Data Mining & Analysis

Fraud Detection And Risk Scoring

Deceiving and scamming people in their financial transactions by committing crime is referred to ad Fraud and identifying this fraud with help of some techniques is called as Fraud Detection. Machine learning approach to fraud detection gained much attention in recent times by signaling possible fraud by detecting subtle and hidden events in user behavior. It includes less manual work and faster data processing. Uncontrolled loss of value of something is called as risk and a number which determines the severity of a risk is called as a risk score. Risk scoring can be done by Qualitative and quantitative methodologies, which includes assessment and techniques. Credit risk modelling in machine learning uses financial data to predict the default risk. Artificial neural networks, random forest and boosting are some of the approaches of machine learning related to risk scoring through credit risk modelling