Assessing risk and deciding whether to offer coverage to a potential policyholder can be a time-consuming and labour-intensive process. Complex products or applications involving unstructured data like medical reports or financial documents are a particular challenge for busy underwriting teams.
This is where Sprout.ai comes in. We’ve spent years transforming claims processing with advanced document processing and data extraction technology. Now, we are applying this expertise to the underwriting process.
By automating the extraction and structuring of data from underwriting documents, Sprout.ai can help insurers streamline their underwriting operations, reduce manual workload, and make more informed decisions faster.
In this blog, we will explain how underwriters can use Sprout.ai, explore some real-world examples and highlight the benefits it offers to insurers looking to enhance their underwriting efficiency and accuracy.
Read more: Art of the possible: How Sprout.ai transforms claims
How can underwriters use Sprout.ai?
When they’re working manually, underwriters need to sift through vast amounts of unstructured data, such as medical records, financial statements, or identity documents. This can be a laborious task that introduces the potential for human error.
- Data collection: Sprout.ai streamlines data collection by integrating with insurers’ systems to efficiently gather customer information.
- Data extraction: Sprout.ai extracts essential details from unstructured data, like medical or business records.
- Decision support: Sprout.ai helps underwriters make faster and more accurate decisions by highlighting key information and risks.
Sprout.ai works behind the scenes, streamlining the underwriting process by automating time-consuming tasks and providing intelligent insights to underwriters.
Here’s how that works.
1. Broker submits application
When a broker submits an application, the process starts with the collection of necessary documents and data from the applicant. Traditionally, this can involve a lot of manual data entry and handling of various forms and documents. You’ll see in the next step that Sprout.ai makes this much easier.
2. Data enrichment
In this step, Sprout.ai extracts and validates information from the submitted documents.
Sprout.ai can also process emails to extract relevant data and attachments that are part of the application process. It extracts critical information from various documents, such as identification, medical records, or financial statements, and validates the data to ensure accuracy and consistency.
The application is then enriched with external data, such as credit checks or other third-party information, which helps in building a comprehensive profile of the applicant.
3. Decision recommendations
Sprout.ai applies a philosophy-driven approach to decision-making, which means it uses predefined business logic and rules set by the insurer to assess the application.
The system automatically validates the information against internal and external data sources to cross-check the accuracy and consistency of the application.
Based on the insurer’s underwriting guidelines and risk appetite, Sprout.ai executes business logic to provide a recommendation on whether to accept, reject, or further review the application.
4. Risk assessment
Sprout.ai enhances risk assessment by thoroughly inspecting incidents, checking coverage, and involving vendors when necessary. This helps accurately determine the risk associated with an application, ensuring that underwriters have a clear understanding of potential liabilities.
5. Underwriter takes action
After Sprout.ai has processed the application and provided its insights, the underwriter steps in to make the final decision.
Based on the information and recommendations provided by Sprout.ai, the underwriter can decide to accept, modify, or decline the application.
With all the data at their fingertips, underwriters can make more informed decisions quickly, reducing the turnaround time for policy issuance.
Read more: How Sprout.ai uses AI, and what sets it apart from other AI tools
Real-world applications of AI in underwriting
Sprout.ai is already being used for underwriting by several leading insurers.
Case study 1: Large European insurer
A major European insurance provider faced challenges underwriting their life insurance policies, which often required reviewing detailed medical reports. Traditionally, underwriters would manually read through these reports, which could range from 10 to 100 pages, to assess the risk associated with a potential policyholder. This process was time-consuming and required underwriters to have a deep understanding of medical terminology.
We worked with this insurer to implement an automated solution that extracts relevant medical information from these reports. By doing so, the system provides underwriters with a summarised view of the applicant’s health status, highlighting any critical issues that might impact the insurance decision. This has the potential to save up to 45 minutes per case, allowing underwriters to focus on more complex decision-making rather than administrative tasks.
Case study 2: Leading Southeast Asian insurer
An insurance company in Southeast Asia needed in-depth verification of applicants’ information. For new business applications, particularly in life insurance, the company needed to validate various documents such as identity proofs, payslips, and affidavits. This process involved a lot of manual checking and data entry, which was both error-prone and inefficient.
We helped this insurer automate the process by using AI to verify the consistency and accuracy of submitted documents. For instance, the system can check if the name on a payslip matches the applicant’s ID or if the stated income aligns with the provided financial documents. This reduces the burden on underwriters and speeds up the entire application process, enabling quicker decision-making and reducing operational costs.
Benefits of using AI in underwriting
By integrating Sprout.ai into existing workflows, insurers can benefit in multiple ways:
1. Increased efficiency
Sprout.ai significantly reduces the time required to process underwriting applications by automating the extraction and analysis of data from various documents. This means underwriters can review more cases in less time, increasing overall productivity and allowing them to focus on more complex decisions.
2. Reduced costs
With less manual intervention needed, insurers can lower their operational costs. By automating repetitive tasks, such as data entry and document verification, Sprout.ai helps to minimise the need for large underwriting teams, which can result in significant savings.
3. Improved accuracy
Human error is an inherent risk in manual data processing. Sprout.ai’s advanced AI algorithms reduce these errors by ensuring that data is accurately extracted and structured from unstructured documents. This leads to more reliable underwriting decisions and a better risk assessment.
4. Scalability
Sprout.ai is designed to handle a wide range of data sets and document types, making it easily scalable to different types of insurance products and markets. Whether dealing with straightforward applications or complex cases requiring detailed document review, our system can adapt to meet the needs of any insurer.
5. Better customer experience
By speeding up the underwriting process and improving accuracy, insurers can offer a better experience to their customers. Faster decisions mean shorter waiting times for policyholders, and more accurate assessments reduce the likelihood of disputes or claim rejections in the future.
Getting started with Sprout.ai for underwriting
Integrating Sprout.ai into your underwriting process is straightforward and can be tailored to fit your specific needs.
Here’s what would happen if you started using AI in your underwriting process with us.
1. Initial consultation and needs assessment
The first step involves a consultation to understand the specific challenges and goals of your underwriting process. Our team will work closely with you to identify areas where automation and AI can deliver the most value.
2. Proof of Concept (POC)
During the POC, we implement Sprout.ai on a small scale to demonstrate its capabilities in a real-world setting. This allows you to see firsthand how our technology can streamline your underwriting process, improve accuracy, and save time.
3. Implementation and integration
Once the POC is successful, we move to implementation. Our team will work with your IT department to integrate Sprout.ai into your existing systems and ensure a seamless transition. We provide training and support to help your staff get up to speed with the new technology, but there’s very little they will need to learn as Sprout operates behind the scenes.
4. Ongoing support
After the initial implementation, we continue to monitor and optimise the system to ensure it delivers the best results. As your needs evolve, Sprout.ai can be scaled to cover additional underwriting areas or expanded to other parts of your organisation.
Sprout.ai can streamline underwriting operations, reduce manual workload, and support faster, more informed decision-making. It’s not designed to replace underwriters, but rather help them apply their skills and expertise more effectively.
