Physicians spend nearly two full business days per week on prior authorization requests as part of an antiquated, manual process. Prior authorization is the "single highest cost for the healthcare industry" in the U.S., totaling some $767 million a year, according to the CAQH index. Physicians spend nearly two full business days per week on prior authorization requests, and payers devote thousands of manhours reviewing and approving them in an antiquated, manual process involving phone calls and faxes. The arduous task often delays necessary treatment and sometimes results in treatment abandonment -- patients just get tired of waiting, so they give up -- both of which hurt patient outcomes and ultimately raise costs in the long run. Prior authorization has been identified as one of the biggest opportunities for applying artificial intelligence (AI) to help lower the administrative burden and cost.

Explainable AI can help decision-makers in insurance understand the rationale and logic behind AI and machine learning results. "Most businesses believe that machine learning models are opaque and non-intuitive and no information is provided regarding their decision-making and predictions," -- Swathi Young, host at Women in AI. Explainable AI is evolving to give meaning to artificial intelligence and machine learning in insurance. The XAI (explainable AI) model has the key factors, which are explained in the passed and not passed cases. The features that are extracted from the insurance customer profile and the accident image are highlighted in the XAI model.

Managing risks from poor-quality AI is too important to leave purely to technical specialists. An organization-wide perspective is needed. As the adoption of artificial intelligence (AI) continues at pace across industries, there is increasing awareness of the risks it can pose. Recent high-profile examples have highlighted the risk of unjust bias regarding race and gender, such as those found in some law enforcement or recruitment algorithms. Other examples have highlighted the reputational risk from poorly communicated AI use cases, such as an online insurer's recent claims of using facial emotion recognition to detect fraud.

Lemonade's recent glitch sheds light on public fears about AI -- and about what must be done to keep AI innovation from slowing. Being a disruptor is hard. It requires taking disproportionate risks, pushing the status quo and -- more often than not -- hitting speed bumps. Recently, Lemonade hit a speed bump in their journey as a visible disruptor and innovator in the insurance industry. I am not privy to any details or knowledge about the case or what Lemonade is or isn't doing, but the Twitter event and public dialogue that built up to this moment brings forward some reflections and opportunities every carrier should pause to consider.

Insurance is moving toward a world in which carriers will not be allowed to make decisions that affect customers based on black-box AI. Artificial intelligence (AI) holds a lot of promise for the insurance industry, particularly for reducing premium leakage, accelerating claims and making underwriting more accurate. AI can identify patterns and indicators of risk that would otherwise go unnoticed by human eyes. Unfortunately, AI has often been a black box: Data goes in, results come out and no one -- not even the creators of the AI -- has any idea how the AI came to its conclusions. That's because pure machine learning (ML) analyzes the data in an iterative fashion to develop a model, and that process is simply not available or understandable.

The recent advancements in AI and robotics are some of the most significant computer science advancements of our generation. Over the past decade, U.S. tech firms have made significant advancements in artificial intelligence and robotics, making it far easier and more efficient to automate tasks and functions across industries. Artificial intelligence (AI) affects all types of risks and lines of insurance, and the workers' compensation market has a particularly large stake in the developments. Although the U.S. has experienced technological change and disruption during prior periods of industrial revolution, the pace and scope of the fourth industrial Revolution positions it to have a far greater impact on the U.S. and global economies. The recent advancements in AI and robotics are some of the most significant computer science advancements of our generation.

Advances in cloud computing, AI and sensors are combining to offer insurers new, better variables to characterize occupancy risk in buildings. Commercial general liability insurers traditionally estimate business risk exposure of similar businesses based on variables like floor area and revenue. Advances in cloud computing and artificial intelligence are combining to offer insurers new, better variables to characterize risk. Insurers generally understand that liability risk correlates to human presence and movement. A hair salon with twice the foot traffic should present twice the slip-and-fall risk.

ITL FOCUS is a monthly initiative featuring topics related to innovation in risk management and insurance. Cognitive computing is a funny beast. Every time you hit your target, you find that another pops up off in the distance. When I first saw a demonstration of speech recognition, some 30 years ago, I was mightily impressed that the computer understood a few words. If I had seen what would be possible today, I'd have been stunned.

Here are five priorities for insurance leaders to consider, drawing on insurtech-driven solutions. Carriers, regulators and solution providers in the insurance industry continue to seek innovative solutions to some of the most pressing issues facing the industry. Priority 1 – Global risk: Even before the pandemic, volatility in global risks stemming from climate change, economic uncertainties, cyber attacks and more were at the forefront of long-term concerns for insurance providers. The pandemic has accelerated those debates, bringing both urgency and added complexity to forecasters and those evaluating the macro and micro impact of those risks. Insurtech Response: Better understanding of weather volatility from storms, drought/wildfire, etc., has long been a focus in the insurtech sector, with firms such as Hazard Hub, Opterrix and Athenium Analytics, to name a few, giving insurers greater insights for underwriting and claims.

When automation is a core technology, transformation can occur at speed, meaning faster return on digital investment. Insurers that are heavily dependent on traditional business models have struggled over the past year with financial strain. But automation presents an opportunity to establish short-term successes to speed up recovery and long-term results that can stimulate growth by accelerating digital transformation. Customer expectations are at an all-time high as digital-native brands like Amazon continue to dominate and set standards that are difficult for other companies to copy. These standards don't only appear in business-to-consumer transactions but also in business-to-business transactions that include promoting product innovation and mix.