The words "artificial intelligence" might make you think of movies like The Terminator. Machines that learn like humans can be a frightening thought, especially if they look like Arnold Schwarzenegger and roam through Los Angeles wearing dark sunglasses and shooting machine guns. But in real life, artificial intelligence has far more practical applications and is poised to forever transform insurance underwriting. Imagine workers' comp underwriters quickly analyzing thousands of pages of medical bills and health records to predict injury risks. Imagine commercial property underwriters seamlessly incorporating external data from city governments, regulatory agencies and news sources to get a clearer picture of a risk.
As Singapore currently has one of the highest rates of card fraud in the world, in order to combat this, in 2017 Singapore's financial industry came together to commit to data analytics as a means of fighting financial crime. As such, Featurespace's technological platform uses machine learning to detect anomalies in individual behaviour for fraud and risk management, and it was developed by computer scientists in the laboratories of Cambridge University. The real-time ARIC platform uses Adaptive Behavioural Analytics to self-learn and continuously respond to new customer data. By understanding the behaviour of each individual banking and credit card customer, ARIC identifies new and known attacks, while blocking fraud at the moment it occurs. ARIC reduces false positives, by 70%, increasing revenue, and reducing customer friction.
The rewards of machine learning can be compelling, and it may make you want to get started, now. At the same time, however, you'll want to consider machine learning challenges before you start your own project. This article isn't meant to scare you away; rather, it's meant to ensure you're prepared and that you're carefully thinking about what you'll need to consider before you get started. We spoke with Brian MacDonald, Data Scientist on Oracle's Information Management Platform Team, about the pitfalls he's seen and what companies can do to avoid them. The biggest difficulty, of course, is the skills gap that lies with using machine learning in a big data environment.
These are just a few ways the world's top researchers and industry leaders have described the threat that artificial intelligence poses to mankind. Will AI enhance our lives or completely upend them? There's no way around it -- artificial intelligence is changing human civilization, from how we work to how we travel to how we enforce laws. As AI technology advances and seeps deeper into our daily lives, its potential to create dangerous situations is becoming more apparent. A Tesla Model 3 owner in California died while using the car's Autopilot feature. In Arizona, a self-driving Uber vehicle hit and killed a pedestrian (though there was a driver behind the wheel). Register for the live briefing to find out about the top AI trends expected to reshape industries and economies this year. Other instances have been more insidious. For example, when IBM's Watson was tasked with helping physicians diagnose cancer patients, it gave numerous "unsafe and incorrect treatment recommendations." Some of the world's top researchers and industry leaders believe these issues are just the tip of the iceberg. How might that redefine humanity's place in the world?
Love it or hate it, technology is well and truly embedded into our lives. Investing in new tech isn't an option anymore, it's a necessity – especially when it comes to the workforce. A recent report from Brandon Hall Group found that 41% of employers want a better system of reporting on HR data, whilst 45% want to be able to alleviate the burden of manual tasks from HR's shoulders. With that in mind – it's time for practitioners to start embracing these digital changes, rather than shying away from them in abject fear. We spoke to Robert Childs, EVP HR Advisory Services & Capabilities at American Express, who revealed what new tools he'll be focusing on in 2019.
Never before has the importance of technology been greater in financial services. Competition from fintech firms and big tech giants, increased expectations from the consumer, and new innovations connecting data to digital delivery are requiring banks and credit unions to embrace new technologies in order to build winning strategies. Here are some of the most important technologies banks must focus on this year and in the foreseeable future. These are in no particular order, since each organization will be different as to the prioritization and investment allocation. Suffice it to say, however, than none should be ignored.
These days, you hear a lot about machine learning (or ML) and artificial intelligence (or AI) – both good or bad depending on your source. Many of us immediately conjure up images of HAL from 2001: A Space Odyssey, the Terminator cyborgs, C-3PO, Data from Star Trek, or Samantha from Her when the subject turns to AI. And many may not even be familiar with machine learning as a separate subject. The phrases are often tossed around interchangeably, but they're not exactly the same thing. In the most general sense, machine learning has evolved from AI. In the Google Trends graph above, you can see that AI was the more popular search term until machine learning passed it for good around September 2015.
As 2019 gets underway and your marketing plan unfolds, you've probably set some goals for the coming year: We're going to break down the data silos that keep us from understanding our customers. We're going to improve our messaging relevance. We're going to target customers more accurately on their preferred channels What if you could just find the time to make any one of these resolutions a reality? Although the promise of one-to-one marketing has been around for many years, brands still send customers too many marketing messages that are irrelevant, generic or only slightly personalized. The problem is that marketers today have too much data and not enough creative time to respond to soaring customer expectations for a personalized buying experience.