Master's Theses

Permanent URI for this communityhttps://hdl.handle.net/10679/9875

Browse

Browsing by Author "Aktuna, Mehmet"

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Placeholder
    Master ThesisPublicationMetadata only
    Comparison of different customer segmentation models for the financial sector versus the Turkish factoring sector and a clustering model proposal for a sme focused factoring company
    Aktuna, Mehmet; Güntay, Levent; Güntay, Levent; Ahi, Emrah; Özlük, Ö.; Department of Financial Engineering
    Factoring is the process of transferring a forward receivable arising from commercial transactions to a factoring company by assignment. Companies that prefer to do factoring, instead of waiting for the due date of the collection of their receivables arising from their trade, transfer their receivables to the factoring company with a certain discount, with documents such as post-dated cheque, trade invoices and factoring contracts. The use of cheque in Turkey is different from that in the world due to the post-dated maturity written on the cheque and SMEs in Turkey frequently use cheques in their trade with maturity. In terms of segmentation, there are many studies for banks in the literature, but there is no study in the SME focused factoring segment. This study is the first, where a two-stage customer segmentation is implemented using the data of a SME focused Turkish factoring company. When the clusters generated by the K-Means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) models are compared, it is observed that the clusters of the K-Means algorithm are more balanced in terms of distribution of the number of customers across clusters. On the other hand, the clusters generated by the DBSCAN model are more homogeneous, especially for outlier clusters. Homogeneity means an effective segmentation, which is there are few outliers within a cluster as defined by the Mahalanobis distance of each observation. The DBSCAN model can generate several homogeneous clusters containing a small number of customers if model parameters are appropriately calibrated. On the contrary, if few number of clusters are preferred, the homogeneity of the DBSCAN algorithm gets worse and the K-Means model gives more balanced clustering results.