There is huge amount of noise currently about the use of artificial intelligence (AI) in the financial services sector. Every fintech application or new piece of banking software must be accompanied by bold claims about its use of AI, even though in many cases they are simply upgraded algorithms. It would certainly be beneficial if we had an AI application that could cut through the spin and present us with only what is relevant to the specific requirements of our job or organisation. In truth, such advanced filtering applications are already available. They are just one of the AI-driven applications that are going to transform the way customers view financial organisations, especially in the retail banking and lending sector. See also: Should financial experts fear the rise of artificial intelligence?

A continuous strive towards making payment faster and easier is embracing technology innovation, like never before, which at the same time is creating newer risk exposures for frauds and money laundering. As technology evolves and new form & channel of payment emerge, it creates newer loop-hole for previously unknown pattern of fraud to sneak peek. Initiatives towards Faster & Easier payments are keeping financial industry on its toe to safeguard payments originated anywhere & anytime. Rising trend of P2P and m-commerce are fueling growth in Real-time Retail Payment Systems (RT-RPS). The growth has been encouraging, with 18 countries now having a'live' RT-RPS system in place.

With each delivery cycle, measure the results, see the impact of AI, and use that to raise awareness with the C-Suite on how AI can transform operations. Many packaged cognitive services, such as optical character recognition, sentiment analysis, or speech to text, are very easy to implement. However, to leverage these appropriately, one must understand how each solution can fit within overall business processes and where it must be applied to accelerate intelligent automation. Beyond plug-and-play cognitive services, organizations can start to explore custom machine learning algorithms to create unique predictive models based on their business data. Accessing the technology to enable this is straightforward, but understanding an organization's unique data and process requirements to apply machine learning to maximum impact remains the biggest challenge.

The Electronics Entertainment Expo, or E3 for short, is the world's biggest video game trade show and is hosted every year to give industry watchers a look at what's on the horizon. As direct competitors in the console platform and software publishing spaces courting largely similar demographics, Microsoft (NASDAQ:MSFT) and Sony (NYSE:SNE) are often compared to each other. This article originally appeared in the Motley Fool. Putting on the best E3 conference isn't always a harbinger of overall success, but what's spotlighted at the trade show and how it's presented sometimes have a significant impact on the progression of the industry and each company's respective performance. So, who had the better show?

Establishing trust in data is an essential requirement for businesses and entities for whom credible, reliable information is the lifeblood. As enterprises seek to manage data as an asset, it becomes increasingly vital that data sources are trusted and verifiable. I wrote a few weeks ago about the MIT initiative to establish a framework for trusted data, and the resulting position paper, "Towards an Internet of Trusted Data: A New Framework for Identity and Data Sharing". The authors highlight the criticality and need for "trustworthy, auditable data provenance" where "systems must automatically track every change that is made to data, so it is auditable and completely trustworthy". One of the key recommendations of the study was to improve the process and quality of data sharing.

Establishing trust in data is an essential requirement for businesses and entities for whom credible, reliable information is the lifeblood. As enterprises seek to manage data as an asset, it becomes increasingly vital that data sources are trusted and verifiable. I wrote a few weeks ago about the MIT initiative to establish a framework for trusted data, and the resulting position paper, "Towards an Internet of Trusted Data: A New Framework for Identity and Data Sharing". The authors highlight the criticality and need for "trustworthy, auditable data provenance" where "systems must automatically track every change that is made to data, so it is auditable and completely trustworthy". One of the key recommendations of the study was to improve the process and quality of data sharing.

Establishing trust in data is an essential requirement for businesses and entities for whom credible, reliable information is the lifeblood. As enterprises seek to manage data as an asset, it becomes increasingly vital that data sources are trusted and verifiable. I wrote a few weeks ago about the MIT initiative to establish a framework for trusted data, and the resulting position paper, "Towards an Internet of Trusted Data: A New Framework for Identity and Data Sharing". The authors highlight the criticality and need for "trustworthy, auditable data provenance" where "systems must automatically track every change that is made to data, so it is auditable and completely trustworthy". One of the key recommendations of the study was to improve the process and quality of data sharing.

Artificial Intelligence or Machine Learning, as it's better known as, is something that has garnered my interest over recent years. I even used the technology to design a company logo for a side hustle project. The expanding uses for Artificial Intelligence in business is fascinating and only becomes more exciting to see what is in store as the technology continues to mature. After being in the business for 25 years I have learnt and experienced first-hand the ever-changing landscape in which marketing continues to grow. We have seen a huge change in the industry over the last ten years, with the most prevalent and revolutionary change being the utter dependence of the online sphere. I have grown my business from strength to strength in the online realm, with platforms such as Twitter being a core tool.

Technologically, the 20-year jump from 2015 to 2035 will be huge. During that time some elements of our world will change beyond recognition while others will stay reassuringly (or disappointingly) familiar. Consider the 20 years to 2015. Back in 1995 we were in the early days of the internet, we worked in cubicles and our computers were chunky and powered by Windows 95. There were no touch screen phones or flat screen TVs; people laughed at the idea of reading electronic books, and watching a home movie meant loading a clunky cassette into your VCR.

The US, China, Singapore, Greece and Japan – All have been gunning autonomous vehicles since years now. Now Israel is another country to join the club. Israel has become a focus for car technology in recent years. Large companies such as General Motors, Toyota, Skoda, Volvo, BMW, Honda, Hyundai and some others have built R&D centers in Israel to develop self-driving cars. Israeli startups don't find themselves much behind in this race of driverless tech.