The financial institutions offer a wide range of credits for the members of the private and
economic sectors. In order to monitor a client and to form a valid position about solvency and
reliability, there is need for a mature, accurate client-qualifying system. The retail credit section has
been developing since 2003 in Hungary, similarly to the western countries, since then we are using
modern client-qualifying systems, but these applications don’t always give satisfactory results.
During a work at a medium-sized bank in Hungary, I have met an applied classification system,
which did not rank the clients well in every case. In order to solve this problem, I chose the
qualification of the bank clients as the topic of my thesis. I tried to work out a more accurate method
in order to make a better classification about the bank clients. I used the SAS softwares to solve the
computer science tasks.
In my thesis, I continued to improve a bank client-qualifying system in order to be able to predict
the clients more efficiently in terms of their payment willingness, and to filter out the potential bad
clients before the credit disturbement. In this methodology, I assign scores to the clients on the basis
of their activities from the viewpoint of the bank.
As a first step, I’m presenting the currently applied system, then I’m revealing my suggested
methodology. Through an actual example I will apply the adapted and improved algorithm that
qualifies the clients more efficiently. I generated sample data, since I might not have used real bank
data for the comparison of the algorithms.
The main aspect of realizing the suggested system was not to score the events themselves
separately, but to find the most suitable score point by looking into the context of these events. After
the forming of the algorithm, I obtained the scores to both systems. Then I made the assessment of
the results, first with the help of elemental statistics, then with the Credit Scoring method. The final
results of the analysis indicated that the new methodology is more efficient than the actual method
applied by the bank.
I think I managed to prepare an algorithm which can be applied better, one that was tested with
scoring analysis. I would suggest my methodology for other banks, because it demonstrates that I
worked out a better client-qualifying system that can filter out the insolvent clients more efficiently.