Business Law


Insights from a Regulator into the application of Machine Learning and AI to enhance compliance

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Scott W. Bauguess is the Acting Director and Chief Economist at the Division of Economic and Risk Analysis ("DERA") from the US Securities and Exchange Commission ("SEC"). He recently gave a Keynote speech: "The Role of Big Data, Machine Learning, and AI in Assessing Risk: A Regulatory Perspective", that many people missed. In it he covered how the SEC is taking advantage of these technologies, and more importantly where they are not, at least not yet. First, let me agree with Scott W. Bauguess that the use of machine learning has great benefits. We see it with our clients every day.


5 Factors to Consider Before Exploring AI in Fraud Prediction - Corporate Compliance Insights

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In the field of fraud prediction, with transactional fraud raising every day; investors, board, management and business heads are keen to look at possibilities of detecting fraudulent transactions before they pass through the system. Machine learning algorithms bring efficiency in identifying potentially fraudulent transactions. Certain factors require critical consideration while adopting machine learning for fraud prediction. Through this article find out how these factors are vital in making an effort towards creating machine learning driven fraud prediction meaningful. Artificial intelligence has evolved into mainstream businesses over the past few years.


Nuance reportedly acquires Voicebox Technologies

ZDNet

Nuance Communications has acquired Voicebox Technologies, GeekWire reports. Nuance's most recent Securities and Exchange Commission (SEC) filing suggests the speech recognition company paid $82 million for Voicebox. Voicebox, which is based in Bellevue, Washington, has developed a VoiceAI platform that delivers a multi-person, cross-device experience. The platform is used in home and IoT devices, as well as connected cars. Its partners include Toyota and Samsung.


Technology Driven Compliance

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The Sarbanes-Oxley Act of 2002 (SOX) changed the way the U.S. looked at managing risk and compliance in the investment banking technology space. It is hard to believe that we soon will see the 15th anniversary of SOX. The Dodd-Frank Wall Street Reform and Consumer Protection Act, meanwhile, is in its sixth year of existence. While many changes have occurred in the technology landscape since 2002, one element has been constant: risk and compliance remain at the core of the banking industry as institutions continue to grow and deliver new, complex service offerings in this highly regulated environment. "With the emergence of artificially intelligent technologies, we must be mindful that these are tools and strategies that can be used to manage risk" As the scope of compliance continues to increase, the technology used to support this expansion also has an opportunity to grow.


Banks are already bumping up against the limits of AI in lending decisions

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While big tech companies might not face regulation of their artificial-intelligence efforts in the US, banks trying to use AI still have to contend with reams of industry-specific rules, including laws ensuring equal treatment of customers. According to Bank of America technology executive Hari Gopalkrishnan, that's a problem for banks interested in using deep learning, the technology responsible for the current AI boom. That's because the decisions made by deep learning can be difficult to interpret--the "why" behind everything the algorithm does is a bit of a mystery. In banking, "[w]e're not fans of lack of transparency and black boxes, where the answer is just'yes' or'no,'" Gopalkrishnan said at a company tech summit. "We want to understand how the decision is made, so that we can stand behind it and say that we're not disfavoring someone."


Vodafone to Buy Liberty Global's European Assets

WSJ.com: WSJD - Technology

The roughly €19 billion deal would face a possibly lengthy European Union antitrust review, but if completed, would create one of the continent's biggest telecommunications operators, selling the industry's holy grail "quad-play" package: cable, internet, wireless and landline-phone service on a single bill. The Financial Times reported earlier Tuesday the two companies were nearing a deal. The deal would represent the latest in a global trend of wireless carriers acquiring cable operations, or vice versa, to offer quad-play packages. Wireless carriers need high-speed cable networks to quickly transmit data to cellular towers for 5G, the coming generation of mobile networks that promise to be fast enough to enable near-instantaneous movie downloads and innovations such as self-driving cars. Both companies have said they have engaged in various forms of merger talks with each other in recent years.


Applying Machine Learning to SEC Filings to find Anomalous Companies

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Contemporary machine learning algorithms are well-suited to the complex, high-dimensional data associated with accounting records. In this short note we apply a simple unsupervised algorithm to find anomalous companies -- those with accounting metrics that don't match the statistical patterns implied by the bulk of the companies. To do this we leverage the SEC structured financial statements data set, a regularly updated collection of the machine-readable numeric core of the financial disclosures regularly filed to the SEC through its EDGAR system. We use the reported company assets as a normalizing factor; while size is of course a variable of interest, we are looking for less obvious, scale-independent patterns and anomalies. Note that axis and values in the graph above are in many ways arbitrary; it's simply a reasonable effort at representing in three dimensions the relative distances between points in the six-dimensional data space for the company fillings.


How a strong board of directors keeps AI companies on an ethical path

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Following the corporate corruption scandals of the early 2000s, then-Securities and Exchange Commission chairman William Donaldson said determining the company's moral DNA "should be the foundation on which the Board builds a corporate culture based on a philosophy of high ethical standards and accountability." Today's crisis of confidence in technology companies, especially those controlling deep pools of data and developing and deploying artificial intelligence, not only demands more responsible engineers, entrepreneurs, and executives but more assertive boards who make ethics and the public interest strategic priorities. Board of directors' responsibilities include hiring, firing, and holding the CEO's feet to the fire, as well as approving and overseeing the company's strategy and ensuring the integrity of company financials. Boards must also set a tone at the top of ethics and responsible business practices. Norms and standards are still emerging; laws, regulations, and legal precedent are scarce; and pressure groups are still finding their voice and translating concerns into actionable demands.


The world's most valuable resource is no longer oil, but data

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A NEW commodity spawns a lucrative, fast-growing industry, prompting antitrust regulators to step in to restrain those who control its flow. A century ago, the resource in question was oil. Now similar concerns are being raised by the giants that deal in data, the oil of the digital era. These titans--Alphabet (Google's parent company), Amazon, Apple, Facebook and Microsoft--look unstoppable. They are the five most valuable listed firms in the world.


The Amazing Ways Google Uses Artificial Intelligence And Satellite Data To Prevent Illegal Fishing

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Google services such as its image search and translation tools use sophisticated machine learning which allow computers to see, listen and speak in much the same way as human do. Machine learning is the term for the current cutting-edge applications in artificial intelligence. Basically, the idea is that by teaching machines to "learn" by processing huge amounts of data they will become increasingly better at carrying out tasks that traditionally can only be completed by human brains. These techniques include "computer vision" – training computers to recognize images in a similar way we do. For example, an object with four legs and a tail has a high probability of being an animal.