How Machine Learning Could Transform Insurance Underwriting

by | Published on Apr 25, 2019 | Document Conversion / Scanning Services

In the insurance industry, under writing is the initial process of approval. Insurance companies need to review the risks when working with clients, so having accurate and reliable information is critical. However, inconsistent results are common as by-products of manual intervention and decision making by humans. Now, advanced technology solutions are available that can capture and extract data in a cost-effective and efficient manner. This enables data analytics to be done more easily and effectively. Machine learning (ML) is being increasingly used to evaluate and rank insurance deals when underwriting. Bulk document scanning companies also provide great support to insurance companies with accurate insurance data entry for various forms.
How Machine Learning Could Transform Insurance Underwriting
Machine learning (ML) can be used to predict future data from a given set of large amount of data using statistical models. Artificial intelligence (AI)and ML can be used to perform certain tasks like humans work and think. These system scan easily perform tasks like visual perception, speech recognition, decision-making, self-correction, and translation between languages. Underwriting energized by AI can help speed up the process and enable quick and judicious decision making.

According to experts, the three AI related trends in the insurance sector are: behavioral policy pricing, coverage personalization and customer experience, and faster claim settlements.

  • The IoT (Internet of Things) will provide personalized data to pricing platforms, and this will allow people with healthier lifestyles to pay less for health insurance, or people who drive safely to pay less for auto insurance.
  • Chatbots can provide personalized interactions with customers based on the customers’ geographic and social information that is available. In addition, insurers can allow users to customize their coverage for specific events and items.
  • Online interfaces and virtual claim adjusters will enable more efficient claim settlement, and reduce fraud.

Artificial intelligence is not widespread in the insurance underwriting landscape, but in the near future, there will be a seismic impact of AI and its related technologies, which can lead insurance underwriting to the next level from distribution to underwriting and pricing to claims. When insurance companies are equipped with quality data, they are better-equipped to make informed decisions that will benefit both themselves and their clients.

What are the ways in which insurance companies can make the best use of machine learning and AI solutions?

  • AI-powered underwriting services can extract insights from multiple data sources using the AI based underwriting platform. Thus data capturing for claims processing has become fast and a great benefit to the clients too. The ML algorithm model provides the support underwriters need to make decisions more quickly and with more sensible judgment.
  • With NLP or natural language processing, applicants can communicate with a bot and get the data required for an underwriter to make a risk assessment. NLP also facilitates text-based data mining. When the relevant information is input into a bot, it instantly creates an electronic form. This form can be fed into the algorithm with the help of machine learning, which insurance underwriters can use later for fast data access. Natural language processing can act as virtual assistants to underwriters by performing data entry as well as by assisting them via search-based analytics to identify relevant data regarding the risk they are writing.

A provider of data entry services for the insurance sector knows that the industry still has challenges to face when attempting to implement ML and AI solutions. The main issue is the data that exists in diverse systems and applications. This spread-out data has to be consolidated before an ML algorithm can start the data mining process. Another concern is the considerable investment required for implementing AI solutions. Insurance companies cannot suddenly launch these systems, the approach has to be cautious and slow to reduce financial risks and also avoid wasting time.

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