Using machine learning to improve data quality in the Financial Sector
Frank Christine, 2019
Betreuende Dozierende: Lionel Pilorget
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Data and its adequate quality is a mandatory criteria for making business and taking strategic decisions within the financial sector. Machine learning, as one of the main drivers of digital transformation, offers a lot of possibilities to use organizations data. Research has shown that a combination of machine learning and data quality improvement is possible. The goal of this study is to identify machine learning approaches to improve the data quality specifically for the financial sector. Based on the literature review of data quality problems and current solutions, as well as machine learning in this area, the investigation is accomplished using an online survey for the identification of data quality problems within the financial sector and a case study for a hands-on use case evaluation with a partner in the financial sector. The analysis of the survey identified different data quality problems in the financial sector, which were discussed in the case study to indicate possible machine learning approaches to improve the data quality of the organizations customer data. Moreover, a process on “how to use machine learning in the organization” for data quality problem detection is suggested.
Studiengang: Business Information Systems (Master)
Fachbereich der Arbeit: Wirtschaftsinformatik & IT-Management