Christophe Hurlin bSessi Tokpavi bEuropean Journal of Operational ResearchSullivan, Hue & Christophe, Hurlin & Tokpavi, Sessi. (2017). Machine Learning for Credit Scoring: Improving Logistic Regression with Non Linear Decision Tree Effects.
Nowadays, detection of frauds has become easy thanks to Machine Learning. Given the fact that machine learning is a very broad concept, we will learn a few ways how Finance could benefit with the use of Machine Learning. What is a Credit Scoring System? A credit scoring system is a ...
Using machine learning requires a different way of approaching a problem: You let the machine learning algorithm solve the problem. This is a shift in mindset for people familiar with thinking through functional steps. It takes some trust that the machine learning program will produce results and ...
Why is there a need for Real-time Machine Learning? Machine Learning Model Preparation Difference between Real-time and Analytical Machine Learning What is the Efficiency of Stream Processing and Batch Processing? Real-time Machine Learning trends in the real world What are the technologies used...
This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantu
In micro-lending markets, lack of recorded credit history is a significant impediment to assessing individual borrowers’ creditworthiness and therefore deciding fair interest rates. This research compares various machine learning algorithms on real micr
of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Use supervised learning if you have known data for the output you are trying to predict...
Credit scoring is an effective tool for banks and lending companies to manage the potential credit risk of borrowers. Machine learning algorithms have made grand progress in automatic and accurate discrimination of good and bad borrowers. Notably, ensemble approaches are a group of powerful tools to...
credit scoring, fraud detection, stock trading, drug design, and many other applications. A recent report from the McKinsey Global Institute asserts that machine learning (a.k.a. data mining or predictive analytics) will be the driver of the next big wave of innovation.15 Several fine textbooks...
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