Technology: Asp.Net, MS SQL, VueJs, SAAS

Mizani is credit-scoring and appraisal software as a service platform for banks and financial institutions to maintain credit-history, generate credit-scores and perform loan appraisal through machine learning algorithms.

Mizani is a loan origination and management software. The value proposition of the software is to simplify and accelerate the agriculture loan appraisal process, thereby ensuring fast and hassle-free loan access to farmers. The conceptual prototype of the software was the winner of the 2019 UNCDF Agritech Challenge Fund. So far, Aria Technologies have developed loan pre-screening, credit-scoring and credit-appraisal modules with their own resources. These modules are ready for field testing and final phase of installation in Mero microfinance and Muktinath Bikas Bank. The integration of Mizani in Mero Microfinance is supported by UNESCAP, and is part of the research project. The research will study the bank staff’ behavior and perception toward the software and at the same time measure the effectiveness of the software on the indicators such as process simplification and time saved during a loan appraisal process. The special features of the software are as follows:

  • Digital credit appraisal process.
  • Machine learning based credit scoring.
  • Credit document digitization.
  • Adding Branch, Employees, and Customer detail records.
  • Restrictions of Menu Access as per the Position
  • Dashboard Report contains Total Loan Amount, Weekly Loan, Current Applications,
  • Total Loan Dispatched and Loan Statistics
  • Loan Pre-Processing: Credit Scoring on the basis of Personal Details, Loan Details, Credit background and other details. If the credit score is more than the cut-off score then it is assumed to be more eligible for loan processing.
  • Consolidated Reports are generated.

Mizani software is useful to achieve automated loans faster and in an effective way. It helps to eliminate human error in the credit decision making process and reduce its risk with intelligent scoring models.