Enhancing risk management models to make more credit available
Logical Glue’s platform allows easy building, validation and deployment of cutting-edge machine learning models, without requiring long development cycles or advanced data-science expertise.
Models were built using PayBreak’s data and integrated into their credit decision process via an API to improve accuracy and provide greater customer insight.
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consumer credit card data to enable accurate lending predictions
Given the recent economic turmoil resulting from the banking crisis, consumer credit lending decisions have come to the fore of the consciousness of both lenders and the public. Companies are coming under pressure and looking carefully at approaches to credit rating in order to make improvement.
For companies who work within or with credit scoring, some questions remain key for each new approach suggested. This case study helps to answer some of these questions.
Using machine learning to increase your credit scoring function
Fast and accurate credit scoring can make or break the profitability of any lending product. Aside from marketing the product successfully and positioning it competitively, the key to increasing profitability is to have the most accurate reading on the credit risk represented by each applicant. Relying on traditional credit scoring methodologies, with their inherent inaccuracies, will soon become a key competitive weakness for many lenders.Access Case Study