About Us

Finscore's missions are to provide innovative credit scoring solutions to banks
providing microcredit to those at the bottom of the economic pyramid.

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About Finscore

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 on 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 date. 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.

How we built our toolset
for financial inclusion

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 landing software was in particular developed for the biggest banks.

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Our happy customers say about us

There are some success stories from our happy customers. We work carefully and hard every day to give you the best and the cleanest services in the world.

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