2. June 2022

AI-powered customer service in the insurance industry

Words by Gaurav Athavale

In recent times, the focus in the insurance industry has shifted towards delivering excellent customer service by adopting a consumer-centric model. As the customer volume steadily increased with rapidly improving tech, the demand increased for using modern technologies to speed up insurance activities. One such technology that has now managed to make a significant foray into this industry is AI in the form of cloud-based solutions. This has caused extensive positive ripples for AI-powered customer service in the insurance industry. In fact, in 2021, over 40% of CIOs increased their expenditure to implement AI for pilot projects in the insurance industry with an aim to improve customer service [1].

The customer base in insurance and other financial services now makes the associated organizations demand faster processing of their inquiries, personalized services, and improved security for personal data, among many customer-centric actions by using Artificial Intelligence. The main driving factors for the industry to adopt AI are –

Detecting and analyzing complex unstructured data (free-text)
Faster communication with customers 24/7
Real-time interactions and responses
Manage streamlined operations amidst strict regulatory environments

Taking these facts into consideration, this blog elaborates on key operational areas in the insurance industry being transformed with AI for improving customer service.

Claims handling for AI-powered customer service

Traditional claims processing in manual ways was prone to errors, thus affecting work efficiency, and driving up insurers’ costs by about 50% [2].

With AI coming into the picture, claims management became faster with fewer errors, along with processing large volumes of claims and notices in a short span of time within a few days.

Many vendors today provide AI solutions for claims handling that can be integrated with CRM systems, third-party email systems, and email & document archival systems. These solutions can be trained with the help of minimal samples to read and understand claims and insurance notices, interpret the information, extract data, and carry out actions such as categorization, replies and forwards, updating CRM, and archival. Some of the key areas in claims handling use cases where AI can be used for customer service are – claims management audit, initial claims routing, and fraudulent claims detection.

The AI-powered algorithms in the solutions can also scan incoming data, interpret it with minimal agent help, and provide faster claims settlement to end customers, thus boosting overall customer service. These solutions also learn over time with repeated training and become better at claims handling processes.

Document digitization for AI-powered customer service

Traditional insurance companies relied on paper-based print documents, and even hand-written texts. Retyping all the info took multiple days for agents in their systems, thus hampering task efficiency. However, this changed as rapid document digitization using Optical Character Recognition (OCR) made a foray into the industry.

AI-powered automation solutions with OCR enable automated data entry of documents via integration with third-party systems in the business work environment.

OCR can handle different types of documents including images. Together with Natural Language Processing (NLP) techniques, these solutions simplify communication between operational systems and humans. About 80% cost savings are possible by using OCR in the insurance industry as per Mckinsey [3].

When it comes to formats, data mostly exists in an unstructured form (called free-text), which is difficult to process with traditional business automation solutions. However, AI solutions with OCR can accurately read and interpret free-text data even if the formats keep changing from business to business. In the insurance industry, claims, documents, and even policies don’t exist in a structured manner, thus emphasizing the importance of OCR which can increase the accuracy of processing insurance documents. This can ultimately lead to enhanced AI-powered customer service by improved customer onboarding and KYC processes.

Underwriting management for AI-powered customer service

Artificial Intelligence-based business automation is also suitable for implementation in underwriting procedures to provide guaranteed assurance to customers while applying for insurance policies. Moreover, AI that can perform evaluations based on rules and decisions, can also provide accurate estimates to customers in case of an event that demands using insurance coverage benefits.

Modern AI solutions can update third-party systems with real-time information based on the incoming data. This is highly beneficial for customer service in insurance as the processing time of customer info is reduced, further increasing the chances of such insurance cases getting accepted faster.

Fraud management for AI-powered customer service

Insurance companies are highly prone to fraud in the BFSI sector and often sustain millions of dollars in loss every year due to fraudulent schemes. For instance, according to Insurance Europe, it is estimated that in 2022, insurance fraud cases will cost European insurers approximately €13bn a year [4].

With AI penetrating the insurance industry, many companies have started adopting the technology for improved fraud detection and eradication. Fuelled with machine learning and deep learning, AI solutions can identify recurring patterns of unordinary customer behavior that could be fraud signatures. With reference to customer service, these AI solutions can assist in carrying out thorough background checks of customers before they are onboarded for insurance applications.

Conclusion

The insurance sector from a customer service perspective offers huge scope for Artificial Intelligence. Fast customer responses and round-the-clock communication are prime aspects of customer service in this sector, together with simplifying complex insurance cases. These aspects are mainly responsible for driving the need for AI in insurance.

Today, we see AI being incorporated in different work areas such as claims handling, document handling, underwriting, and many more, that contribute to better customer service. In the next few years, more advanced AI technologies will emerge. These could make complex actions easier to process such as claims settlement, premium adjustment, and legal activities related to insurance.

About Simplifai

Simplifai is an AI solutions company that provides end-to-end automation for businesses with the help of Digital Employees. These AI-powered solutions can work in any third-party system, can be programmed to carry out specific tasks at the front- and back-end, and provide high-grade automation. Our focus lies in the BFSI sector, and many of our Digital Employees are suitable for implementation in the Insurance Industry. These Digital Employees are capable of supporting and with business teams for claims coordination, document processing, front-line support, payment and policy handling, and commission calculations. In the future, our solutions will also streamline fraud detection and underwriting processes for insurance businesses.

Your company has already taken a very good step ahead by considering using AI in business applications and what AI can do for a business. It’s time to know more details about starting out in AI, and the further processes involved. For the same, check out how Simplifai’s Digital Employees for Insurance work by clicking the following:

Learn about Digital Employees for Insurance

Sources:

[1] Shaw, Gary., 2022 insurance industry outlook (2021). Deloitte. https://www2.deloitte.com/us/en/insights/industry/financial-services/financial-services-industry-outlooks/insurance-industry-outlook.html

[2] Successfully reducing insurance operating costs. (2015) European Insurance and Asset Management. Mckinsey & Company https://www.mckinsey.com/~/media/mckinsey/industries/financial%20services/our%20insights/what%20drives%20insurance%20operating%20costs/successfully_reducing_operating_costs.ashx

[3] McKinsey & Company (2022). Financial Service: Our Insights, Mckinsey & Company https://www.mckinsey.com/industries/financial-services/our-insights/evolving-insurance-cost-structures

[4] Fraud prevention: Insurance fraud is not a victimless or insignificant crime (2022), Insurance Europe. https://www.insuranceeurope.eu/priorities/23/fraud-prevention