id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-327810-kquh59ry	Canhoto, Ana Isabel	Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective	2020-10-17		.txt	text/plain	11120	525	47	These requirements mean that financial services organisations are wary of adopting technologies where they lack complete control over use of customer data, or whose workings they do not fully understand, as in the case of black-box type of algorithms. In addition to the specific technical and organisational challenges associated with the specific types of algorithms discussed above, there are some generic issues that condition BANK's ability to use machine learning in AML profiling. Machine learning's ability to discover patterns in data, process various types of data and act autonomously promises to enable financial intermediaries to detect money laundering activity in a cost-effective manner (Fernandez, 2019) . While financial services organisations may be essential enablers of money laundering and, indirectly, criminal activity, their perspective is limited to the transaction data for their own customers and their own institution.	./cache/cord-327810-kquh59ry.txt	./txt/cord-327810-kquh59ry.txt