AI in Underwriting: Revolutionizing the Insurance Industry

Data reigns supreme in today’s world, and the insurance industry is no exception. The question is, can insurance effectively align with the power of data? Although the commercial insurance industry’s early days might have seen premiums calculated over informal conversations, the field has evolved significantly. The industry adopted advanced analytical techniques, from statistics to generalized linear models. Now, AI in underwriting stands as a transformative force. Let’s explore the real potential behind this technological advancement.

AI and machine learning (ML) tools unveil valuable insights from previously underutilized data, including unstructured formats like text, speech, and images. This access offers the potential to leverage additional data during underwriting—data that remains inaccessible or extremely difficult to obtain through traditional methods.

Challenges of Manual Underwriting: The Need for AI

Why should insurance companies embrace AI in underwriting? To understand the value, let’s examine the difficulties presented by manual underwriting, which AI and ML models help overcome:

1. Lengthy Processes: While manual underwriting offers what some consider a personalized touch, the process is time-consuming. The speed and accuracy of manual underwriting cannot match the speed and reliability of commercial underwriting with AI.
2. Increased Complexity: Time-consuming, manual form-filling processes, which include intricate fine print, underwriting errors and omissions, extended return times, increased premiums, lack of customization, and predictive services, often drive customers away from manual underwriting.
3. Decreased Efficiency: Manually assessing risk variables with each new application becomes complex. Processing complexity is particularly taxing on resources, making the process one of the most intensive within insurance. These factors adversely affect organizational efficiency and productivity.
4. Inefficient Pricing: Manual underwriting is riddled with price inefficiencies, quality issues, and potential procedural errors, making it difficult to create accurate risk profiles or determine risk levels accurately for each individual. These factors highlight the challenges associated with manual underwriting.

Insurance organizations are shifting toward AI-driven underwriting to improve efficiency.

Advantages of AI in Underwriting

What specific benefits does AI confer on the process?

1. Minimizing Human Error: Humans are inherently fallible, and mistakes can be made. AI in insurance consolidates vast, varied datasets, which reduces the likelihood of errors. After applying established identification models, human underwriters analyze the results and make informed decisions based on the data. AI is designed to learn from its previous errors. AI in underwriting saves both time and resources and is more efficient and scalable.
2. Improved Risk Understanding: Best practices in insurance underwriting increasingly involve incorporating various data sources. These data sources can be expanded and enhanced through AI, which improves risk evaluations. Insurance companies have already achieved positive results by adopting initiatives like predictive analytics models, big data, and machine learning in their departments. These tools reduce time-consuming due diligence procedures.
3. Cyber Threat Combat: Cyber threats are increasing with the adoption of cloud-based platforms and the increase in cyberattacks. For insurers, staying ahead is an ongoing challenge. Machine-learning-based fraud detection systems can stay ahead of emerging dangers and anticipate new cybersecurity dangers before they materialize.
4. Improved Customer Loyalty: Insurers enhance customer experience with the help of AI in underwriting from the outset, helping foster client loyalty. By automating less complex tasks, insurance companies allocate resources to develop long-term retention strategies based on individual account servicing, creating lucrative pricing models based on risk-sharing, and using effective loss control tactics. This frees up human underwriters to handle complex customer interactions. Leading commercial insurers are already enhancing their underwriters’ skills to take on more high-value responsibilities. They are also implementing AI-based platforms to streamline underwriting and post-sales services.
5. New Business Acquisition Opportunities: AI in underwriting integrates with larger insurance value chains. Insights from centralized data lakes enable cross-platform visibility and generate new cross-sell opportunities. This enables insurers to create a better customer journey. Integrated AI-driven systems allow underwriters to proactively engage clients with tailored plans prior to application submission. For instance, NLP-powered chatbots provide underwriters with a complete view of the customer’s journey. This,in turn, allows underwriters to evaluate various customer concerns.
6. Fairer Pricing: According to McKinsey, coverage amounts obtained by a small business owner for commercial property and casualty insurance varied by a significant 233%, suggesting price inefficiencies in place. Automated underwriting provides better risk visibility. Underwriters then act as knowledgeable, in-house guides, recommending the best pricing options and coverage terms.
7. Increased Profitability: AI in underwriting contributes to profitability by helping underwriters create lower loss ratios, generate better conversion rates, and eventually optimize resource use. As a result, insurers must leverage automated insurance for high-impact reforms to preserve profitability. Companies can use an AI-assisted underwriting transformation roadmap to reduce expense ratios and improve employee experience.

Roadmap to Integrate AI in Insurance Underwriting

The roadmap for integrating AI in underwriting involves:

1. Regulate and Digitize the Underwriting Process: Start with process audits to find bottlenecks. Adjust as needed by getting feedback and adjusting the process. Standardize procedures, lay down SOPs, and create metrics. Digitize paper-based processes.
2. Automate Manual and Repetitive Activities with AI: Here, AI aids humans in data collection, calculation, and presentation. Underwriters can enhance their underwriting judgments based on these inputs.
3. Apply AI in More Evolved Processes: In this more advanced stage, AI leads the underwriting process, carrying out research and analysis to make contextually informed judgments. Human underwriters act as supervising agents that check for quality assurance and process compliance.

AI in Underwriting Automation Journey

Insurance companies modernize underwriting from fully manual to different automated aspects. Commercial insurers can use AI in underwriting as part of a comprehensive solution. There are three main stages in the automation journey:

1. Prefill: AI in underwriting helps data population. Algorithms help insurance companies prefill application data for minor commercial risks by mining many data sources, including unstructured data. This provides relevant classification suggestions and risk characteristics that impact premium and claim costs. Then, human underwriters can evaluate this information more easily.
2. Selective Automation: Some insurance sectors are selected for automated underwriting based on the insurer’s risk appetite. In this case, prefilled application data is compared to the insurer’s underwriting standards to decide whether to accept the application or require more information. The insurer’s logic is used to apply credits/debits, and quotes are generated.
3. Full-blown Automation: Using the knowledge from step two, insurers can push additional classes of businesses through high-speed pipelines or incorporate different companies into the workflow to automate operations further. The importance of manual assistance can’t be overstated, even in an environment of complete automation.

Lastly, insurers can choose additional business segments, based on step two, to automate their operations more deeply to reduce their operation costs. AI in underwriting will require manual intervention, even in completely automated environments.

AI for Underwriting Modernization – Use Cases & Applications

1. Data Intake: The benefits of intelligent automation and RPA in insurance have been strengthened for real-time underwriting technology. Software takes over data compilation from various sources, making data collection quicker. Intelligent technologies read documents and extract the necessary data to deliver valuable information to the underwriting process.
2. Triaging and Risk Assessment: Bulk data requires examination and modification to gain risk insight. Automation, including artificial intelligence, supports triage. Developed by underwriters, the rules can guide customers to the product that meets their requirements by categorizing information. AI reduces the workload of human underwriters by handling lower-value policy submissions.
3. Pricing: AI and ML can create pricing models for policy based on risk variables. It can suggest the price. You can develop pricing algorithms using policies. The creation of data analytics models, and underwriting expertise is key to efficient pricing.
4. Processing: Intelligent options can handle administrative insurance tasks, extracting data from platforms that track claims and manage policies. They will integrate workflows for policy underwriting. This enables simple policies to be underwritten quickly, and the customer experience to improve.

Underwriting Modernization Strategy – 4 Steps

To modernize underwriting and customer onboarding, companies must be restructured and adopt agile principles. The four most crucial elements for this are:

1. Adopt Systems Approach: Understand how different components of the process interact. The procedure must be easy to understand and simple to submit. Give insurers top-priority underwriting standards to pick the bare minimum of information required for risk assessment and decision-making. The most important aspects when adopting a systems approach are:

   • Submission and Requirements Gathering: The procedure must be straightforward. Simplify the questions to gather the most critical data and fill it in automatically.
   • Underwriting Decision Process: Prioritize underwriting standards to collect the bare minimum information to evaluate risks and make judgments. Strong audit and risk controls and test-and-learn feedback loops should guarantee continuous efficacy. Automate as many elements as possible; quick human review is necessary for non-automated scenarios.
   • Digital Issuance: Digitize paper files and analog procedures with electronic or voice signatures.
2. Break Down Silos: Coordinate departments including underwriting, actuarial, product development, distribution, IT, risk, legal, and compliance. Introduce newer roles, including data science and advanced analytics. Delivering successful change requires cross-functional teams.
3. Bring Changes from the Highest Level: Maintain momentum, resources, and conviction by getting buy-in from senior leaders. Understand that the transition will alter how underwriting and onboarding are provided.
4. Fast-track the Pace: Deliver tangible successes for customers every quarter. Deliver bite-sized features to the market, followed by quick course corrections. Reduce development cycles to months, or weeks. Successful execution needs agile processes.

Conclusion

Insurance companies need to adopt AI-based underwriting to avoid falling behind on short and long-term goals. A lack of data-driven insights could leave competitors ahead. Keeping underwriting modernization as a priority is going to be beneficial for organizations to retain long-term clients and have a proactive underwriting team.