AF Group companies will use CLARA's industry-leading technology platform to identify the health care providers best suited to help injured employees recover successfully and return to work quickly. AF Group comprises seven affiliated insurance brands that provide innovative, specialty insurance solutions. AF Group companies utilize industry-leading best practices, analytics and resources to help manage risk and minimize losses for policyholders -- and always strive to provide injured workers with security, compassionate care, and the opportunity to return to work as soon as possible. "Innovation in data analytics is a key pillar in our strategy to be the best at what we do, maintaining a culture of claims excellence and compassionate care for our customers," said Paul Kearney, Chief Claims Officer at AF Group. "Returning to work after an injury helps employees rebuild their livelihoods and restore their quality of life. AF Group companies help those workers using a multifaceted approach that incorporates data analytics, evidence-based medicine, and smart technology. CLARA Analytics aligns nicely with that strategy -- helping injured employees recover quickly while also minimizing losses for policyholders and improving our claims management processes."

Insurance is a $1.2 trillion industry in the U.S. alone, employing 2.9 million people. Historically, the insurance industry hasn’t felt the effects of digital disruption, due to a strict regulatory environment, the scale required to create a risk portfolio, and the time needed to establish trust with customers. But in a recent IBM Institute for Business Value (IBV) survey, insurance executives identified changing market forces (such as increased competition and changing customer preferences) as the top driver affecting their enterprise. The core function of the insurance industry, risk management, has gotten more complex as customer data continues to compound. Insurance companies collect data scattered across siloed business units in paper or various unstructured digital formats. In this data-rich environment, underwriting and claims management workers don’t have immediate access to the information needed for informed internal and external decision-making, leading to burnout and costly mistakes. In fact, knowledge workers spend 30% of their time finding information required to…

Many insurance companies are automating their claims processing as it minimizes turnaround time, regulation and claims processing cost leading to enhanced customer services and business profitability. FREMONT, CA: Claims processing cost and fraudulent claims payment raise operation cost of the companies and cause a lot of hassle for customers. This is why claims management has become a priority for insurance companies as it influences the bottom line and customer retention strategies. Digital transformation and artificial intelligence (AI) can optimize claims management practices and provide improved customer satisfaction. However automation in the insurance industry can face some challenges.

Artificial Intelligence within the Insurance industry has overhauled the claims management process by making it faster, better, and with fewer errors. From smart chatbots that offer quick customer service round the clock to the array of machine learning technologies that spruce up the functioning of any workplace through its automation power, the expanding potential of Artificial Intelligence in Insurance is already being used in many ways. With increased awareness and resources about the game-changing influence of AI in the Insurance industry, the initial hesitations and shallow discomfort around its implementation are now fading quickly as it begins to trust in the caliber and numerous opportunities brought forward by Artificial Intelligence and Machine Learning. The only question that remains is -- how far can we push its capabilities? In 2017, Artificial Intelligence has shown its substance in various business verticals by rapidly creating controlled, digitally enhanced automated environments for maximum productivity.

Artificial Intelligence (AI) continues to gain traction as a significant business driver for large organizations across major industries. The traditional financial services industry was known to be slower in adopting new technologies, with some organizations using software running on COBOL or Fortran, which was invented in the 50s and 60s. Recently, thanks to the rise of fintech and more AI applications becoming available, the industry is starting to accelerate investment in AI across all key business functions. But how should the right AI use cases in FinServ be identified? While internal efficiency use cases might be great short-term wins, zeroing in on the customer experience may prove to be a competitive advantage in the future.

Multiple disruptive forces are reshaping the global insurance sector. Technology such as artificial intelligence (AI), machine learning (ML), blockchain and internet of things (IoT) solutions stand on one end, and nimble, innovative insurtech startups sit on the other. These technology advancements are forcing traditional incumbents to rethink their business models and accelerate their digital transformation strategies. In the coming months, insurance as we know it will undergo rapid transformational changes that include the following trends. Back-end workflows within the insurance enterprise (including underwriting) will be automated to a large extent using AI/ML.

WIRE)--Gradient AI, the leading enterprise software provider of artificial intelligence solutions in the insurance technology space, announced today that it has raised $6.0 million in a Series A financing led by Forte Ventures and Sandbox Insurtech Ventures. The round also includes participation from Gradient's existing investor MassMutual Ventures. Gradient's artificial intelligence helps commercial insurers automate and improve underwriting results, reduce claim costs, and improve operational efficiencies. The Gradient software-as-a-service (SaaS) platform boasts a proprietary dataset comprised of tens of millions of claims, which is complemented with several economic, health, and litigation datasets. This robust aggregation of data provides out-of-the-box claims and underwriting precision for new clients, and it is continuously refined with client-specific data over time.

The insurance industry is facing tumultuous times with technology shaping the way it operates. And, in a bid to cover the possibilities and challenges of inculcating artificial intelligence and machine learning in the insurance industry, we have already learned a lot in this four-part series. In the introductory piece, we analyzed the existing scenario of the insurance industry, considered the challenges it faces today and skimmed over the opportunities AI presents to eliminate hurdles in insurance on the path to digital. We followed that up with a second in-depth article that detailed how artificial intelligence is helping the insurance industry prevent frauds and false claims – a pressing challenge for organizations in the space. We concluded the report with a glance over the possibilities further down the road at the intersection of AI and insurance companies.

With this, Artificial Intelligence within the Insurance industry has overhauled the claims management process by making it faster, better, and with fewer errors. From smart chatbots that offer quick customer service round the clock to the array of machine learning technologies that spruce up the functioning of any workplace through its automation power, the expanding potential of Artificial Intelligence in Insurance is already being used in many ways. With increased awareness and resources about the game-changing influence of AI in the Insurance industry, the initial hesitations and shallow discomfort around the its implementation are now fading quickly as it begins to trust in the caliber and numerous opportunities brought forward by Artificial Intelligence and Machine Learning. The only question that remains is – how far can we push its capabilities? In 2017, Artificial Intelligence has shown its substance in various business verticals by rapidly creating controlled, digitally enhanced automated environments for maximum productivity.

Most of the time, the discussions looking into artificial intelligence (AI) have seemed like they're in the distant future. AI-powered technology fully automates business processes and displaces large swaths of today's workforce. You don't, however, hear all that much about the ways that AI is already making significant changes in the way businesses work. Part of the reason we don't hear all of the significant business work uses that AI is doing is because current-generation AI technology hasn't become as fully autonomous in operation, as some would wish. AI has been limited to functions carried out in the background, rather than being deployed in customer-facing positions.