Credit risk assessment has become an important topic in financial risk administration. Fuzzy clustering
analysis has been applied in credit scoring. Gustafson-Kessel (GK) algorithm has been utilised to cluster
creditworthy customers as against non-creditworthy ones. A good clustering analysis implemented by
good Initial Centres of clusters should be selected. To overcome this problem of Gustafson-Kessel (GK)
algorithm, we proposed a modified version of Kohonen Network (KN) algorithm to select the initial
centres. Utilising similar degree between points to get similarity density, and then by means of maximum
density points selecting; the modified Kohonen Network method generate clustering initial centres to get
more reasonable clustering results. The comparative was conducted using three credit scoring datasets:
Australian, German and Taiwan. Internal and external indexes of validity clustering are computed and
the proposed method was found to have the best performance in these three data sets.