The Banking and Finance sector (BFSI) is witnessing one of its most interesting and enriching phases. Apart from the evident shift from traditional methods of banking and payments, technology has started playing a vital role in defining this change. Mobile apps, plastic money, e-wallets and bots have aided the phenomenal swing from offline payments to online payments over the last two decades. Now, the use of Artificial Intelligence (AI) in BFSI is expediting the evolution of this industry. AI enables a computer to behave and take decisions like a human being.

Artificial intelligence (AI) can be understood as a science, engineering and deployment of machines, which perform tasks with intelligence as similar to humans. Since its inception 60 years ago, AI has observed significant growth in recent years. Initially, AI was considered as topic for academicians, though in recent years with development of various technologies, AI has turned into reality and is influencing many lives and businesses. Additionally, evolution of various other supplementary technologies such as cloud computing, machine learning and cognitive computing are collectively paving the growth of the market for AI. Many IT giants and start-ups are investing heavily in development of AI software solutions and hardware products. Some the prominent players in AI market in the recent times are Intel Corporation (U.S.), Google Inc. (U.S.), Microsoft Corporation (U.S.), Amazon.com,

With the explosion of Big Data and Analytics there are several related terms that are being used frequently that may not be completely understood. Below are definitions and details for a few of the high profile terms. Predictive Analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends as defined by Webopedia. Predictive analytics provides probabilities of results not guarantees. Predictive Analytics of one sort or another has been done for decades through tools like SAS and SPSS. There are more contemporary solutions as exemplified by companies like Alpine Data Labs and KNIME.

Over the last two years, we have highlighted deep learning use cases in enterprise areas including genomics, large-scale business analytics, and beyond, but there are still many market areas that are still building a profile for where such approaches fit into existing workflows. Even though model training and inference might be useful, for some areas that have complex simulation-driven workflows, there are great efficiencies that could come from deep neural nets, but integrating those elements is difficult. The oil and gas industry is one area where deep learning holds promise, at least in theory. For some steps in the resource discovery workflow, deep learning could lead to faster and more accurate results for potential discovery zones. Reservoir characterization is a critical step in this discovery process and is currently a hot area for explorations into how deep learning might be applied.

Facebook chatbots are one application of this revolution, as they rapidly gain popularity and provide a new tool for marketers to leverage. These chatbots are the incorporation of automatic chatbots within Facebook Messenger. Chatbots offer flexibility in order to automate tasks, and assist in retrieving data. They are becoming a vital way to enhance the consumer experience for the purpose of better customer service and growing interaction. In April 2016, Mark Zuckerberg announced that third parties could use the messenger platform to create their own personal chatbot.

Security remains one of the top unresolved challenges for businesses. Billions of dollars have been spent on security technology over the last 30 years, yet hackers seem to be more successful than ever. Every organization is now under extreme threat, all the time. Today, hacking is a much more complex art than it used to be: It no longer only involves just scanning and penetrating the network via a vulnerability. Yet the traditional security tools used by most companies are often inadequate because they still focus on this, ignoring what is now a very complex post-compromise chain of events.

Amazon has just launched a retail experience like no other. Customers are now, thanks to AI technology, able to walk in, grab what they want and walk out. And, while still in the beta phase of testing, Amazon Go is set to shake things up on a number of levels including business. Tim Dunlop of The Guardian says it's confirmation that we're moving from a globalized world of manufacturing giants to a networked one of technology giants. So just what does Amazon's Go mean for business?

Over 1,000 attendees, 50 speakers and 30 exhibitors; this is a brief summary of what I was lucky enough to take part in during the first AI Europe 2016 conference held in London on the 5 and 6 December. The attendee list boasted the biggest names from the world of artificial intelligence such as Microsoft, Dell, Uber, Samsung and Nvidia, as well as several innovative start-ups, the likes of Blippar and DreamQuark whose innovations are based on machine or deep learning models. Even if we can say with a degree of certainty that further advances in artificial intelligence are yet to come, leading players are in agreement that most AI techniques and technologies are now well-advanced. Therefore, their major preoccupation today is more about the quality of the data sets being used to train and validate their machine and deep learning models. Whether it's Dell or Uber, Microsoft or Blippar, they all have one thing in common: they all agree on the fact that as of now, the quality of the data used in AI for machine learning is of the utmost importance.

Every year I pick out digital and marketing trends and developments which I think will shape the industry and its planning and thinking in the year ahead. There is an increasingly blurred line between'digital marketing' and'marketing' but the following trends focus on the digital elements of marketing. In part 1, I looked at broad macro trends affecting brands, and in part 2, marketing-specific trends. Econsultancy's recent research on'The New Marketing Reality' with IBM highlights the many challenges facing digital marketing: fragmentation, complexity, challenges in understanding the customer journey, challenges with organisational and data silos, confusion around metrics and what good looks like, managing both generalist and specialist agencies and vendors at the same time, lack of capability in areas like data and customer experience, faltering attempts to be more agile, lack of clarity in strategy and leadership. There is nothing particularly new here and there will not be in 2017.

Ask any marketer what's on their to-do list in 2017, and they'll tell you they have a project underway to achieve a 360-degree view of the customer, Tealium's global CEO, Jeff Lunsford, says. "Any marketer is going to be looking to pull in data about that customer or a prospect from the myriad points where data is available in this new world," he says. "This could be IoT, mobile devices, or customer care. "Every marketer will nod yes, they want to leverage all the data they possibly can. So there's vision sync across the industry, the question is, how to do that." Tealium is one of a growing number of vendors looking to provide that answer with its Universal Data Hub, a software solution aimed at addressing data fragmentation for marketers across online and offline channels. The platform brings together the vendor's AudienceStream and DataAccess solutions with its iQ foundational technology. Since launching six years ago, Tealium has spent several years integrating its offering with more than 1000 applications across the marketing ecosystem, and recently raised another US$35m in capital, off the back of increased investment earlier in 2016, bringing total funding to $112.9m. Tealium now has 750 enterprise customers globally, from small digital-first companies to the largest, mature organisations. Australian clients include Cronulla Sharks, Nude by Nature, Greenstone Financial, and Melbourne University, while Asia-Pacific clients include Cathy Pacific. Speaking to CMO during a visit to Australia this week, Lunsford described Tealium as the "neutral layer down the stack of the marketing cloud", and the common management component organisations need in order to be able to exchange data across multiple best-of-breed systems in real time. Rather than competing with the large marketing cloud providers, he sees Tealium's role as being a complementary component. Not surprisingly, Lunsford sees technology as providing the foundational layer marketers need across customer touchpoints to pull that 360-degree vision off. "Companies use multiple software applications to create the customer experience, each has its own idea of the customer, and most don't talk to each other," he says. "The average Tealium customer has 26 software applications that contribute to the customer experience.