Artificial Intelligence in insurance

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Artificial Intelligence in insurance

Due to the virtual nature of insurance, the insurance industry is ideal for digitalization.

On the other hand, because of knowledge complexity, it has not been easy to automate.

Insurers wanted to apply predictive analytics and machine learning techniques to detect fraud for decades. However, the challenge was a small amount of structured data available, as the information was "hidden" in documents and heads of experts.

There is a great number of documents and pictures to analyse in claims handling and underwriting, and a lot of information to match with many conditions. That complexity requires many experienced employees. Moreover, with such an elaborate process complexity, a lot of mistakes may occur.

Insurance business is condition- and contract-based. Insurer’s representative needs to know how to effectively respond to regulatory inquiries or manage contract risk. For a call centre consultant finding a correct way to answer specific customer question plays a big role.

It is not easy for contact centre consultants to answer customer questions ad hoc while managing their emotions at the same time. It is challenging (if not impossible) for a supervisor to monitor the calls and intervene – a few against hundreds of calls.

It happens that a customer needs to type a lot of information during claim notification online, little hints. The same information may exist in documents he already attached. And then he is notified about missing information even a few days later and again – waiting for a proper reply.

Insurer’s benefits from AI

Lower operating costs

AI automates repetitive human tasks up till now possible to carry out only by humans. Examples include analysing documents, pictures, and complex information.

Lower claims paid

Humans make mistakes, which results in claim leakage, usually estimated by internal audits. Once trained, AI is consistent and independent of tiredness.

Lower substitute costs in claims

AI automation can shorten the claims handling process, which may result in shorter rent of a replacement car or lower payments of business interruption claims.

Better customer experience

AI enables new ways of interacting with customers. An immediate, empathetic and correct response from the insurer can have a positive impact on the customer's perception.

Better analytics of frauds, tariffs, efficiency

Automatic AI recognition unveils data hidden in unstructured form in documents and photos that previously had to be manually processed by employees. Structured data is the fuel for predictive analytics.

Improved contract visibility, compliance & risk assessment

AI allows for automatic search and analysis of different types of contracts. It enables insurers to respond quickly to regulatory inquiries and increases the visibility of contractual risk.

Better employee experience

AI automation will reduce manual tasks and foster efficiency, allowing employees to focus on the higher-value creative aspects of work. Additionally, it will create exciting new jobs related to configuring and managing automated processes.

Higher quality of human work

AI can augment the work of all employees, not just experts, leveraging features like analytical skills and scalability to unlock insight and efficiency. This enables faster decision-making, more precise problem-solving, and enhanced innovation across the organization.

AI tools are only a small part of the overall landscape

Typically, AI automation introduces new data and business processes into current systems, which means the entire company architecture must be rethought to support it.
Otherwise, excessive implementation costs and architecture complexity can halt the progress of AI automation initiatives.
As an organisation, do we have experience in managing complex architecture change connected with business transformations?

Cloud platforms with AI tools

The major cloud providers offer a set of various best-of-breed AI components for addressing different problems of automation. You can think of it as building blocks, which democratises the usage of AI.

Examples: AWS, Azure, GCP.

injured person getting money from insurance

omni:us Digital Claim Adjuster

The solution enables end-to-end automation of the insurance claim-handling process. It comes with AI components, reference processes and preconfigured business logic.

SEND Smart Submission & Underwriting Workbench

Underwriting workbenches support the underwriter workflow in managing new business, renewals and endorsements. They have various automation features which enhance submission, risk selection, pricing, quoting and underwriting. Some of the features are AI-driven.

Intelligent Document Processing platforms

IDP platforms provide a complete set of functionalities, including AI and NLP, to streamline the entire workflow of extracting information from various types of documents.

Example solutions: ABBYY, Appian, Hyperscience, Indico Data, Tungsten.

Generative AI/LLMs

Generative AI/Large Language Models (LLM), like ChatGPT, can augment experts, perform complex tasks, and improve business processes. In Sollers Consulting, we leverage the capabilities of LLMs to fit the needs of insurers.

Examples: Amazon Bedrock, Google Gemini, OpenAI.

Predictive Analytics

Predictive Analytics employs statistical algorithms and machine learning techniques to analyse historical data, unveil patterns, and predict future events or trends, allowing organizations to gain insights, anticipate outcomes, and make informed decisions. These solutions are integrated into various AI cloud platforms and enterprise decision engine systems, among others.