What are the benefits of using artificial intelligence in car insurance underwriting

AI is a broad application of machine learning models trained on historical data that closely measures some behaviour. These models can extrapolate that data into predictive models. Conversational AI, a specific subset, is a technology that combines natural language processing (NLP) and machine learning (ML) in a software package along with responsive features such as digital assistants and chatbots.

Benefits of AI in Insurance Underwriting

1. Minimise the possibility of human error

No matter how intelligent and rational humans are, mistakes are usually possible. This is where insurance modernization plays an important role. AI in insurance can reduce the likelihood of errors by combining huge data sets in various formats.

Human underwriters apply preset identifications and models, then evaluate the results and make informed choices based on the data. AI is algorithmically self-sufficient and committed to learning from previous mistakes. In insurance underwriting, AI saves time and is more efficient and scalable.

2. Better risk awareness

Underwriting best practices involve a variety of data sources. With the help of AI, insurance underwriting can expand and improve access to these data sources, which in turn can improve risk assessment.

Insurance companies are already seeing positive results from AI adoption, including introducing predictive analytics models, algorithms, big data, and machine learning into their departments. These solutions assist in cutting down on laborious due diligence processes.

3. Fight against cyber threats

Cyber threats within businesses are increasing. As more businesses adopt cloud-based infrastructure, the risks associated with cybersecurity will also increase. For insurers, keeping up is a never-ending struggle.

Machine learning-based systems for fraud detection can address these new risks. Ultimately, you need to be able to anticipate new cybersecurity risks before they materialise. The future of insurance underwriting will be AI-assisted, providing better security and more advanced coverage features.

4. Improve customer loyalty

Insurers can use AI in underwriting to improve customer experience during the sales process and build loyalty from the beginning. Insurance companies can develop long-term retention roadmaps based on individual account servicing, profitable pricing models based on risk sharing, and practical loss control strategies by automating low-complexity functions. This allows underwriters to have more complex customer interactions.

Leading commercial insurers are already upgrading underwriter technology to enable them to take on higher value liabilities. Additionally, we are implementing an AI-based platform to speed up the acquisition process and after-sales service.

5. Opportunity to acquire new business

Just as we know that AI in underwriting will become integrated with the larger insurance value chain, insights from a centralised data lake can support cross-platform visibility and create new cross-selling opportunities.This aids in improving the client experience that we offer.

The integrated AI-based system allows insurers to approach customers with customised plans before they submit an application. For example, underwriters can identify queries from NLP-based chatbots to get a holistic view of the customer journey. This allows insurers to take into account the different concerns of their customers.

6. Fire price

According to a McKinsey report, a small business owner looking for commercial property and casualty insurance reported receiving coverage amounts from five different insurers for nearly identical risks that varied by an astonishing 233%. The loss of commercial lines alone costs a company like AIG $75 million a day. This scenario illustrates the pricing inefficiencies plaguing the commercial insurance sector. Failure to accurately capture your risk profile can result in losses.

Automated underwriting provides better risk visibility in these cases. Acting as a knowledgeable gatekeeper responsible for course correction, underwriters recommend the best pricing options and coverage terms.

7. Increased profitability

AI can contribute to the profitability of the insurance industry by helping underwriters reduce loss ratios, increase quote conversion rates, and ultimately optimise the overall resource utilisation of the insurance industry. Therefore, insurers must leverage automated underwriting to drive high-impact reforms and increase profitability.

Using an AI-enabled acquisition transformation roadmap, companies can reduce cost ratios and create better employee experiences. In AI-driven insurance, the role of insurers will expand to business builders and value creators.

AI in your insurance underwriting automation journey

Insurance companies prefer to modernise underwriting by automating various aspects of their purely manual workflow. AI underwriting is now available to commercial insurers as part of a comprehensive package.There are three main steps in your automation journey:

1. Prefill

AI helps populate data in insurance underwriting. Computer algorithms help insurance companies mine a variety of data sources, including structured data, to pre-populate application data for small commercial risks. This information can provide classification suggestions and risk characteristics that impact premiums and claims costs. Human underwriters can more easily evaluate this information without having to search the web to find relevant insurance standards.

2. Optional automation

Depending on the insurer's risk appetite, certain insurance segments can be selected for automatic underwriting. In this case, pre-filled application data is automatically compared against the insurer's underwriting criteria to determine whether approval is possible or whether additional information is required. The insurance company's business logic is used to automatically apply credits or debits and generate quotes.

3. Full-scale automation

Based on the knowledge gained in the second step, insurers can select additional business classes to further automate operations through accelerated pipelines or integrate multiple companies into automated workflows. AI in insurance underwriting requires manual intervention even in fully automated environments.

Finally, the second step allows insurers to select additional business units to drive automation deeper into their operations. The important thing to remember here is that AI in acquisitions requires manual intervention even in fully automated environments.