Statistical models can help lenders in emerging markets standardize and improve their lending decisions. These models define customer scoring based on a statistical analysis of past borrowers’ characteristics instead of relying on the subjective judgments of loan officers. Evidence shows that statistical models improve the accuracy of credit decisions and make lending more cost-efficient. They also help companies make key decisions throughout the customer lifecycle.
Lenders sometimes assume that statistical credit scoring is too costly or difficult or that they do not have the kind of data needed to implement it. However, the primary input needed for this type of modelling is something many providers already possess: customers’ repayment histories. This guide explains what types of data lenders can leverage for statistical credit scoring and the ways in which it can be used.
Furthermore, different statistical models can be used for building credit scores. Lenders who are new to data analytics can start with a simple model and tailor it over time to meet their needs. In this guide, readers will find a step-by-step approach to building, testing, finetuning, and applying a statistical model for lending decisions based on a company’s growth goals and risk appetite.
This guide emphasizes that the effectiveness of data analytics approaches often involves building a broader data-driven corporate culture.