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FinScore is the Predictive Analytical tool developed and incubated within the "The Microfinance Association" UK. The Microfinance Association is a global body for microfinance practitioners, the association was set up in 2008 to provide training, consultancy and delinquent management consulting to microfinance institutions, these are institutions that serve those at the bottom of the economic pyramid and in most cases have no access to finance and where credit scoring systems are not present.
FinScore is based on Machine Learning, the focus of machine learning today is to create computer algorithms that learn from data and can make accurate predictions of outcomes-based upon the patterns deduced within the data. Unlike traditional statistical Modeling, the FinScore predictive models of machine learning are generated by an algorithm, as opposed to determinations made by statisticians based upon their interpretation of the results of linear regression and related techniques.
FinScore uses data that either held or collected by MFIs/Banks along with behavioral and psychometric indicators, we then use the data using a wide range of algorithm to predict the outcome of loan applications
The Microfinance Association Digital Finance Team created FinScore, a cloud-based Predictive analytical tool that would assist microfinance institutions to make informed decisions on who they should lend to. We built the toolset to plug a missing gap, as there is no predictive tool specifically developed for the microfinance target market segment. Most predictive lending software was in particular developed for the bigger banks.See more
Microfinance clients can benefit from a faster loan application process, in addition to equal, fair and transparent treatment based on the Output Scores. By fairly and reliably differentiating credit-worthy from very high-risk clients, financial institutions will increase their willingness to lend to clients requiring vital capital.See more
Most microfinance institutions in developing countries have no way of predicting whether a lender would be able to maintain its loan repayments because they do not have sophisticated systems that they can use to predict a borrower's capacity and willingness to make loan repayments. Therefore, before financial inclusion can then be enhanced further, sophisticated lending tools need to be developed so that financial inclusion can be extended to those that are at the bottom of the economic pyramid.
Financial inclusion is simply defined as "the availability and equality of opportunities to access financial services". Yet the challenges to enhancing financial inclusions are wide-ranging and require us to consider new innovations. We see this broad reach in the Sustainable Development Goals, where 'financial inclusion' is reflected as a target in 8 of the 17 goals.
Microfinance clients can benefit from a faster loan application process, in addition to equal, fair and transparent treatment based on the Output Scores. By fairly and reliably differentiating credit-worthy from very high-risk clients, financial institutions will increase their willingness to lend to clients requiring vital capital. This is depicted in the figure below:
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