Large data and fast computers mean that new and different things can be discovered from large datasets of text by writing and running software. In the 1990s, statistical methods and statistical machine learning began to and eventually replaced the classical top-down rule-based approaches to language, primarily because of their better results, speed, and robustness. The statistical approach to studying natural language now dominates the field; it may define the field. Data-Drive methods for natural language processing have now become so popular that they must be considered mainstream approaches to computational linguistics.

Machine learning and artificial intelligence have become buzzwords in the financial services industry. Daniel Fernandez helps to break down the difference between the two terms and explains how these technologies are being used by compliance departments today. By delving into how these technologies work, Daniel sheds light on the issues they can help to solve, the challenges facing their increased adoption and what financial institutions should be doing right now to take advantage. Like many industry buzzwords, artificial intelligence (AI) has become a hot topic that RegTech technologists often write or speak about. But the reality is this: AI has become an overloaded and misused term, often mistaken for machine learning (ML).

For years, video game developers have used artificial intelligence to animate those characters encountered by a player, but non-playable characters, or NPCs, have been based on sets of rules coded by humans. Using the AI technology du jour, machine learning, future NPCs will program and reprogram their own rules, based on the experiences they encounter in games, in the process getting smarter the longer they play. So says Danny Lange, the VP of AI and machine learning at Unity Technologies, a major maker of game "engine" software that handles the underlying mechanics of titles like Firewatch and ChronoBlade. Today the company announced Unity Machine Learning Agents--open-source software linking its game engine to machine learning programs such as Google's TensorFlow. It will allow non-playable characters, through trial and error, to develop better, more creative strategies than a human could program, says Lange, using a branch of machine learning called deep reinforcement learning.

NEW YORK – The foreign ministers of Japan, the United States and India agreed Monday in New York to work together to develop strategically important ports and other infrastructure in the Indo-Pacific region, apparently seeking to balance China's bid to strengthen its regional influence. Foreign Minister Taro Kono said he, U.S. Secretary of State Rex Tillerson and Indian External Affairs Minister Sushma Swaraj "completely agreed to coordinate with each other toward the realization of a free and open Indo-Pacific." They agreed to work to spread and establish their shared basic values of the rule of law and the freedom of navigation and overflight in the region, Foreign Ministry officials said. The ministers affirmed that they will strengthen connectivity in the region through investment in infrastructure and work together to assist strategically important coastal nations in the region with maritime capacity-building, centering on key ports. According to the U.S. State Department, the ministers "discussed the importance of a free and open Indo-Pacific region underpinned by a resilient, rules-based architecture that enables every nation to prosper."

Machine learning is coming to auto finance, bringing with it the potential to revamp traditional lending approaches and stimulate sales volumes. It remains early days but some industry players see considerable potential…. This content is available only to members of Automotive World with a valid subscription.

Since the introduction of 3-D Secure in 2007, adoption rates have been slow, and have varied between countries. Concerns about lower conversions and rising customer friction are preventing higher penetration rates, but new fraud prevention software solutions offer ways to balance customer satisfaction and security requirements. Roger Lester, Payments Expert at Featurespace, explores how advanced machine learning and adaptive behavioral analytics technology help balance 3-D Secure checks against futureproofing an organization's fraud defenses. Why has adoption of 3-D Secure been mixed? Designed to be an additional layer of security for online payments, the 3-D ("3 Domain") Secure protocol has been adopted by all major card networks--more familiar to many of us under commercial names, such as "Verified by Visa" and "MasterCard SecureCode."

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.