelligence and machine learning technology (AI/ML) elevate underwriting quality and reduce fraud, according to the Capgemini Research Institute’s World Property and Casualty Insurance Report 2024.
However, at the moment, just 8% are using AI-driven insights and automation to make informed decisions and accurate risk assessments with efficiency.
Underwriting is particularly well-suited to automation and AI, for several reasons that we’ll explore in this blog.
Read more: 5 ways underwriters can use AI
Why is AI so useful in underwriting?
1. High volume of data processing
Traditional underwriting relies heavily on manual data entry, where underwriters sift through multiple, complex documents like medical records and financial statements. This process is tedious and time-consuming, often leading to errors and delays.
AI automates data extraction from unstructured documents, allowing underwriters to process vast amounts of information quickly. This efficiency reduces the chances of human error and frees up underwriters to focus on analysis and decision-making, significantly speeding up the underwriting process.
2. Complex decision-making
Underwriters manually analyse risk factors and apply business rules based on their experience and judgement. This can lead to inconsistencies, as decisions may vary depending on individual interpretations of data.
AI-driven analytics provide data-backed insights, applying predefined business logic consistently across applications. This ensures that underwriters make informed, uniform decisions based on comprehensive analyses of risk, boosting accuracy and reliability.
3. Improved accuracy and consistency
Manual processes are prone to human error, and inconsistencies in data entry can affect the outcome of risk assessments. This may result in inaccurate pricing and potential losses for the insurer.
AI algorithms cross-reference multiple data sources, validating information to ensure accuracy. This consistency reduces the risk of errors in underwriting decisions, leading to more reliable pricing models and better financial outcomes for the insurer.
4. Enhanced fraud detection
Identifying fraudulent applications manually relies on underwriters’ instincts or lengthy cross-referencing, which can be labour-intensive and ineffective at spotting subtle signs of fraud.
AI analyses patterns in claims data to detect anomalies and flag suspicious activities. By automating this process, underwriters can focus on high-risk cases that need further investigation, reducing losses from fraudulent claims down the line.
5. Lots of repetitive, routine tasks
The underwriting process often involves repetitive, time-consuming tasks, such as data validation and application triage. In fact, the average underwriter spends 40% of their time on administrative tasks, according to Accenture. Underwriters may spend excessive time on administrative duties, slowing down the overall process.
Automation of routine tasks allows for efficient workflow management. AI can handle initial application reviews and data validation, enabling underwriters to concentrate on more complex cases, improving productivity and reducing processing times.
6. Scalability
As application volumes increase, traditional underwriting processes can become overwhelmed, leading to bottlenecks and delayed responses. This limits an insurer’s ability to scale operations effectively.
AI systems can easily scale to accommodate rising application volumes without sacrificing speed or accuracy. This flexibility allows insurers to adapt to market demands and maintain high service levels, even during busy periods.
7. Potential for better customer experience
According to Capgemini, 42% of policyholders find current underwriting processes complex and lengthy. Customers often experience long wait times for policy approvals due to manual processes, which can lead to frustration and dissatisfaction.
Automated underwriting significantly reduces turnaround times for policy issuance. Faster responses and personalised service, based on customer data, enhance the overall experience and foster stronger client relationships.
8. An evolving area of insurance
Traditional underwriting relies on static processes that do not adapt to new data or evolving market conditions. This can result in outdated practices and missed opportunities for optimisation.
AI systems can learn from historical data and past decisions, continually refining their algorithms. This allows underwriting models to evolve over time, improving risk assessment and enhancing the quality of decision-making.
Read more: How can AI enhance underwriting?
How Sprout.ai streamlines underwriting
After years of supporting claims handlers, Sprout.ai is now available for underwriting too. It works behind the scenes to not only automate routine underwriting tasks but also equip underwriters with actionable insights, leading to more informed decisions and a more efficient underwriting process.
It is already being used for underwriting by several leading insurers around the world.
Here’s how it transforms each stage of underwriting – although it can be used for just some aspects if you prefer.
1. Broker submits application
The process begins when a broker submits an application, which typically involves gathering and handling numerous documents and data entries. Traditionally, this step is labour-intensive, requiring lots of manual effort. With Sprout.ai, the process is simplified and accelerated, as it sorts the documents for you.
2. Data enrichment
Sprout.ai extracts and validates information from the submitted documents. It processes emails to retrieve relevant data and attachments, ensuring all critical information from documents like identification, medical records, or financial statements is accurately captured and verified. The system further enriches the application with external data, such as credit checks, to build a comprehensive applicant profile.
3. Decision recommendations
Following predefined business logic and rules set by the insurer, Sprout.ai evaluates the application by cross-referencing internal and external data sources. The system applies business rules to provide a recommendation on whether to accept, reject, or request further review of the application, aligning with the insurer’s guidelines and risk appetite.
4. Risk assessment
Sprout.ai improves risk assessment by examining incidents, coverage details, and engaging vendors when necessary. This thorough inspection helps accurately determine the risk associated with the application, providing underwriters with a clear understanding of potential liabilities.
5. Underwriter takes action
With the insights and recommendations from Sprout.ai, the underwriter makes the final decision. Armed with comprehensive data and analysis, the underwriter can decide whether to accept, modify, or decline the application, reducing turnaround time for policy issuance.
Getting started with Sprout.ai for underwriting
Integrating Sprout.ai into your underwriting process is simple and tailored to your team’s way of working.
Here’s how it works:
Initial consultation
We assess your underwriting challenges and goals to identify where Sprout.ai can add the most value.
Proof of Concept (POC)
We deploy Sprout.ai on a small scale to showcase its capabilities and demonstrate how it can streamline workflows and improve accuracy.
Implementation and integration
After a successful POC, we integrate Sprout.ai into your systems. Our team ensures a smooth transition with minimal learning required from your staff, providing training and support as needed.
Ongoing support
We monitor and optimise Sprout.ai to ensure continued performance. As your needs evolve, we can expand its use across your underwriting and other areas.
Underwriting has a major impact on profitability and customer satisfaction. Automation and AI can help insurers meet growing pressure to process applications quickly and accurately. The results? Smoother workflows, higher customer satisfaction, and better financial outcomes.
