Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

How AI-Based Service Engagement Platforms Benefit the Insurance Industry

A service engagement platform that uses artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) to digitally transform both backend processes and conversational interfaces.

How AI-Based Service Engagement Platforms Benefit the Insurance Industry

Insurance agents often spend an inordinate amount of time chasing down and processing claims. For example, one Fortune 500 insurance company recently told me their agents make 671,000 outbound calls per year just to gather return to work dates to process the company’s short-term disability claims. A typical claim interaction takes each agent about three weeks and roughly six attempts to reach each claimant and get this information.

To help remove this time-intensive phone dialing burden, insurance companies are starting to implement AI-based workflow automation platforms that utilize artificial intelligence (AI), machine learning (ML) and robotic process automation (RPA) to digitally transform both backend process and conversational interfaces.

Read More: Why Is Explainability Important and How Can It Be Achieved?

How it Works

Related Posts
1 of 472

AI-based service engagement platforms typically incorporate a language Intelligence engine that enables insurance companies to build omnichannel, service workflows across a disparate set of unstructured and semi-structured data sources, such as emails, document stores, knowledge bases, etc. The machine learning algorithms incorporate some form of natural language processing (NLP) and natural language understanding capabilities to assist with tasks that include text classification, relationship extraction, question-answering, topic detection and many more. If the platform features automated machine learning (AutoML), it should be able to train and deploy complex models at scale, support multiple languages and custom ontologies for domain-specific learning.

Insurance companies are using the technology today for automating email triage, claims processing, digital triage of incoming insurance quotes (accept/declines) and as a classification pipeline input into data extraction (extract business categories, disease/ailment labels and more).

Read More: Cryptocurrency Tax Returns and the IRS

The Benefit to Insurance Agents

In speaking with insurance organizations who could benefit from an AI-enhanced workflow automation platform, I’ve encountered some misconceptions. Most often the concerns revolve around the idea that the technology will ultimately cost people their jobs. While theoretically, the technology could replace headcount, it would not be a wise decision for an insurance organization to do so. Insurance, like any business, must always be growing the business. Replacing headcount with AI technology in this instance won’t help grow a business, it will only allow it to maintain the status quo. Using the technology to augment an agent’s job enables agents to effectively delegate the more menial aspects of their job tasks to the software. This frees up the agent’s time to pursue new revenue-generating tasks, and hence grow the profitability of the business.

According to U.S. News & World Report, commissions are an important source of income for most insurance agents and those agents spend considerable time developing and pursuing sales leads. There tends to be a lot of turnover in this career because many new agents struggle to earn sufficient commission income and switch to other occupations. For agents working on commission, AI-based workflow automation platforms allow them to potentially increase their pay. For example, the median salary of an insurance agent is $49,710. Assuming an AI-based service engagement platform can shave 10 percent off an agent’s routine workload, allowing them to focus on more important commission-based work, it could equate to a pay bump of $4,971 per year. And because the technology is picking up parts of the job agents typically don’t like doing, job satisfaction goes up.

The insurance company described above ultimately implemented an AI-based automation platform and let me know that since implementing the technology into their network, they’ve saved roughly $11.5 million in agent call time and reduced the claims process from three weeks per claimant to an hour. And when I asked if they’ve laid anyone off as a result of implementing this technology, they said, “no.”

Read More: What Is Automated Machine Learning (AutoML) and Is It About to Put Data Scientists out of Work?

Leave A Reply

Your email address will not be published.