For decades, there have been developments of computer software to support human decision making. Along with the increased complexity of business environments, smart technologies are becoming popular and useful for decision support based on huge amount of information and advanced analysis. The aim of this study was to explore the potential of using machine learning for financial risk management in debt collection, with a purpose of providing a clear description of what possibilities and difficulties there are. The exploration was done from a business perspective in order to complement previous research using a computer science approach which centralizes on the development and testing of algorithms. By conducting a case study at Tieto, who provides a market leading debt collection system, data was collected about the process and the findings were analyzed based on machine learning theories. The results showed that machine learning has the potential to improve the predictions for risk assessment through advanced pattern recognition and adapting to changes in the environment. Furthermore, it also has the potential to provide the decision maker with customized suggestions for suitable risk mitigation strategies based on experiences and evaluations of previous strategic decisions. However, the issues related to data availability were concluded as potential difficulties due to the limitations of accessing more data from authorities through an automated process. Moreover, the potential is highly dependent on future laws and regulations for data management which will affect the difficulty of data availability further.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-324684 |
| Date | January 2017 |
| Creators | Ohlsson, Caroline |
| Publisher | Uppsala universitet, Företagsekonomiska institutionen |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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