Suspected money laundering enterprises are in the headlines again, ensnaring the state-controlled Industrial and Commercial Bank of China as well as . Yet for financial institutions that want to identify, track and stop money laundering and other financial crimes, the technological tools of banking's yesteryear no longer cut it. A new wave of tech tools can help banks and other financial firms discover dispersed and complex webs of illegal activity. Increasingly, financial firms are turning to artificial intelligence and sophisticated network analysis to detect such criminal enterprises. The legacy tools banks use to find suspicious dispersals of money often rely on logical "if-then" rules to spot criminal activity, as Wired reports.

Like many industry buzzwords, Artificial Intelligence (AI) has become a hot topic that RegTech technologists often write or speak about. But the reality is – AI has become an overloaded and misused term, often mistaken for Machine Learning (ML). This blog aims to clarify the difference between the two, explain some of the complexities of implementing these solutions today, and highlight how ML can immediately add value in financial compliance applications. In simple terms, Artificial Intelligence enables computer systems to perform tasks that require human intelligence. Intelligence is the key word.

The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to retrieve (by derivation) multiple statements (sometimes assumed) or results from the literature.

Before breakfast, I check my Facebook and LinkedIn newsfeeds for a quick synopsis of the day. As I jump in the shower, I hit "download" on a recommended movie on Netflix, knowing I have a long flight this evening. While wolfing down my cereal, I click once to buy a gift for a friend's birthday next week. My iPhone pings to tell me that I need to leave now if I want to make that early meeting 54 miles away. And as I get in my car, I use voice activation to play my favorite Spotify playlist, and Apple Maps informs me it will take five minutes to drive to the train station this morning.

We typically think of artificial intelligence (AI) within our industry in terms of processes and calculations. Media buying, for example, is ripe for intervention by sophisticated algorithms and machine learning systems. The commonly-held assumption is that developments such as these will free up humans to spend more time on creative tasks, like campaign strategy and content production. But as we move from rule-based automation to true AI, should we believe that creativity will remain a singularly human pursuit? How close is artificial intelligence to being able to carry out the role of a content marketer?

Amazon Prime Day (APD) was a huge success, they say. At an estimate 60 percent increase in sales over 2016 and nearly $2 billion in revenue, it's hard to argue otherwise. If you want to talk numbers though, let's consider this. What would you say if you were told that Amazon could lose nearly 5 percent of that revenue, or $100 million, due to fraud? And it's not just Amazon on its Prime Day, it's every online retailer that is exposed to online fraud every single day.

Over the course of the last few decades, the gaming industry has seen great strides. Beginning with simple games like Pong* and Pac-Man* which offered players a short escape from reality and growing into such involved games like World of Warcraft* and Call of Duty 4* which are serious hobbies to those that play them. Today's gamers, who according to the Entertainment Software Association (ESA) have an average of 13 years of gaming under their belt, have grown accustomed to seeing each new game become increasingly complex, engaging, and intelligent. For developers, the challenge becomes pushing the envelope to create games that are increasingly compelling. Computer-controlled Artificial Intelligence (AI) has evolved in many forms to meet the test.

We typically think of artificial intelligence (AI) within our industry in terms of processes and calculations. Media buying, for example, is ripe for intervention by sophisticated algorithms and machine learning systems. The commonly-held assumption is that developments such as these will free up humans to spend more time on creative tasks, like campaign strategy and content production. But as we move from rule-based automation to true AI, should we believe that creativity will remain a singularly human pursuit? How close is artificial intelligence to being able to carry out the role of a content marketer?

Instances of reported cybercrime are growing astronomically – and yet many successful attacks are still not reported, or even detected. In response to the escalating threat, detection capabilities are constantly being refined, improved and almost fully re-imagined. As new threats arise, so do technologies that offer a control against these threats. Automating the process without compromising the accuracy or effectiveness of the measures helps to augment the role of a human in security operations. The automation wave is the progression of technology and machine learning into intelligent software that can act to both identify and remediate incidents, leaving security professionals to tackle more complex and relevant issues.

AI experts take much less punishment than NFL quarterbacks do, and some earn just as much, apparently. Forget all the hype around artificial intelligence (AI), if you've really got the skills that Silicon Valley and Wall Street need, you could haul in millions of dollars a year. "Right now, AI is an elitist sport – there are very few people who know how to practice it," said Tom Eck, the CTO of industry platforms at IBM and a software developer who has been involved in AI as far back as the early '90s. "The top-tier AI researchers are getting paid the salaries of NFL quarterbacks, which tells you the demand and the perceived value." While Eck, speaking at the Markets Media's Summer Trading event in New York, didn't specify whether he meant starting quarterbacks or their backups, he did say that the best AI researchers are earning as much as the highest paid position in the National Football League.