Investigating the Potential of Using SOM on Audit Changed Trades
Abstract: During the last 20 years, operational risk has been identified as an considerablerisk that needs to be tracked and handled, particular in the financial industry.The Basel Committee on banking supervision is a global cooperation thatsets standard regulations for banking corporations. The framework of Basel IIcontains an explicit definition and proposed calculation about the size of riskcapital needed. Beside this, the responsibility of how to manage and lower theoperational risk is still in the hands of the operating companies.The company Handelsbanken has identified that their operational risk riseswhen trades become the object of Audit Change. Because of the large numberof trades every day that are Audit Changed, the motivation is to automatevisualization and categorization of the trades, so focus can be given to thosetrades that are associated with higher risk.This thesis has been carried out to investigate the usage of the Self-OrganizingMaps (SOM), an unsupervised Machine Learning algorithm, to explore its potentialin gaining information from the trades that have been Audit Changed.The results reveal that SOM have several potential applications for Handelsbankento develop around the trades done in their business, especially withinAudit Changed but also in general for categorizing trades done in the company.
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