Browsing by Author "Ogutu, Carolyne"

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    A Hidden Markov Model of Risk Classification among the Low Income Earners
    (Journal of Finance and Economics, 2018-12-18) Ntwiga, Davis Bundi; Ogutu, Carolyne; Kirumbu, Michael Kiura; Weke, Patrick
    Low income earners have volatile incomes and most financial providers shun this group of borrowers even though they are motivated in managing the limited resources they have through savings and investments as a means to lower the fluctuations of their income. Peer groupings of the low income earners can assist in pooling the resources they have and improve the group risk mitigation process as group members act like social collateral in credit lending. The study used Kenya Kenya Financial Diaries data of 2013 from 280 households to analyze and understand the credit quality levels and credit scores of peer groups versus individuals among men and women. Hidden Markov model classified the low income earners into credit risk profiles wih a view of understanding the role of groups in low income group lending. Peer groups diversify risk inherent in individual borrowers with women only groups having higher credit quality levels as compared to men only groups. Women and their respective peer groups are more stable with less variability as compared to men. Financial technology providers can incorporate the wide array of soft information to lend to low income earners through mobile based peer groups.
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    Inclusion of peer group and individual low-income earners in M-Shwari micro-credit lending: a hidden Markov model approach
    (International Journal of Electronic Finance, 2018-04-10) Ntwiga, Davis Bundi; Ogutu, Carolyne; Kirumbu, Michael Kiura
    The M-Shwari micro-credit lending system has excluded the low income earners as they lack good financial options due to volatile and fluctuating income. This paper proposes a decision support system for credit scoring and lending of the low income earners who are customers of M-Shwari using the hidden Markov model. The model emits the credit scores of the customers, both for the peer groups and the individual customers. The learning and training of the model utilises the customers' socio-demographics, telecommunication characteristics and account activities. The peer groups have higher credit scores and are more attractive to offer credit facilities using M-Shwari when compared to the individual borrowers.