Bio

Lecturer/ Researcher

Davis Bundi is a Lecturer in the School of Mathematics, University of Nairobi. His research interests include Finance, Simulation, Social networks, Hidden Markov models, Financial deepening, Mathematical finance and Credit risk. 


Publications


2020

Ntwiga, DB, Wanyonyi AW.  2020.  Consumer Perceptions and Behaviour toward Credit Usage in Kenya. Open Access Library Journal. 7(6):1-16. AbstractWebsite

Consumer behaviour and perceptions evolve over time and affect credit usage from the financial service providers. We use the 2016 FinAccess Household survey data of 2015 from 8665 households to examine how perceptions and behaviour of un(der) banked consumers can shape their dynamics towards credit usage. The perceptions and behaviour are based on source of financial advice, trust of the institutions, characteristics of the financial instrument and cost of credit. The multinomial logistic regression model predicts the odds of credit usage based on perceptions and behaviour of the consumers. The categories for the credit usage are: have credit, used to have credit and never had credit. Consumer perceptions and behaviour based on cost of credit and trust increase credit usage, while source of financial advice had minimal influence on credit usage. The characteristics of the financial instrument are catering to emergencies and being safe to use increased credit usage. The Savings and Credit Cooperative Organizations and microfinance are the most trusted financial institutions by the consumers, while shylock has the highest cost of credit. Radio as a source of financial advice reduced credit usage. The dynamics of credit usage are shaped by the perceptions and behaviour of the consumers.

Ntwiga, DB.  2020.  Credit usage among the un (der) banked: consumer socio-economic characteristics and influence of financial technology. International Journal of Financial Services Management. 10(1):38-54. AbstractWebsite

We use the 2016 FinAccess Household survey data of 2015 from 8665 households to analyse how the socio-economic characteristics and financial literacy of un(der) banked consumers can shape their dynamics towards credit usage. A qualitative analysis is presented on the influence of financial technology on consumer credit usage. The access to financial services is influenced by the socio-economic characteristics and financial literacy of the consumers. Gender, financial literacy, age, income, marital status, education level and geographical cluster are statistically significant in influencing credit usage, both current and past usage relative to never had credit. As financial technology continues to expand and offer credit, there is need to understand the user experience to match their social and economic status as a means to increase credit usage in Kenya.

Ntwiga, DB.  2020.  Technical Efficiency in the Kenyan Banking Sector: Influence of Fintech and Banks Collaboration. Journal of Finance and Economics. 8(1):13-20. AbstractWebsite

Efficient banks increase financial stability, intermediation and value to the shareholders. As Fintech innovations continue to alter the financial landscape in Kenya, banks will leverage on Fintech to enhance efficiency. This study investigates if Fintech and bank collaboration has an influence on efficiency in the banking sector. A two step data envelopment analysis is applied with input-orientation based on three intermediation dimension models. Efficiency scores are decomposed into technical, pure technical and scale efficiencies. Financial statement data from 2009-2018 for five banks with Fintech collaborations form the analysis. The study period is segmented into Pre-Fintech, 2009-2014 and Post Fintech, 2015-2018. Descriptive statistics summarize the data with Panel regression model testing the selected financial variables influence on efficiency of banks in the Pre-Post Fintech period. In the ten year period, technical inefficiency based on the three models for the Pre-Post Fintech period is failure to operate at the most productive scale, poor input utilization and managerial inefficiencies. For the Panel regression, loan intensity in model M1, return on asset in model M2, and cost of intermediation in model M3 had a significant and positive influence on technical efficiency. Fintech and banks collaboration has had a positive influence on efficiency in the Kenyan banking sector.

2019

Ntwiga, DB.  2019.  Can FinTech Shape the Dynamics ofConsumer Credit Usage among theUn(der)banked?, 2019 Kenya Bankers Association Working Paper Series. Abstract

We use the 2016 FinAccess Household survey data of 2015 from 8665 households and desktop reviews to examine how perceptions, behaviour, financial literacy and socio-economic characteristics of un(der) banked consumers can shape their dynamics towards credit usage. The challenges and opportunities for the market players are examined using desktop reviews and their role towards an increase in financial inclusion and credit usage through FinTech. The disruptive innovations have provided new possibilities, challenges and opportunities to boost financial and credit usage in the market. Consumer perceptions on cost, trust, source of financial advice, financial literacy and socio-economic characteristics influences credit usage. The business models being developed by the FinTech providers are taunted to change the landscape of lending to the un(der) banked

Ntwiga, DB.  2019.  Fintech and Banks Collaboration: Does it Influence Efficiency inthe Banking Sector?, September 13, 20 Kenya Bankers Association 8th Banking Research Conference. , Radisson Blu Hotel Nairobi Kenya Abstract

The efficiency of the banking sector in Sub-Saharan Africa is low compared to rest of the world and Fintech is taunted to alter this scenario. Efficient banks increase financial stability, intermediation and value to the shareholders. As Fintech innovations continue to alter the landscape in the banking sector, banks in Kenya are forming collaborations that are envisioned to shape the evolution of credit allocation and delivery of services. The study investigates the influence of Fintech on a bank’s efficiency in credit allocation using thedata envelopment model with input-orientation based on the intermediationdimension. Efficiency scores are decomposed as technical efficiency, pure technical efficiency and scale efficiency.Secondary data for the period 2009-2018 is extracted from thirteen banks sampled from the top fifteen banks in Kenya based on their market share. The banks are either locally owned or listed in Nairobi Securities Exchange, of which five have Fintech collaborationswith a Pre-Fintech and Post Fintechperiod. Panel regression model tested the effect of financial ratios on technical efficiency of the banks. Fintech collaborating banks are more technically efficient based on models M1, M2 and M3 in Pre-Fintech. In Post Fintech, the Fintech banks are more efficient based on models M2, M3 and M4 but with decreasing returns to scale which is due to the banks being overly large, thus non-optimal in their operations. The positive effect on technical efficiency is observed from the ratios, liquidity, loan intensity, return on assets and cost of income. Cost of intermediation and credit risk had a negative effect on technical efficiency. Therefore, Fintech and banks collaborations did not significantly influence efficiency in the banking sector.

2018

Pweke, D. B. Ntwiga, Ogutu C, Kirumbu MK.  2018.  A Hidden Markov Model of Risk Classification among the Low Income Earners. Journal of Finance and Economics. 6, (6):242-249. AbstractWebsite

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.

Ntwiga, DB;, Ogutu C;, Kirumbu MK.  2018.  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 . 9(2) AbstractWebsite

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.

2016

2013

2012

2011

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