Case based Reasoning as a Tool to Improve Microcredit

Mohammed Jamal Uddin, Giuseppe Vizzari, Stefania Bandini

Abstract

This paper will discuss the possibility to adopt the Case-Based Reasoning approach to improve microcredit initiatives. In particular, we will consider the Kiva microcredit system, which provides a characterisation (rating) of the risk associated to the field partner supporting the loan, but not of the specific borrower which would benefit from it. We will discuss how the combination of available historical data on loans and their outcomes (structured as a case base) and available knowledge on how to evaluate the risk associated to a loan request (exploited to actually rate past cases and therefore bootstrap the CBR system), can be used to provide the end-users with an indication of the risk rating associated to a loan request based on similar past situations. From this perspective, the case-base and the codified knowledge about how to evaluate risks associated to a loan represent two examples of knowledge IT artifacts.

References

  1. Aamodt, A., Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications (7) 39-59
  2. Al-Azzam Md, Hill R.C, Sarangi S. (2012). Repayment performance in group lending: evidence from Jordan. Journal of Development Economics 97: 404-414.
  3. Attanasio O, Barr A, Cardenas JC, Genicot G, Meghir C. (2012). Risk pooling, risk preferences, and social networks. American Economic Journal: Applied Economics 4(2): 134-167.
  4. Amin, N. (2008). Enabling the Expansion of Microfinance Using Information and Communication Technologies, 3658-3680
  5. Besley, T., & Coate, S. (1995). Group lending, repayment incentives and social collateral. Journal of Development Economics, 46, 1-18.
  6. Cabitza, F., & Locoro, A. (2015). “Made with Knowledge”: disentangling the IT Knowledge Artifact by a qualitative literature review. In Knowledge Discovery, Knowledge Engineering and Knowledge Management 5th International Joint Conference, IC3K 2014, Roma, Italy, October 21-24, 2014. (Vol. Forthcoming, pp. 64-75). Springer.
  7. Cason, T. N., Gangadharan, L., & Maitra, P. (2012). Moral hazard and peer monitoring in a laboratory microfinance experiment. Journal of Economic Behavior and Organization, 82(1), 192-209.
  8. Choo, J., Lee, C., Lee, D., & Park, H. (2014). Understanding and Promoting Micro-Finance Activities in Kiva. org.
  9. Chowdhury, P. R. (2005). Group-lending: Sequential financing, lender monitoring and joint liability. Journal of Development Economics, 77, 415-439. doi:10.1016/j.jdeveco.2004.05.005
  10. Elahi K.Q, Rahman M.L. (2006). Micro-credit and microfinance: functional and conceptual differences. Development in Practice 16(5): 476-483.
  11. Ghatak, M., & Guinnane, T. W. (1999). The economics of lending with joint liability: Theory and practice. Journal of Development Economics, 60(May), 195- 228. doi:10.1016/S0304-3878(99)00041-3
  12. Hamada, M. (2010). Financial Services to the Poor: an Introduction to the Special Issue on Microfinance. The Developing Economies, 48(1), 1-14.
  13. Hermes, N., & Lensink, R. (2007). The empirics of microfinance: What do we know? Economic Journal, 117, 1-10.
  14. Isa, Q. A., Ashfaq, Y., & Haq, A. (2011). What Makes a Microfinance Apex Work??
  15. Islam, J. N., Sciences, P., Mohajan, H. K., & Datta, R. (2012). Aspects of Microfinance System of Grameen Bank of Bangladesh, International Journal of Economic Research, (August), 76-96.
  16. Manzoni, S., Sartori, F., Vizzari, G. (2007). Substitutional Adaptation in Case-Based Reasoning: A General Framework Applied to P-Truck Curing. Applied Artificial Intelligence 21(4&5): 427-442 (2007)
  17. Milana, C., & Ashta, A. (2012). Developing microfinance: A survey of the literature. Strategic Change, 21(7-8), 299-330.
  18. Nayak Abhay. (2010). Credit Delivery Methodologies used by Microfinance Institutions, India.
  19. Paal, B., & Wiseman, T. (2011). Group insurance and lending with endogenous social collateral. Journal of Development Economics, 94(1), 30-40.
  20. Pompa, A., Daley-harris, S., Asia, S., & Bn, E. U. R. (2012). Microfinance Barometer 2012 individual lending.
  21. Salazar-Torres, G., Colombo, E., Da Silva, F. C., Noriega, C. A., & Bandini, S. (2008). Design issues for knowledge artifacts. Knowledge-based systems, 21(8), 856-867.
  22. World Attacking Development. (2000).
  23. Wrenn, E. (2007). Perceptions of the Impact of Microfinance on Livelihood Security. Research and Perspectives on Development Practice. Dublin, Ireland.
Download


Paper Citation


in Harvard Style

Jamal Uddin M., Vizzari G. and Bandini S. (2015). Case based Reasoning as a Tool to Improve Microcredit . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KITA, (IC3K 2015) ISBN 978-989-758-158-8, pages 466-473. DOI: 10.5220/0005664604660473


in Bibtex Style

@conference{kita15,
author={Mohammed Jamal Uddin and Giuseppe Vizzari and Stefania Bandini},
title={Case based Reasoning as a Tool to Improve Microcredit},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KITA, (IC3K 2015)},
year={2015},
pages={466-473},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005664604660473},
isbn={978-989-758-158-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KITA, (IC3K 2015)
TI - Case based Reasoning as a Tool to Improve Microcredit
SN - 978-989-758-158-8
AU - Jamal Uddin M.
AU - Vizzari G.
AU - Bandini S.
PY - 2015
SP - 466
EP - 473
DO - 10.5220/0005664604660473