Deep learning in healthcare can uncover the hidden opportunities and patterns in clinical data, helping doctors to treat their patients more efficiently. Artificial Intelligence, machine learning and deep learning have gained a lot of attention for quite some time now. These technologies are revolutionizing various industries such as retail, finance, travel, manufacturing, healthcare, and so on. Healthcare is an important industry that implements these technologies. As health is a priority, medical experts are continually trying to find ways to implement new technologies and provide impactful results.

While I do think it presents an existential threat to some lawyer jobs -- specifically those doing low-skill tasks as part of Biglaw behemoths -- when a company told me several years ago that they would license AI based off the brains of famous attorneys within the decade I went right ahead and laughed. But then we started talking about some of the cool technology Casepoint is bringing to the party and discussed how the system's ability to break down all the data and map out the connections for itself and build a real story of events. Hurt feelings aren't necessarily a fraud claim and keeping the presumption in favor of the four corners of the document can dissuade cases that -- even if they're true -- would be difficult to prove because we can't reliably muster the whole life cycle to sort out in litigation. Would judges eyeing a motion to dismiss start to balk at the risk of missing a fraud when the costs of getting at the whole story in discovery isn't prohibitive? Transactional lawyers might need to think about the well-worn language to avoid an influx of fights if this is the sort of material that a party could easily compile in a dispute.

Nigel Cade, managing director of The Insurance Claims Service Centre, explained to Insurance Business the innovative ways in which Artificial Intelligence (AI) can be used to help detect and prevent fraudulent claims, but also that a'fraud fighting culture' needs to be present to properly utilise the software. "There is often a zero-tolerance policy, but not necessarily a fraud fighting culture in place," Cade explained. "I don't think that the industry is yet to deal that well with fraud at all." Cade spoke to Insurance Business in anticipation of his presentation on the topic at the TechFest in May. Fraud, he said, was still a pressing issue that the insurance industry must grapple with. "It's huge – it's a huge problem still," he said.

The insurance industry is one of the oldest and most critical industries in the world. 'Trust' is the most important currency in this industry and hence insurance was always a people-intensive industry. Whether it is an agent who sells policies or surveyor who assesses claims, insurance companies always had to rely on people to build customer trust. A large part of claim assessment is to monitor'fraud claims' – and until recently, the only way to assess claims was to manually investigate on a case by case basis. With the advent of technology, there is a big disruption coming to the insurance industry.

Data science and statistics are not magic. They won't magically fix all of a company's problems. However, they are useful tools to help companies make more accurate decisions and automate repetitive work and choices that teams need to make. Machine learning and data science get referenced a lot when referring to natural language processing, imaging recognition and chat bots. However, they also can be applied to help managers make decisions, predict future revenues, segment markets, produce better content and diagnosis patients more effectively. Below, we are going to discuss some case examples of statistics and applied data science algorithms that can help your business and team produce more accurate results. This doesn't require complex hadoop clusters and cloud analytics. Just, let's get the basics going first! Before we jump to far down the rabbit hole of technology and hype!