Business Law


Big data and machine learning algorithms could increase risk of collusion: ACCC

ZDNet

The Australian Competition and Consumer Commission (ACCC) has provided an overview of its approach to potential future cases where machine learning algorithms are deployed as a tool to facilitate conduct that may contravene competition law. While the ACCC sees many economic advantages in "data-driven innovation" -- such as consumers being able to compare products online -- it also has a number of concerns, such as the possibility of such innovation increasing the risk of engaging in and sustaining collusion, and decreasing competition in the market without necessarily violating any competition laws. "Cases brought to date globally by competition authorities relating to the use or misuse of online databases to determine prices reflect circumstances where'something more' occurred," ACCC chair Rod Sims said at a conference in Sydney on Thursday. In the United States Airline Tariff case, a database that was accessible to travel agents was being used by airlines to negotiate supra-competitive airfares and ensure proposed price hikes were maintained, Sims said. "It is said that a profit-maximising algorithm will work out the oligopolistic pricing game and, being logical and less prone to flights of fancy, stick to it," he said.



how-artificial-intelligence-is-set-to-change-the-face-of-online-gambling

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Artificial Intelligence or AI is making revolutionary changes in most of the industries in the recent years. Almost every industry face the heat of AI, and many of them will get the benefit of it in future. Per industry experts, transportation, medicine, gambling, and Personal Assistance Services Industry would see most visible changes. Due to the transition of gambling to online mode, the technology would enhance the gaming experience to higher levels. It is great to know how AI is going to change the pace of online gambling.


Rise of the Machines Must Be Monitored, Say Global Finance Regulators

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Replacing bank and insurance workers with machines risks creating a dependency on outside technology companies beyond the reach of regulators, the global Financial Stability Board (FSB) said on Wednesday. The FSB, which coordinates financial regulation across the Group of 20 Economies (G20), said in its first report on artificial intelligence (AI) and machine learning that the risks they pose need monitoring. AI and machine learning refer to technology that is replacing traditional methods to assess the creditworthiness of customers, to crunch data, price insurance contracts and spot profitable trades across markets. There are no international regulatory standards for AI and machine learning, but the FSB left open whether new rules are needed. Data on rapidly growing usage of AI is largely unavailable, leaving regulators unsure about the impact of potentially new and unexpected links between markets and banks, the report said.


Rise of the machines must be monitored, say global finance regulators

#artificialintelligence

Replacing bank and insurance workers with machines risks creating a dependency on outside technology companies beyond the reach of regulators, the global Financial Stability Board (FSB) said on Wednesday. The FSB, which coordinates financial regulation across the Group of 20 Economies (G20), said in its first report on artificial intelligence (AI) and machine learning that the risks they pose need monitoring. AI and machine learning refer to technology that is replacing traditional methods to assess the creditworthiness of customers, to crunch data, price insurance contracts and spot profitable trades across markets. There are no international regulatory standards for AI and machine learning, but the FSB left open whether new rules are needed. Data on rapidly growing usage of AI is largely unavailable, leaving regulators unsure about the impact of potentially new and unexpected links between markets and banks, the report said.


Financial regulator warns on artificial intelligence risk

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The increased use of artificial intelligence in financial services could pose a risk to stability, according to an international regulator. The Financial Stability Board, which includes all G20 major economies, has published a report on the implications of the increasingly widespread use of machine learning and artificial intelligence in the financial sector. It has raised concerns about an "arms race" for the use of artificial intelligence, with financial services companies investing in it simply because their competitors are. While the report said there were likely to be benefits from this development, such as the more efficient processing of information and improved regulatory compliance, this was not without risks because of the potential emergence of new "systemically important" players. The report said: "AI and machine learning services are increasingly being offered by a few large technology firms.


FSB say rise of the machines must be monitored

Daily Mail

Replacing bank and insurance workers with machines risks creating a dependency on outside technology companies beyond the reach of regulators, the global Financial Stability Board (FSB) said. The FSB, which coordinates financial regulation across the Group of 20 Economies (G20), said in its first report on artificial intelligence (AI) and machine learning that the risks they pose need monitoring. AI and machine learning refer to technology that is replacing traditional methods to assess the creditworthiness of customers, to crunch data, price insurance contracts and spot profitable trades across markets. Replacing bank and insurance workers with machines risks creating a dependency on outside technology companies beyond the reach of regulators, the Financial Stability Board (FSB) said. There are no international regulatory standards for AI and machine learning, but the FSB left open whether new rules are needed.


AI in the Boardroom: The Next Realm of Corporate Governance

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Just as artificial intelligence is helping doctors make better diagnoses and deliver better care, it is also poised to bring valuable insights to corporate leaders -- if they'll let it. At first blush, the idea of artificial intelligence (AI) in the boardroom may seem far-fetched. After all, board decisions are exactly the opposite of what conventional wisdom says can be automated. Judgment, shrewdness, and acumen acquired over decades of hard-won experience are required for the kinds of complicated matters boards wrestle with. But AI is already filtering into use in some extremely nuanced, complicated, and important decision processes.


Corby - AI Core Banking Bot

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TekMonks – Global, Skilled and Successful 2 Vision Statement To be a reputable Global Corporation providing quality solutions for business issues using technology and highly skilled people. Any banking bot requires… • Integration into core banking systems • Needs a mature platform which can perform transactions • And a mature AI engine which can correctly understand transactional requests For decades, IBM mainframe and AS/400 systems have powered banking transactions. TekMonks – Global, Skilled and Successful 2 Vision Statement To be a reputable Global Corporation providing quality solutions for business issues using technology and highly skilled people. Any banking bot requires… • Integration into core banking systems • Needs a mature platform which can perform transactions • And a mature AI engine which can correctly understand transactional requests For decades, IBM mainframe and AS/400 systems have powered banking transactions.


Is Artificial Intelligence Ready for Financial Compliance? - Corporate Compliance Insights

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In simple terms, artificial intelligence enables computer systems to perform tasks that require human intelligence; intelligence is the key word. Today, ML is used in many narrow compliance applications, including risk detection models and other event classification use cases. Most artificially intelligent systems use a combination of machine learning applications and techniques along with rule-based systems (to be fully interactive). And this is a good thing, because while smart machines and complex algorithms can process a lot of data to automate and perform some human tasks faster, there are limitations.