AI in Finance,

3 Ways Artificial Intelligence is Used in Insurance Underwriting

Feb 25, 2021

Artificial intelligence (AI) enables computer systems to accomplish human intelligent tasks without supervision. Examples include researching and gathering information, analyzing data by running a model, and making decisions. The use of AI has increased exponentially especially across the financial industry over the past several years. According to the National Association for Insurance Commissioners (NAIC) the rise in accessible data, higher computing power, technological advancements, and changing consumer expectations has led to a strong acceleration of AI development within the insurance industry. Let's take a look at the three ways artificial intelligence is used in insurance underwriting.

AI in Insurance Underwriting

The rapid development and evolution of artificial intelligence (AI) are disrupting and improving capabilities across the insurance industry. Leading insurance firms such as Allianz, State Farm, Progressive, and Oscar have adopted AI solutions to improve customer engagement and retention and interact with partners and employees. AI is transforming the underwriting, fraud detection, distribution of services, and risk management processes across the insurance value chain.

Today, auto, health, life, and property and casualty insurance firms face the pressures of changing expectations from clients. Insurance firms must rely on technology to provide the quick, on-demand services to meet these evolving needs. With the wealth of data available, the speed at which it is generated, and the different forms it comes in, insurance companies face a number of challenges in underwriting. However, AI and NLP offer solutions for insurance firms by helping extract data, automating the underwriting process, and providing client-personalized recommendations to underwriters.

NLP Use Cases in Insurance Underwriting

Insurers must extract key information from structured and unstructured data to ensure the accuracy of their underwriting process, provide personalized client recommendations, and manage risk. NLP is a subset of AI that enables computers to understand text and audio data in the same way that humans can- with the human language. By combining linguistics and computer science, computers can understand data and interpret the text's intent, relevance, and sentiment.

In order to provide an accurate insurance quote and to calculate the risk involved with insuring an individual, auto, home, and life insurance firms must go through an underwriting process. During the process, an underwriter will review an individual's employment, income, debt, and assets. Underwriters can look at a potential policy holder's savings, checking, 401k and IRA accounts, tax returns, other income sources, and debt-to-income ratio.

1. Extracting insights from multiple structured and unstructured data sources.

With all of the documents needed for the underwriting process, critical information is easy to miss. Underwriters face challenges in sorting through and extracting key insights from the enormous amount of data, which can come in the form of pdf files, excel spreadsheets, audio, images, emails, and more.

Normally, underwriters would have to compile the data and manually go through each document to identify any insights to risk. The manual process can take hours, sometimes even days, and leaves room for human error. In recent years, AI has become a topic of interest for insurance companies as it proves a solution to the obstacles that come with detecting, extracting, and analyzing large amounts of data.

According to a survey by Accenture, 68 percent of insurance employees expect intelligent technologies to create opportunities for their work, and 63 percent of insurance executives believe AI will transform the industry.

Source: Insurance Thought Leadership

2. Automating insurance underwriting with accurate pricing guidelines and policies.

Insurers can automate the insurance underwriting process using advanced AI and ML. AI and ML models can be trained to follow the insurance company's specific underwriting guidelines and policies. Once a client's documents are processed by AI and ML, underwriters can determine the risk presented by the client. The AI can also evaluate how much coverage a potential client should receive and the cost they should pay for it. This allows providers to generate a profit from underwriting and provide personalized policies to enhance customer satisfaction.

The benefits of automated insurance underwriting is that it saves human underwriters time and increases their productivity. With advanced algorithms and AI software, a client's financial and health history can be analyzed quickly. However, manual underwriting can take hours or even days to complete as it depends on manual data research, extraction, and analysis from bank statements, tax returns, medical history, credit score, demographic profile, employment information, and more.

There are unique instances where manual underwriting may be a better option, such as if a client is trying to build new credit or rebuild credit. However, for insurance providers, the manual underwriting process and can be time consuming and inefficient.

3. Personalizing policy and pricing through enhanced services

The importance of data in the insurance industry is often overlooked. Underwriters and claims, distributions, and risk managers can use data to capitalize on client insights. By doing so, insurance firms can actually maximize profit, retain clients, and manage risks better.

After AI and NLP are used to extract information, analyze data, and structure unstructured data on customers, underwriters will have a 360 degree view on the client. Insurers will be able to understand a customer's current and growing insurance needs, financial history and capability, and predict a client's future financial status. By capitalizing on the data, underwriters can provide personalized policy and pricing recommendations to clients.

For example, life insurance companies can use AI to analyze data from medical records, family medical history, bank statements, income, tax returns, etc to provide personalized life insurance recommendations. The insights gathered can also provide accurate and personalized pricing for potential clients based on their current financial status.

Insurers can also keep up with current clients by creating customer profiles and analyzing continuous interactions. For example, a simple neural-network based model or forecasting model can be created with the client's historical data. The data can be analyzed to find patterns and predict a client's behavior. The model can then be used to analyze customers' emails, calls, and online activity to predict whether the client will stay or leave.

Implementing AI within the underwriting process leads to a more efficient and streamlined process. AI models can act as a standardized framework for financial advisors to gather, evaluate, and analyze data and interact with customers. Data analytics increases the quantity and quality of information so that underwriters can make better-informed decisions. Additionally, the insights derived from data analytics and machine learning will ultimately help engage new clients, prevent insurance firms from leaving money on the table, and improve client retention.

Accern's No-Code AI Solutions for Insurance Underwriting

With the advancement of AI and ML tools, we were able to develop a no-code AI platform at Accern, enabling financial analysts and data scientists and engineers to use artificial intelligence without having to code. Firms can now implement AI and ML easily and quickly to enhance efficiency and productivity across the insurance value chain. Learn more about the top AI use cases in finance at the Ai4 2021 Summit.

With the technological advancements, the pressure for financial services teams to provide more personalized services is increasing. Insurance firms that can adopt new technologies to provide personalization will gain a competitive advantage. AI and NLP can quickly extract information from both structured and unstructured data so that underwriters can manage risk and provide personalized pricing and services to clients.

Researchers and data scientists and engineers within insurance firms can deploy ready-made NLP use cases and build custom NLP models. With its focus on empowering and easing workflows for data scientists and engineers and financial analysts, Accern's platform can be used to structure unstructured data, analyze the sentiment and relevance of data, identify specific data points, and summarize large amounts of text with accurate next steps.

Insurers find Accern's platform helpful as underwriters can import various files from PDF, Excel, PowerPoint, CRM systems, and their internal database. Accern's No-Code AI platform can then structure any unstructured data and help with the underwriting processes such as identifying risk, automating insurance underwriting processes, and coming up with personalized pricing and policy recommendations.

One of the ways in which the underwriting process can be automated is through Accern's retrainable adaptive NLP models. Although Accern offers ready-made NLP models that have been built specifically to provide insurance solutions in underwriting, we understand that each insurance firm is different. Therefore, our NLP models can be trained custom to each insurance firm's policy and pricing guidelines.

Once AI models are deployed and the data is analyzed, underwriters can view detailed and visual reports through a business intelligence (BI) tool like Kibana. The Kibana dashboard enables you to build your own visualization through charts, plots, and graphs on top of large amounts of data in a story form.

One of the ways in which insurance firms have found artificial intelligence useful, is by integrating AI tools and software with insurance firms' CRM systems. This enables insurers to keep track of, analyze, and form predictions based on customer notes, emails, meetings, and more. Accern enables insurance teams to upload data from their own CRM system, excel spreadsheet, Word doc, PowerPoint, and PDF to build NLP use cases. Visit The Benefit of Integrating Artificial Intelligence in CRM Systems to learn more about why financial firms are integrating AI with their CRM systems.

The adoption of AI within the insurance industry is growing but there is still much work to be done before we can say most insurance firms have implemented artificial intelligence. The time consumption, cost factors, and expertise needed in creating and training AI models are obstacles to AI adoption. However, with no-code AI, we are one step closer to enabling financial services enterprises to adopt artificial intelligence quicker. To find out if no-code AI is a good fit for your firm, request a demo today.

About Accern

Accern is a no-code AI platform that provides an end-to-end data science process that enables data scientists at financial organizations to easily build models that uncover actionable findings from structured and unstructured data. With Accern, you can automate processes, find additional value in your data, and inform better business decisions- faster and more accurately than before. For more information on how we can accelerate artificial intelligence adoption for your organization, visit accern.com

AI in Finance