JERSEY CITY, N.J., Aug. 19, 2021 (GLOBE NEWSWIRE) -- Many consumers may soon be able to buy life insurance more quickly without undergoing inconvenient at-home medical tests. Management Research Services (MRS), a cutting-edge technology and data provider for the life insurance industry, is integrating Verisk's groundbreaking proprietary voice analytics into its telephone medical interviews to help flag potential tobacco users early and streamline the underwriting workflow. MRS is implementing Verisk's Tobacco Usage Propensity Model, which uses artificial intelligence and machine learning to analyze audio interviews. MRS will then apply the rules it has developed, based on Verisk's model output, to identify a small percentage of applicants who may require lab testing to verify their tobacco usage status, while enabling the vast majority of applicants to bypass lab testing. "Verisk will add tremendous value for our customers," said Tim Dineen, CEO of MRS.

A comprehensive list of top startup companies who are building quite a reputation in the tech domain through voice tech offerings. Voice AI has been around since IBM introduced it in 1961 through IBM Shoebox. It was the first digital speech recognition tool which at its time could recognize 16 words and 9 digits. Today, using voice AI, developers can train neural network models, create human like voices, chatbots and more. The voice AI tech startups space is booming and now encompasses various avenues such as voice analytics, speech recognition, artificial voice synthesis, voice transcription, voice recognition, among others.

Corporate contact centers are embracing big data to offer an improved and more customized customer experience. Also, presently, contact centers are digitizing and collecting each customer interaction that happens by means of telephone, social media, email, text or even face to face. Following this move into big data, contact centers are utilizing speech analytics to take their products, processes and customer service efforts step ahead. Voice analytics is the process of digitally analyzing interactions between customers and agents. What's more, despite the fact that the innovation has been around for over 10 years, late headways in digitalization, machine learning and artificial intelligence (AI) have made it all the more remarkable and have empowered contact centers to change piles of data into real-time insights.

Australia's financial watchdog might be dreaming of the day when call centre surveillance software automatically catches crooked insurance sales staff. But there's still a way to go before AI-powered voice analytics can decipher the verbage that bubbles out of a sales boiler room. That's the reality check bowled up to regulators and industry rapidly spitballing prototypes of new regtech solutions as banks, insurers and auditors all trying to find ways to automatically detect bad behaviour without creating a profit sapping compliance cost sinkhole in the process. At a closely watched regtech forum late last month, ASIC outlined its findings from a trial of voice analytics software applied to a sample of life insurance sales calls. With a freshly sharpened set of teeth, the watchdog says it sees "great potential" in using voice analytics to automatically identify instances of potential misconduct in life insurance sales calls - but there's a catch.

In this episode of our Inside the Strategy Room podcast, senior partner Tamim Saleh cuts through the hype around artificial intelligence (AI) and offers clear guidance for executives looking to make precise strategic decisions about where and how to employ AI in their businesses. Tamim shares insights on the impact of machine vision on AI, the future of voice recognition, and the latest developments in advanced analytics, virtual assistants, and robotics. He outlines the challenges companies face when adopting AI and the steps CEOs can take to overcome them. Tamim is a senior partner in our London office, and he is with me at our Global CFO Forum, where he's speaking about AI and machine learning. Tamim, one of the things you've talked about is the notion of five different developments of AI. Tamim Saleh: Machine learning and AI are limited by the fact that when we input data as humans, first of all we are slow, and we make mistakes. One of the fastest-growing technologies is capturing data through image analytics and cameras. And the beauty of this is, cameras don't make the same mistakes we do, because they capture things the way they are, and they don't see the world the same way that we do. In fact, the spectrum is much wider than what we see. It includes infrared, et cetera. So there are a lot of business problems [that image technology can help].

In this interview, Tamim Saleh cuts through the hype around artificial intelligence with guidance for executives about where and how to employ AI in their businesses. In this episode of our Inside the Strategy Room podcast, senior partner Tamim Saleh cuts through the hype around artificial intelligence (AI) and offers clear guidance for executives looking to make precise strategic decisions about where and how to employ AI in their businesses. Tamim shares insights on the impact of machine vision on AI, the future of voice recognition, and the latest developments in advanced analytics, virtual assistants, and robotics. He outlines the challenges companies face when adopting AI and the steps CEOs can take to overcome them. Tamim is a senior partner in our London office, and he is with me at our Global CFO Forum, where he's speaking about AI and machine learning. Tamim, one of the things you've talked about is the notion of five different developments of AI. Tamim Saleh: Machine learning and AI are limited by the fact that when we input data as humans, first of all we are slow, and we make mistakes. One of the fastest-growing technologies is capturing data through image analytics and cameras. And the beauty of this is, cameras don't make the same mistakes we do, because they capture things the way they are, and they don't see the world the same way that we do. In fact, the spectrum is much wider than what we see. It includes infrared, et cetera.

Customer service calls can be ... infuriating. Part of the reason is that humans generally aren't great at reading subtle emotional cues, especially if we only have voice to go by. At the same time, we often inadvertently broadcast unintended emotional signals, easily leading to miscommunication and discomfort over the phone. But an MIT spinoff called Cogito is using voice analytics to help customer service reps better understand how customers are feeling. The technology behind Cogito's enterprise product, which can predict a customer's emotional state by analyzing tone and voice patterns, has also been used to identify signs of PTSD and depression in veterans.

Most retailers of today are completely exchangeable from a customers' point of view. Products and prices are the same and thus no means for differentiation any more. To survive, retailers must continuously deliver a value beyond their core offerings. We advise retailers to focus on CRM to master that challenge, where we understand CRM as a holistic approach that puts customers at the heart of business. The fundament of that approach is to build and foster long-term relationship with key customers and customer segments.

There are various different forms of artificial intelligence. The aspects of greatest relevance to the call center manager are Natural Language Processing and Speech Recognition; these provide the basis for platforms that allow for business-to-customer or business-to-business interactions through the call center model. According to Erni Medeovic, who is a Technical Architect for the Patent Transformation Project, the past five years have seen a steady rise in the use of artificial intelligence for the call center model. This includes analyzing big data sets and making use of predictive analytics, so that automated and personalized customer services can be improved. Interpreting big data With these key metrics, artificial intelligence technology can be used to interpret big data to identify customer browsing patterns, purchase history, recent access to customer devices, and most visited webpages.

The concept of predictive voice analysis appears relatively simple on the surface. You use a computer to analyze a voice file and determine whether the speaker is telling the truth or not, or doing something like following a prescribed speech pattern for example. Of course, while this sounds easy in theory, it is a highly complex task in practice. Indeed, this sounds like an ideal candidate for the use of deep learning algorithms. Deep learning is the next stage of advancement in teaching machines how to make intelligent decisions.