Introduction to Fraud Detection Algorithms in Machine Learning
For years, fraud has been a major issue in sectors like banking, medical, insurance, and many others. Due to the increase in online transactions through different payment options, such as credit/debit cards, PhonePe, Gpay, Paytm, etc., fraudulent activities have also increased. Moreover, fraudsters or criminals have become very skilled in finding escapes so that they can loot more. Since no system is perfect and there is always a loophole them, it has become a challenging task to make a secure system for authentication and preventing customers from fraud. So, Fraud detection algorithms are very useful for preventing frauds.
Here comes Machine Learning which can be used for creating a fraud detection algorithm that helps in solving these real-world problems.
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Manual Review and Transaction Rules
Nowadays, Machine Learning in Artificial Intelligence resolves most of the issues that human beings find difficult to deal with. Previously, industries were using a rule-based approach for fraud detection. But due to the popularity and acceptance of Artificial Intelligence Tutorial, especially by students and Machine Learning in every industry vertical, organizations have moved from the ruled-based fraud detection to ML-based solutions.
Now, we will look at the rule-based fraud detection system and ML-based systems.
Rule-based Approach or Traditional Approach in Fraud Detection Algorithms
In the rule-based approach, the algorithms are written by fraud analysts. They are based on strict rules. If any changes have to be made for detecting a new fraud, then they are done manually either by making those changes in the already existing algorithms or by creating new algorithms. In this approach, with the increase in the number of customers and the data, human effort also increases. So, the rule-based approach is time-consuming and costly. Another drawback of this approach is that it is more likely to have false positives. This is an error condition where an output of a test specifies the existence of a particular condition that does not even exist. The output of a transaction depends upon the rules and guidelines made for training the algorithm for non-fraudulent transactions. So, for a fixed risk threshold, if a transaction is rejected where it should not be, it will generate a condition of high rates of false positives. This false-positive condition will result in losing genuine customers.
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ML-based Fraud Detection Algorithms
In the rule-based approach, the algorithms cannot recognize the hidden patterns. Since they are based on strict rules, they cannot predict fraud by going beyond these rules. But in real world, fraudsters are very skilled and can adopt new techniques every time to commit a crime. Therefore, there is a need for a system that can analyze patterns in data and predict and respond to new situations for which it is not trained or explicitly programmed.
Hence, we use Machine Learning for detecting fraud. Here, a machine tries to learn by itself and becomes better by experience. Also, it is an efficient way of detecting fraud because of its fast computing. It does not even require the guidance of a fraud analyst. It helps in reducing false positives for transactions as the patterns are detected by an automated system for streaming transactions that are in huge volume.
Now, we will look at the two most commonly used Machine Learning models for detecting fraud in transactions.
Supervised Learning Used in Fraud Detection Algorithms
Supervised Learning models are trained on tagged outputs. If a transaction occurs, it is tagged as either ‘fraud’ or ‘non-fraud.’ Large amounts of such tagged data are fed into the supervised learning model in order to train it in such a way that it gives a valid output. Also, the accuracy of the model’s output depends on how well-organized your data is.
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Unsupervised Learning Used in Fraud Detection Algorithm
Unsupervised learning models are built to detect unusual behavior in transactions which is not detected previously. Unsupervised learning models involve self-learning that helps in finding hidden patterns in transactions. In this type, the model tries to learn by itself, analyzes the available data, and tries to find the similarities and dissimilarities between the occurrences of transactions. This helps in detecting fraudulent activities.
So, both these models, supervised and unsupervised, can be used independently or in combination for detecting anomalies in transactions.
Need for the Fraud Detection Machine Learning Algorithms
Human beings always search for methods, tools, or techniques that reduce the human effort for performing a certain task efficiently. In Machine Learning, algorithms are designed in such a way that they try to learn by themselves using past experience. After learning from the past experience, the algorithms become quite capable of reacting and responding to conditions for which they are not explicitly programmed. So, Machine Learning helps a lot when it comes to fraud detection. It tries to identify hidden patterns that help in detecting a fraud which is not been previously recognized. Also, its computation is fast as compared to the traditional rule-based approaches.
Why do we use Machine Learning in Fraud Detection?
Here are some factors for why Machine Learning techniques are so popular and widely used in industries for detecting frauds:
- Speed: Machine Learning is widely used because of its fast computation. It analyzes and processes data and extracts new patterns from it within no time. For human beings to evaluate the data, it will take a lot of time and evaluation time will increase with the amount of data.
- Scalability: As more and more data is fed into the Machine Learning-based model, the model becomes more accurate and effective in prediction.
- Efficiency: Machine Learning algorithms perform the redundant task of data analysis and try to find hidden patterns repetitively. Their efficiency is better in giving results in comparison with manual efforts. It avoids the occurrence of false positives which counts for its efficiency.
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How does a Machine Learning system work for Fraud Detection?
The below picture shows the basic structure of the working of fraud detection algorithms using Machine Learning:
Feeding Data: First, the data is fed into the model. The accuracy of the model depends on the amount of data on which it is trained, more data better the model performs.
For detecting frauds specific to a particular business, you need to input more and more amounts of data into your model. This will train your model in such a way that it detects fraud activities specific to your business perfectly.
Extracting Features: Feature extraction basically works on extracting the information of each and every thread associated with a transaction process. These can be the location from where the transaction is made, the identity of the customer, the mode of payments, and the network used for transaction.
- Identity: This parameter is used to check a customer’s email address, mobile number, etc. and it can check the credit score of the bank account if the customer applies for a loan.
- Location: It checks the IP address of the customer and the fraud rates at the customer’s IP address and shipping address.
- Mode of Payment: It checks the cards used for the transaction, the name of the cardholder, cards from different countries, and the rates of fraud of the bank account used.
- Network: It checks for the number of mobile numbers and emails used within a network for the transaction.
Training the Algorithm: Once you have created a fraud detection algorithm, you need to train it by providing customers data so that the fraud detection algorithm learns how to distinguish between ‘fraud’ and ‘genuine’ transactions.
Creating a Model: Once you have trained your fraud detection algorithm on a specific dataset, you are ready with a model that works for detecting ‘fraudulent’ and ‘non-fraudulent’ transactions in your business.
The advantage of Machine Learning in fraud detection algorithms is that it keeps on improving as it is exposed to more data.
There are many techniques in Machine Learning used for fraud detection. Here, with the help of some use cases, we will understand how Machine Learning is used in fraud detection.
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Techniques of Machine Learning for Fraud Detection Algorithms
- Fraud Detection Machine Learning Algorithms Using Logistic Regression: Logistic Regression is a supervised learning technique that is used when the decision is categorical. It means that the result will be either ‘fraud’ or ‘non-fraud’ if a transaction occurs.
Use Case: Let us consider a scenario where a transaction occurs and we need to check whether it is a ‘fraudulent’ or ‘non-fraudulent’ transaction. There will be given set of parameters that are checked and, on the basis of the probability calculated, we will get the output as ‘fraud’ or ‘non-fraud.’
In the above diagram, we can see that the probability calculated is 0.9. This means that there is a 90 percent chance that the transaction is ‘genuine’ and there is a 10 percent probability that it is a ‘fraud’ transaction.
- Fraud Detection Machine Learning Algorithms Using Decision Tree: Decision Tree algorithms in fraud detection are used where there is a need for the classification of unusual activities in a transaction from an authorized user. These algorithms consist of constraints that are trained on the dataset for classifying fraud transactions.
Use Case: Let us consider a scenario where a user makes transactions. We will build a decision tree to predict the probability of fraud based on the transaction made.
First, in the decision tree, we will check whether the transaction is greater than ₹50,000. If it is ‘yes,’ then we will check the location where the transaction is made.
And if it is ‘no,’ then we will check the frequency of the transaction.
After that, as per the probabilities calculated for these conditions, we will predict the transaction as ‘fraud’ or ‘non-fraud.’
Here, if the amount is greater than ₹50,000 and location is equal to the IP address of the customer, then there is only a 25 percent chance of ‘fraud’ and a 75 percent chance of ‘non-fraud.’
Similarly, if the amount is greater than ₹50,000 and the number of locations is greater than 1, then there is a 75 percent chance of ‘fraud’ and a 25 percent chance of ‘non-fraud.’
This is how a decision tree in Machine Learning helps in creating fraud detection algorithms.
Now, we will look at the random forest in Machine Learning used in fraud detection algorithms.
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- Fraud Detection Machine Learning Algorithms Using Random Forest: Random Forest uses a combination of decision trees to improve the results. Each decision tree checks for different conditions. They are trained on random datasets and, based on the training of the decision trees, each tree gives the probability of the transaction being ‘fraud’ and ‘non-fraud.’ Then, the model predicts the result accordingly.
Use Case: Let’s consider a scenario where a transaction is made. Now, we will see how the random forest in Machine Learning is used in fraud detection algorithms.
When a request for a transaction is given to the model, it checks for the information like the credit/debit card number, location, date, time, the IP address, the amount, and the frequency of the transaction. All this dataset is fed as an input into the fraud detection algorithm. Then this fraud detection algorithm selects variables from the given dataset that help in splitting up of the dataset. The below diagram shows the splitting up of the dataset into multiple decision trees.
So, the sub-trees consist of variables and the conditions to check those variables for an authorized transaction.
After checking all the conditions, all the sub-trees will give the probabilities for a transaction to be ‘fraud’ and ‘non-fraud.’ Based on the combined result, the model will mark the transaction as ‘fraud’ or ‘genuine.’
This is how a random forest in Machine Learning is used in fraud detection algorithms.
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- Fraud Detection Machine Learning Algorithms Using Neural Networks: Neural Networks is a concept inspired by the working of a human brain. Neural networks in Deep Learning uses different layers for computation. It uses cognitive computing that helps in building machines capable of using self-learning algorithms that involve the use of data mining, pattern recognition, and natural language processing. It is trained on a dataset passing it through different layers several times.
It gives more accurate results than other models as it uses cognitive computing and it learns from the patterns of authorized behavior and thus distinguishes between ‘fraud’ and ‘genuine’ transactions.
Use Case: Now, we will look at an example where a neural network is used for fraud detection. There are different layers in a neural network that focus on different parameters to make a decision whether a transaction is ‘fraud’ or ‘non-fraud.’ In the below diagram it is shown how the layers of neural networks represent and work on different parameters.
First, the data is fed into the neural network. After that, the Hidden Layer 1 checks the amount of transaction, and similarly other layers check for the location, identity, IP address of the location, the frequency of transaction, and the mode of payment. There can be more business-specific parameters. These individual layers work on these parameters, and computation is done based on the models’ self-learning and past experience to calculate the probabilities for detecting frauds.
Thus, neural networks work on data and learn from it, and it improves the model’s performance over every iteration.
This is how neural networks are used for implementing fraud detection algorithms.
In this blog, we have seen how fraud detection algorithms work using Machine Learning techniques such as logistic regression, decision tree, random forest, and neural networks. This technology is improving day by day so that it provides us more accuracy and better results to prevent fraud.
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