Credit Scoring for M-Shwari using Hidden Markov Model


Ntwiga, Davis Bundi
Weke Patrick
School of Mathematics, University of Nairobi, Nairobi, Kenya

The introduction of mobile based Micro-credit facility, M-Shwari,
has heightened the need to develop a proper decision support system to
classify the customers based on their credit scores. This arises due to lack of
proper information on the poor and unbanked as they are locked out of the
formal banking sector. A classification technique, the hidden Markov model,
is used. The poor customers’ scanty deposits and withdrawal dynamics in the
M-Shwari account estimate the credit risk factors that are used in training
and learning the hidden Markov model. The data is generated through
simulation and customers categorized in terms of their credit scores and
credit quality levels. The model classifies over 80 percent of the customers
as having average and good credit quality level. This approach offers a
simple and novice method to cater for the unbanked and poor with minimal
or no financial history thus increasing financial inclusion in Kenya.

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