Written by: Trevor Lloyd-Jones, Content Manager, LexisNexis Risk Solutions

In today’s digital age, consumers have begun to expect seamless interaction and personalised services from their insurance providers across any channels they wish to utilise. No matter the brand, customer satisfaction is king and insurers have started upping their game when it comes to speed of service and underwriting, increasingly delivering smaller, bit-sized policies that fit in with everyday needs.

The buck doesn’t stop with personalisation. With vast reserves of data, a sea of customer queries and thousands of claims to process regularly, the insurance industry faces challenges associated with reaching out to customers in a timely manner with the right mix of products, that are ideally tailored to their needs and facilitating faster claims settlement.

In a situation such as this, artificial intelligence is proving to be a game changer with its ability to support the insurance industry, playing a role in the R&D process of data modelling, helping to shape custom-fit services and improved customer satisfaction.

Development of insurance product according to needs

At the heart of artificial intelligence lies data, and the availability of data in the insurance workflow. With good quality data and machine learning for helping to define algorithms and business processes, insurers can be in a better position to know when and how to communicate with the consumer. The industry can begin to gain a better insight into individual consumer habits, their needs according to lifestages – such as home, location, family and social activities – as well as preferences.

This puts insurers on the road to creating a more seamless way of selling insurance, towards an optimum mix of insurance products for a particular customer, at the most appropriate time and across the right channels.

Changes are coming and creating a less invasive, more responsive experience for policyholders. Our big data sources at LexisNexis Risk Solutions, combined with other innovations coming from insurtechs in the market, are enabling algorithms for assessing risk without the need to conduct lengthy form filling by the customer, or external data verification.

Enhanced one-on-one interaction with insurance products

Artificial intelligence is increasingly going to allow consumers to derive better value from their communication with the insurer’s interface. AI-powered service executives and advisor bots, for instance, can be leveraged to offer consistent counselling, recommendation and post-sales services to customers. It’s all about using automation efficiently for simple, repetitive tasks and simple questions, whilst bringing in human help, such as a human claims handler for more complex tasks, where they can add more value.

Natural Language Processing (NLP) is also helping to put the consumer in the driving seat, using supervised computer learning to take voice recordings from customer interactions – such as a claim on a policy – and converting those texts from various sources into business insights using technology.

The power and accuracy of semantic language processing is improving all the time. One example of this would be the use of voice analysis from customer claims to be able to identify deception signals in the voice (and propensity to commit fraud) or to stratify claims in the most efficient way into ‘pay’, ‘investigate’ or ‘hold’ workstreams. For an insurer, such data-driven processes begin to create a new era of interaction with customers, with greater speed and efficiency, and without always the need for a live representative.

In the insurance sector chatbots have elements of simple machine learning and they have been used for some time now. However, with NLP and advancements in processing power, this technology can be leveraged further to facilitate more complex communications with customers and make interactions faster (using machine power for automation) but also more meaningful and enjoyable (using human power for the human touch where it is needed).

Faster settlement of claims

Filing a claim has traditionally been time consuming, usually requiring human intervention and manual form filling. It is a part of the workflow that is ripe for generating efficiencies through greater digitisation of processes. It is also the element of contact with the consumer where the insurer can really enhance their brand and create a positive experience through data knowledge.

This potential to create productivity gains through automated processing is a global phenomenon and it’s an area where insurance has tended to fall behind other industries such as telecoms or airlines. One recent global report* identified that insurance has not yet fully harnessed technology to address structural operating costs. The gap in productivity has been growing between the top-performing (and most digitised) insurers and the lesser-performing insurers.

IT costs have been rising as a percentage of total operating costs for insurers globally and it’s clear that this is where insurers have been putting their focus. IT costs have risen by  by 24% over the past five years for non-life insurers (reaching 21% of operating costs).

Yet such is the pace of change, and with the consumer’s digital demands continually rising, the race is still on for insurers to make process improvements in claims and other customer touchpoints.

With touchless claims or low-touch claims requiring minimal human intervention a new claim can in many cases be stratified and scored instantly and respective damages validated. Customers benefit from a better and faster experience without having to go through the proverbial ‘red tape’ and form filling. The elements of fraud, waste or abuse can be made more visible by such data enrichment in the claims process.

AI and algorithms can be used to sort claims, dissect aberrations in data patterns and single out spurious claims. The machine learns from past patterns of fraud, across a claims database of the market that is as wide as possible, and is able to apply this predictive analytics to current claims. Once the claims are sorted and stratified, the human resources of the claims handler can be applied to those cases where the benefit is greatest, such as the larger complex claims, and those requiring a challenge or most likely to result in costly legal action.

In conclusion, when leveraged at the right points in the insurance workflow, and powered by enough data, AI can bring many efficiencies and process improvements.

AI can help spearhead efforts towards increasing customer satisfaction by helping insurers understand the needs of customers better and deliver products that fit their risk profile and preferences.

*Report by McKinsey & Company, ‘The Productivity Imperative in Insurance’. This excerpt was originally published by McKinsey & Company, www.mckinsey.com. Copyright (c) 2019 All rights reserved. Reprinted by permission.

Follow the link to the LexisNexis Risk Solutions website to find out more about how we support insurers.

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