How AI Can Help with the Detection of Financial Crimes 7wData

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Paige Dickie develops Artificial Intelligence (AI) and digital strategy for Canada's banking sector at the Vector Institute for Artificial Intelligence in Toronto. She began her career in management consulting -- much to the disappointment of her father, an engineer -- because she had earned advanced engineering degrees in biomedical and mechanical engineering. Dickie initially worked at McKinsey, the global consulting firm, helping multinational financial institutions across a range of fields from data strategy and digital transformation to setting up innovation centers. She recently joined Vector to lead what she describes as "an exciting project with Canada's banking industry. It's an industry-wide, sector-wide, country-wide initiative where we have three different work streams -- a consortium work stream, a regulatory work stream, and a research-based work stream."


Big Data versus money laundering: Machine learning, applications and regulation in finance ZDNet

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Predicting and acting upon financial fraud is one of the prime areas of application of advanced big data techniques like machine learning (ML). Earlier this week, a case of money laundering known as the Laundromat was uncovered by the Organized Crime and Corruption Reporting Project (OCCRP) involving a number of global banks active in the UK. Could ML help prevent such incidents? What progress is there on this front, how does it fit in the bigger picture, what are the roadblocks, and what may be the repercussions of adoption? The Internet of Things is creating serious new security risks.


Big Data versus money laundering: Machine learning, applications and regulation in finance ZDNet

#artificialintelligence

Predicting and acting upon financial fraud is one of the prime areas of application of advanced big data techniques like machine learning (ML). Earlier this week, a case of money laundering known as the Laundromat was uncovered by the Organized Crime and Corruption Reporting Project (OCCRP) involving a number of global banks active in the UK. Could ML help prevent such incidents? What progress is there on this front, how does it fit in the bigger picture, what are the roadblocks, and what may be the repercussions of adoption? The Internet of Things is creating serious new security risks.


Big Data versus money laundering: Machine learning, applications and regulation in finance 7wData

@machinelearnbot

Predicting and acting upon financial fraud is one of the prime areas of application of advanced big data techniques like machine learning (ML). Earlier this week, a case of money laundering known as the Laundromat was uncovered by the Organized Crime and Corruption Reporting Project (OCCRP) involving a number of global banks active in the UK. Could ML help prevent such incidents? What progress is there on this front, how does it fit in the bigger picture, what are the roadblocks, and what may be the repercussions of adoption? There are many different types of fraud related to the financial industry.


Regtech could save banks £2.7bn on AML compliance Global Trade Review (GTR)

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Banks are squandering £2.7bn a year because of outdated anti-money laundering (AML) systems – costs that could be saved by adopting machine learning and big data technology, new calculations show. According to FortyTwo Data, an AML technology company, financial institutions are wasting armies of staff on chasing millions of false leads – red flags that turn out to be innocent – generated every year by legacy systems that rely on stale rules and scenarios. It concludes that, on average, 55% of false positives can be eradicated by modern systems, accounting for 42% of banks' cost on AML compliance. FortyTwo Data refers to figures from WealthInsight, which predicts that global spending on AML compliance will hit £6.4bn billion this year. The potential savings thus equates to £2.7bn.