University of Twente Student Theses


Data driven banking : applying Big Data to accurately determine consumer creditworthiness

Man, Shen Yi (2016) Data driven banking : applying Big Data to accurately determine consumer creditworthiness.

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Abstract:Conventional methods of credit scoring at traditional banks are becoming less relevant in today’s age of massive data generation and fast adaptation. The newest generation of young adults is called the millennials and they are better connected and more digitized than any client group ever before. This leads to new possibilities when looking at the content of this data and the applications that are possible with thorough analysis of great representative quantities. The financial crises after the dot com boom and the housing bubble have shown that somehow there is a consistency of humans misjudging the inherent value of assets. The same rule applies to financial institutions when computing the creditworthiness of consumers. This causes an increased value of outstanding loan which will not be recuperated by banks. For consumers, this means that they will be structurally indebted and crippled under the weight of the loan. To make matters worse, failing to comply with payment obligation will mark consumers for years, lowering their consumer creditworthiness and making it even more difficult for them to obtain a normal loan. To solve this problem of structural consumer debt, we turn to the trend of Big Data analysis. In specific, Machine Learning can be used to greatly improve the credit scoring process.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science, 83 economics
Programme:Business Information Technology MSc (60025)
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