This is a very Important part of Data Science Case Study because Detecting Frauds and Analyzing their Behaviours and finding reasons behind them is one of the prime responsibilities of a Data Scientist. This is the Branch which comes under Anamoly Detection.

Insurance claim fraud detection using machine learning algorithms.

Build and evaluate several machine learning algorithms to predict credit risk.

Evaluate the performance of multiple machine learning models using sampling and ensemble techniques and making a recommendation on whether they should be used to predict credit risk.

Banking-Dataset-Marketing-Targets

Supervised Machine Learning and Credit Risk

Testing 6 different machine learning models to determine which is best at predicting credit risk.

Uses several machine learning models to predict credit risk.

For this analysis, we used computational linguistics and biometrics to systematically identify the trend using various news articles and closing prices using the "CoinGecko CSV & Crypto News API"!

Supervised Machine Learning and Credit Risk

Build and evaluate several machine learning algorithms to predict credit risk

Resampling exercise to predict accuracy, precision, and sensitivity in credit-loan risk

Analysis of different machine learning models' performance on predicting credit default

Extract data provided by lending club, and transform it to be useable by predictive models.

Using machine learning to train and evaluate models with unbalanced classes to determine the best models to predict credit risk.

Train and test multiple Machine Learning models to predict risk based on consumer credit profiles.

Built and evaluated variety of supervised machine learning algorithms to predict credit risk.

using machine learning to assess credit risk

This repo contains code that looks into LendingClub's membership data and employs ML to see if the model can predict a user's "credit risk" based on lending.