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Fraud Detection using Data Mining Techniques

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8. Methods used to identify fraud detection by data mining techniques.

Large businesses such as banks, microfinance institutions, and insurance firms are always seeking methods to enhance their fraud detection procedures. With the advent of big data, it has become more challenging to detect fraud, but it has also provided researchers with new opportunities to discover unnoticed patterns and trends. Data mining can be used to find possible scams by examining and spotting patterns and trends.

Goals of Fraud Detection

We assume that fraud detection has the following goals:

• Eliminate fraud to the lowest level.
• Increase customers’ confidence in non-banking and banking systems, especially for online transactions.
• Deter fraudsters (current and potential ones).

Data Mining Approaches

There are several data mining approaches, most of which have been applied in data mining studies. These include:

• Classification: The most widely used data mining technique is classification, which uses a set of previously categorized instances to create a model that can classify most records.
• Clustering: Clustering is assembling related pieces of data according to their characteristics. By doing so, fraud can be avoided, and patterns and trends in the data can be identified.
• Association Rule: A technique known as association rule mining involves identifying patterns in data that often occur together. This can assist in locating potential fraud by looking for signs of behavior related to the issue.
• Prediction: Prediction is one of the strategies for data mining that identifies connections between independent and dependent variables.
• Sequential Patterns: One data mining technique, “sequential patterns” analysis, looks for recurring patterns in data transactions over a certain period.

Regression Analysis

Regression analysis can be used to model the connection between at least one independent variable and dependent variables. Independent variables in data mining are already known traits, whereas responsive are the variables we want to predict. Unfortunately, many problems in the actual world are difficult to predict. Several regression techniques include:

• Linear Regression
• Multivariate Linear Regression
• Nonlinear Regression
• Multivariate Nonlinear Regression

Anomaly Detection

An “anomaly detection” To find anomalous data points in a huge volume of data, anomaly detection, a fraud detection tool, is typically used. It can assist in spotting probable fraudulent activities by analyzing unusual patterns and trends in data.