Artificial Intelligence (AI) is transforming ways of working across the insurance industry, and underwriting is no exception.
Here are five ways underwriters can use AI to boost efficiency and accuracy. They’ll have more time to focus on complex cases where their expertise is best used, and be able to respond to customers more quickly.
1. Automating data extraction and enrichment
According to Capgemini, 42% of policyholders find current underwriting processes complex and lengthy. AI can streamline the extraction of relevant data from unstructured documents. By using machine learning models to process and analyse documents like medical reports, financial statements, and much more, underwriters can save hours on each case. This reduces manual data entry errors and accelerates the overall underwriting process.
Example: A life underwriter was manually sifting through hundreds of pages of medical records, which was time-consuming and prone to error. She starts using AI to automate the extraction of data from complex medical records. AI extracts relevant details such as medical history and previous treatments from lengthy reports, allowing her to quickly assess the applicant’s risk profile without manually reviewing each document.
2. Enhancing risk assessment
AI can analyse vast amounts of historical data to identify patterns and predict risks more quickly and accurately. This helps underwriters evaluate risk factors more comprehensively, leading to more precise pricing and risk assessment for policies.
Example: An auto insurance underwriter relied on limited historical data and manual calculations, which often led to less accurate risk predictions and inconsistent policy pricing. He starts using AI to refine risk assessment. The AI assesses customer data to deliver a risk score based on historical accident data and driving behaviour analysis, which helps him make more informed decisions about policy approvals and premium rates.
3. Improving fraud detection
AI can detect anomalies and inconsistencies in data that may indicate fraudulent activity. Machine learning models can flag suspicious claims or inconsistencies in personal information, helping underwriters to focus on high-risk cases and minimise potential losses due to fraud or abuse.
Example: An underwriter manually reviewed historic claims for fraud, which was labour-intensive and they often missed subtle signs. She starts using AI to analyse patterns in claims data to flag suspicious activities, such as unusual claim frequencies or discrepancies in information. She then reviews these flagged cases in detail to confirm their legitimacy.
4. Optimising decision-making
This year 70% of insurance firms said inconsistent underwriting decisions are a prevailing issue, according to The World Property & Casualty Insurance Report 2024. AI-driven analytics can support decision making by providing insights based on data trends and predictive models. This helps underwriters make informed decisions more quickly, improving the efficiency of the underwriting process and the accuracy of policy issuance.
Example: A commercial insurance underwriter relied on manual data analysis and intuition, leading to slower decision-making and occasionally inconsistent policy terms. He starts using AI-driven decision support tools. The AI provides him with recommendations based on data trends and predictive analytics, helping him make faster and more accurate decisions on policy terms and conditions.
5. Streamlining workflows
The average underwriter spends 40% of their time on administrative tasks, according to Accenture. AI can automate routine tasks and manage workflow processes. For example, AI can handle the initial triage of applications, route cases to appropriate underwriters, and track the progress of each case, freeing up underwriters to focus on more complex tasks, and deliver quotes more quickly.
Example: An underwriter for a health insurer manually managed case routing and tracking, leading to delays and a higher administrative burden. She integrates AI to streamline her workflow. The AI system manages the intake of new applications, routes them to her based on complexity, and tracks the progress of each case, which helps her focus on higher-value tasks, reduces administrative overhead, and enables simple applications to be processed in close to real time.
Read more: How can AI enhance underwriting?
How Sprout.ai streamlines underwriting
Sprout.ai 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 a step-by-step look at how it transforms underwriting.
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 swiftly 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 your process 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.
From automating data extraction to improving risk assessment and fraud detection, AI tools like Sprout.ai simplify processes and provide actionable insights. By integrating AI, underwriters can streamline their workflows, make faster decisions, and ultimately deliver better service to their customers.
