Online transaction fraud detection is the biggest challenging issue for banking systems and financial institutes.This project deals with the problem of credit card fraud over online or offline transaction. Fraud detection in credit card involves identifying of those transactions that are fraudulent into two classes of legit class and fraud class transactions, several techniques are designed and implemented to solve to credit card fraud detection such as genetic algorithm, artificial neural network etc.
The credit card fraud detection techniques are classified in two general categories: fraud analysis(misuse detection) and userbehavior analysis (anomaly detection).
In this project we will use the following two machine learning algorithms which is under (anomaly detection) -
- Local Outlier Factor
- Isolated Forest Algorithm
The data analysis is done by creating histograms of the variables related to the dataset and by creating co-relation matrix of the variables.
Fraud detection methods are being developed rapidly in order to adapt with new incoming fraudulent strategies across the world. The task of detecting fraud transactions in an accurate and efficient manner is fairly difficult and challengeable.
It means thatvery small percentages of all credit card transactions are fraudulent. This cause the detection of fraud transactions very difficult and imprecise.
in fraud detection task, different misclassification errors have different importance.Misclassification of a normal transaction as fraud is not as harmful as detecting a fraud transaction as normal. Because in the first case the mistake in classification will be identified in further investigations.
Many transactions may be considered fraudulent, while actually they are normal (false positive) and reversely, a fraudulent transaction may also seem to be legitiate (false negative). Hence obtaining low rate of false positive and false negative is a key challenge of fraud detection systems
Note: you have to download datasets containing transactions made by credit cards in September 2013 by european cardholders dataset Link
This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
In the following cells, we will import our dataset from a .csv file as a Pandas DataFrame. Furthermore, we will begin exploring the dataset to gain an understanding of the type, quantity, and distribution of data in our dataset. For this purpose, we will use Pandas' built-in describe feature, as well as parameter histograms and a correlation matrix.
- In the dataset of 2,84,807 transactions, 492are fraudulent.
- Dataset values are in numerical form as PCA (Principal Component Analysis) transformation is done on input values. This conversion is done so that the user’s personal details remain hidden and the user’s security is maintained.
- Columns having heads as V1 to V28 show PCA transformed numeric values but time, amount and class features show their genuine values.
- Histogram is an accurate representation of the distribution ofnumerical data.
- Time feature shows the elapsed time between transactions while amount shows actual transaction amount.
- Class is the result variable which gives values in the form of 0 and 1, 1 for fraudulent transactions and 0 for valid transactions.
- No co-relation between prediction, most fraud transaction done in large amounts.
- No strong co-relation between class, amount or class, time.
The Local Outlier Factor or LOF algorithm is an unsupervised anomaly detection method. It computes the localdeviation of a given a data point with respect to its neighbours. Local Outlier Factor considers as outliers thesamples that have a substantially lower density than their neighbours.
The IsolationForest ‘isolates’ observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.
Since recursive partitioning can be represented by a tree structure, the number of splittings required to isolate a sample is equivalent to the path length from the root node to the terminating node.
This path length, averaged over a forest of such random trees, is a measure of normality and our decision function.
Random partitioning produces noticeably shorter paths for anomalies. Hence, when a forest of random trees collectively produce shorter path lengths for particular samples, they are highly likely to be anomalies.
