Fraud activities are mushrooming in the finance sector, and traditional methods are no more effective. So, organizations should start adopting machine learning to deal with frauds.
FREMONT, CA: Nowadays, cyber crime is increasing day by day in the financial sector, and attacks persistently come in the form of money laundering, identity theft, and mobile fraud, among others. Credit card fraud is one of the most common types of cyber crime, and the growth in e-commerce and mobile payments is partly behind the soaring incidence of card fraud. Earlier banks and financial institutions have approached fraud detection with several manual procedures or rule-based solutions, which had limitations.
How to predict fraud with Machine Learning?
To create a perfect model with sufficient predictive capability and accuracy, a combination of supervised and unsupervised machine learning methods are needed. Machine learning can easily find fraud by operating with tens of thousands of parameters. Generally, there are two types of machine learning algorithms used in fraud detection: supervised and unsupervised learning.
Fraud scenarios and their detection
The supervised machine learning algorithms are used to solve these problems includes logistic regression, decision trees, random forests, and neural networks.
• Logistic regression: It is one of the popular methods, which decides the strength of cause and effect relationships between variables in data sets. It can also be used to produce an algorithm that can tell whether a transaction is ‘good’ or ‘bad.’
• Decision trees: To create a set of rules that model customers’ normal behavior and to train them decision trees can be used. On the other hand, it can also detect frauds and anomalies.
• Neural networks: These are powerful techniques inspired by the workings of the human brain. It can learn and adapt to patterns of normal behavior, neural networks, and also can identify fraud in real-time.
Unsupervised techniques are based on clustering algorithms, which group similar data points together; they are also used for anomaly detection.
• K-means clustering: It splits a dataset into different clusters. The algorithm works iteratively and assigns data points to one of the predefined numbers of classes (k), based on the features that are in the dataset. Data points are clustered based on feature similarity.
• Local Outlier Factor: It is an algorithm that calculates the local density of data points and allows for identifying regions with similar density in the data set. By using the locality concept, one can distinguish points with a much lower frequency than other neighbors.