Data Mining And Performance Of Microfinance Institutions In Kenya
Kokonya, Lorine N
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Mining customer data to measure productivity and enhance performance management is not only feasible in this information era but also in line with the transformation of a microfinance institution into a "customer driven organization". In this paper, we look at application of data mining techniques to performance management in the microfinance industry. Data Mining or knowledge discovery in databases can be defined as an activity that extracts some new nontrivial information contained in large databases. The goal is to discover hidden patterns, unexpected trends or other subtle relationships in the data using a combination of techniques from machine learning, statistics and database technologies. This new discipline today finds application in a wide and diverse range of business, scientific and engineering scenarios. Given the above arguments, this study aimed to answer the following research questions: Are microfinance institutions in Kenya using data mining techniques? What challenges do microfinance institutions face when using these data techniques? What is the relationship between data mining techniques and the success of micro finance institutions? Population of this study comprised of 56 Microfinance Institutions with the population of interest being 56 respondents who are IT officers or database administrators of the 56 microfinance institutions in Kenya. This study was limited to the institutions that are registered and regulated by the AMFI. The findings revealed that data mining has strong and positive correlation with the performance of microfinance institutions in Kenya. Data mining makes it possible to analyze routine business transactions and glean a significant amount of information about individual’s such as buying habits and preferences, banking information and customer details. Businesses collect information about their customers in many ways to understand their purchasing behaviors trends and with the adoption of data mining, microfinance institutions are able to obtain data with ease. Data mining and data warehousing tend to be self-reinforcing. The more powerful the data mining queries, the greater the utility of the information being gleaned from the data, and the greater the pressure to increase the amount of data being collected and maintained, which increases the pressure for faster, more powerful data mining queries. This increases pressure for larger, faster systems, which are more expensive and enable microfinance to increase in performance efficiency. Based on the study findings the study recommends that microfinance institutions should adopt data mining to enhance their performance. The organizations need to make sure that there is enough data to analyze as well as assure quality of data. Organizations should ensure that the analysts are trained well and deduct the correct information which serves the purposes of the problem in the first place. The process of using data mining should be a learning experience.