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[SINGAPORE] Peter Gill, IBM's Asean vice-president for banking and financial markets, said that any approach to risk management must be comprehensive, covering all different risk domains, including internal fraud, payments fraud, anti-money laundering and IT security.迷你倉出租 Big Data gives banks the necessary 360 degree view of all these aspects. Research agency IDC has seen the use of Big Data and advanced analytics in fraud analysis (inward and outward funds flow) and for anti-money laundering (AML) and counter-terrorism compliance. It is also being used to stop suspicious transactions whether it is via credit cards or at ATMs. "We are also seeing Big Data analytics being used in general risk analytics like intra-day risk calculations for market risk and credit valuation adjustments, real-time or near real-time calculations of potential future exposures as well as various other reg-flag reporting activities," said Michael Araneta, IDC Financial Insights' director for consulting and research. IBM's Mr Gill noted that most successful banks embed sound risk management principles throughout their entire enterprises. With precise analytics, executive decision making can advance from reliance on "gut-feel" to empowerment by leveraging accurate operational data, market facts, and insightful customer analyses. Citibank Singapore's head of decision management, Irene Xu, said that with the proliferation of multiple devices and types of 儲存倉ransactions, the challenges involved in fraud detection have also increased. "In order to detect fraud and identify security breaches, geospatial data from smartphone apps, customer behaviour from social media, weblog data from the organisation's online channels, state-of-the- art fraud detection models, and more, have to be stored and analysed along with data from core operations," she said. Citibank, for example, uses Big Data analytics to monitor user behaviour and track account activity for irregularities. "The challenges on this front will continue to rise with the growing complexity of corporate fraud schemes and diversity of transaction channels." Olivier Crespin, DBS group chief data officer and wealth management chief operating officer, told BT that the use of Big Data analytics in the field of AML work is an example of how these technologies have enabled the bank to be significantly more effective. "In the past, the ability for a bank was reliant on the alertness of staff towards handling transactions on a case-by-case basis and the work becomes exponentially more difficult when fund transfers are broken down into smaller values and spread across different payment modes," he said. "Such techniques are now much less likely to succeed because analytical engines are developed to detect unusual transaction patterns and linkages across a much larger set of transactional data from across multiple channels and sources."迷你倉沙田