Articles - Artificial Insurance: Can AI live up to the hype?

When you think of artificial intelligence (AI) what is the first image that come to mind? For many, the associations will be from the realms of science fiction films, robots that look, behave and sound like humans or ‘personal assistants’ that inhabit our smartphones and tablets such as Amazon’s Alexa or Apple’s Siri.

 By Don Schuerman, CTO, Pegasystems
 What this tells us is that the majority think of AI as something that’s distant and futuristic or relatively new. The reality is very different, with artificial intelligence so much broader than sci-fi robots and virtual assistants. It refers to the process of making computers and machines capable of human-like, intelligent behaviour and, in this context AI has been widely used for more than three decades in a range of technologies, from machine learning analytics and data driven adaptive algorithms to advanced business rules engines.

 Whatever form it takes, AI has the capacity to excite as it plays on the imagination from ideas expressed in science fiction.

 But enterprise IT vendors tend to talk too much about the parts of AI that deliver the least immediate value, which doesn’t make sense when you realise the tremendous value AI has already delivered.
 Today, AI is at the heart of some fundamental processes that affect profit and loss in meaningful ways and this applies to the insurance industry as much as many other industries focused on customer service and delivering value.

 Understanding the complexity
 As with virtually all technology, the actual speed at which things move from potential to reality is always overestimated, and the impact generally underestimated. As a very process and data-centric industry with a huge array of legacy IT, the insurance sector has many opportunities to exploit AI and robotics. But, successful customer-facing AI deployments are few and far between, partly due to the industry’s conservative nature and its attitude to managing risk - especially in areas where others in the industry have yet to dip their toes in the water.

 For example, many technology vendors are guilty of overhyping the reality and misunderstanding the complexity of actually deploying robotics. Robotic Process Automation (RPA) has been described as a panacea for all automation challenges. There is some truth here: there can be incredible value delivered quickly by using software robots to automate repetitive tasks, either directly assisting a human, such as an agent in a contact center, or in an ‘unattended’ fashion, such as using a robot to validate portions of a claim submission. But the true value of automation comes from delivering ‘end-to-end’ outcomes, which means orchestrating not just robots, but human tasks, business processes, and legacy systems in a way that cuts across business silos, not just automates within them.

 Humanise rather than mechanise
 Where insurance companies are gaining significant advantage is by automating the repetitive activities that customer service agents manage every day. Interestingly, it’s advanced technology, such as AI and robotics, that’s enabling organisations to become more ‘human’. As McKinsey, would say we’re ‘taking the robots out of the human’, so employees have more time to spend giving better service to their customers with processes become quicker, easier and more satisfying. And AI and robotics can empower customers who want to look after their own affairs, providing intelligent guidance and support where needed.

 What’s more, we can quantify the benefits realised by insurers who’ve implemented robotics to humanise interactions.

 Over 12 months, ROI can be as significant as 100%, so the benefits are felt early on and include:
 • 25 per cent reduction in average handling time (AHT) for calls into a contact centre
 • 30-35 per cent reduction in manual steps via automation and integration
 • 90% reduction in repetitive work relating to extracting data from multiple systems or spreadsheets, validation, de-duping, establishing context and deciding channels

 Smart insurers are putting these productivity gains to good use - redeploying resources from the back office, where robotics flourish, to the front office - enhancing Marketing, Sales, Service and Underwriting. This is where insurers must now focus their attention and budget - winning the right business, at the optimum price, from the right customer.

 Real relationships
 In the context of customer engagement, AI is all about personalisation. And, by this we don’t just mean personalising a letter, but something much more profound. It’s being able to define the ‘next-best-action’, based entirely on the unique mix of data held about each customer being engaged - making customers feel valued and looked after, while balancing the overall goals and risks of the business. So, while the sci-fi view of AI concentrates on neural networks and machine learning to think like humans, the real work is happening in the world of data science, with Decision Management enhancing interactions with customers. For example, a customer service agent will be guided to the ‘next-best-action’ to recommend the right service process for a customer; a sales person will be guided to the right steps to take to advance a deal; a marketing engine will put a personalised and relevant offer in front of a customer. This ensures that every interaction with a customer is optimised to deliver value to both the customer and business.

 It’s perhaps not as exciting as sci-fi and robots, but the insurance industry is benefiting from AI in real and measurable ways. Robotics is becoming embedded in everyday business processes, revolutionising the potential for more efficient and effective customer service in critical areas such as customer retention. Fears that robots are coming for our jobs are exaggerated, and the complete automation of the workplace will take far longer to arrive than predicted. Data Science can help actuaries build models to measure, predict and mitigate risk, but there will always be a significant element of human experience and judgement involved, and AI can’t do that just yet.

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