Basic model for ranking microfinance institutions

TL;DR

This paper presents a Markov chain-based ranking model for microfinance institutions, highlighting feature selection and demonstrating its effectiveness on real data, with potential applications in microinsurance aggregator sites.

Contribution

Introduces a ranking model for MFIs using Markov chains and provides a new dataset for this domain, addressing data scarcity issues.

Findings

Features improve ranking accuracy

Model demonstrates usefulness on real data

Dataset is publicly available for further research

Abstract

This paper discusses the challenges encountered in building a ranking model for aggregator site products, using the example of ranking microfinance institutions (MFIs) based on post-click conversion. We suggest which features of MFIs should be considered, and using an algorithm based on Markov chains, we demonstrate the ``usefulness'' of these features on real data. The ideas developed in this work can be applied to aggregator websites in microinsurance, especially when personal data is unavailable. Since we did not find similar datasets in the public domain, we are publishing our dataset with a detailed description of its attributes.