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.

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"!

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

Supervised Machine Learning and Credit Risk

using machine learning to assess credit risk

Six different techniques are employed to train and evaluate models with unbalanced classes. Algorithms are used to predict credit risk. Performance of these different models is compared and recommendations are suggested based on results.

Supervised Machine Learning and Credit Risk

Uses several machine learning models to predict credit risk.

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

Use scikit-learn and imbalanced-learn machine learning libraries to assess credit card risk.

Testing various supervised machine learning models to predict a loan applicant's credit risk.

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

Build and evaluate several machine learning algorithms to predict credit risk