In an earlier article entitled "The unquenched longing for a transformed KYC-AML solution" I had talked about the key challenges that financial institutions (FIs) have been facing with regards to their current Know Your Customer (KYC) and Anti-Money Laundering (AML) operations. In order to overcome these considerable and lingering challenges, it has now become imperative that FIs leverage new-age smart technology solutions. In this regard, I believe innovative artificial intelligence (AI) and machine learning (ML) enabled solutions can be a game changer for FIs. Artificial intelligence (AI) allows IT systems to imitate the cognitive ability of human – for example "problem solving", "reasoning", "planning" and "learning". AI enabled systems possess inbuilt intelligence to sift through, aggregate, blend, and identify patterns and relationships that are buried within mountains of data - spanning large number and types of data sources.

Today, Financial Institutions (FIs) face significant legal and reputational risks when it comes to complying with anti-money laundering (AML) requirements (including anti-terrorist financing and obligations to conform). Failure can lead to serious sanctions imposed by regulatory bodies (Recently, Societe Generale fined $5.83 MM for a number of shortcomings in its control for preventing money laundering). Today's financial markets are truly global. Transactions and flow of funds take place through a web of interactions across nations and systems. This makes it difficult to be compliant with thousands of regulations and norms across a large number of jurisdictions.

At some online publications, financial summaries and sports recaps are written by artificial intelligence (AI), not humans. In the medical field, thanks to "computer-assisted diagnosis," a computer was able to spot 52% of breast cancers based on mammography scans up to one year before the women were officially diagnosed. In some organizations, AI decides which sales opportunities are worthy of a salesperson's time. Gartner client inquiry on topics closely related to AI tripled from 2015 to 2016. As organizations recognize the potential for AI to disrupt business, interest is growing rapidly.

Using machines to read text as a way to enhance understanding of market movements is a topic of intense polarisation and debate. Back in the 90s, work on natural language processing (NLP) involved teams of linguists and computer scientists attempting to code up rules of grammar. Recent work has focused on techniques like word embedding, the underlying idea that a word is characterised by the company it keeps; semantic similarities between words are based on their distribution in large samples of data. The "bag of words" approach has been applied commercially in finance for more than 10 years. But it can depend on the source of information being analysed: a rule-based approach can work pretty well for news articles that follow certain editorial processes, while social media proves much more challenging.

Artificial intelligence is all the rage in IT these days, with vendors rushing out new products and trying to assure CISOs that their products include some element of machine learning. But how much of this is hype? Quite a bit, cautions Oliver Rochford, vice-president of security evangelism at DFLabs, in a column this week. " Machine learning by itself solves nothing without being applied to distinct problems," he writes. So what's a CISO to do? Ask a few intelligent questions, Rochford advises.

Price cuts on staples at Whole Foods (WFM) are aimed at boosting traffic at rivals' expense This article is being republished as part of our daily reproduction of WSJ.com articles that also appeared in the U.S. print edition of The Wall Street Journal (August 28, 2017). Inc. (AMZN) will bring lower prices to its new Whole Foods Market Inc. It also will bring a new rule book. While Amazon doesn't need to make money from its grocery division yet, food sales are crucial for traditional players like Kroger (KR), Wal-Mart Stores Inc. (WMT) and Target Corp. (TGT) The extremely competitive food-retail business demands high capital investments for low margins. Supermarkets' success has mainly relied on getting customers into conveniently located stores with deals.

There is a growing interest among businesses concerning the idea of integrating AI or Artificial Intelligence into daily operations. After all, AI has proven to be extremely beneficial. It improves efficiency and generates far better results. Now, the pace at which AI is being adopted in the financial sector can be best described as slow. However, that does not mean it isn't happening.

John Taylor, a Stanford University economist famous for the "Taylor Rule" that lays out a rules-based approach to setting monetary policy, participated in all the conference discussions. Also present was Glenn Hubbard, dean of the Columbia University Business School, who chaired the Council of Economic Advisers in the George W. Bush administration and has often been mentioned when the Fed's top job has come open. In his July interview with the Wall Street Journal, Trump said he had other candidates in addition to Yellen and Cohn but he declined to name them.

Machine learning opens the door for the use of training rather than programming in game development. Game development is a complex and labor-intensive effort. Game environments, storylines, and character behaviors are carefully crafted, requiring graphics artists, storytellers, and software engineers to work in unison. Often, games end up with a delicate mix of hard-wired behavior in the form of traditional code and a somewhat more responsive behavior in the form of large collections of rules. Over the last few years, data intensive machine learning (ML) solutions have obliterated rule-based systems in the enterprise--think Amazon, Netflix, and Uber. At Unity, we have explored the use of some of these technologies, including deep learning in content creation and deep reinforcement learning in game development.

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.