What is Machine Learning?
Machine learning is an application of Artificial Intelligence (AI) that learns the patterns in datasets without being programmed. The process of learning starts with data gathering e.g. Direct experience, instruction, look for patterns in data, and capturing the data flow. Machine Learning algorithms are often categorized as the following;
- Supervised algorithm– Predict future events from what they have already learned.
- Unsupervised– Here we don’t need to supervise the model, instead allow the model to work on itself to discover information
Machine Learning helps data scientists quickly confirm which transactions are most likely to be fraudulent, thereby considerably reducing false positives. The techniques are extremely effective in fraud prevention and detection, as they permit the machine-controlled discovery of patterns across large volumes of streaming transactions.
If done properly, machine learning will clearly distinguish legitimate and fraudulent behaviors, thereby adapting over time to new, previously unseen fraud tactics. This could become quite complicated as there’s a need to interpret patterns in the data and apply data science to continually improve the ability to distinguish normal behavior from abnormal behavior. This requires thousands of computations to be accurately performed in milliseconds.
The old rule-based approach
Before Machine Learning became the foremost effective method of detecting fraudulent activity, organizations would rely on rules. Purely rule-based systems require using algorithms that perform several fraud detection scenarios, manually written by fraud analysts. Some of these rules might include parameters such as not permitting purchases from “at-risk” zip codes, flagging transactions from locations that are not near the billing address, or not accepting multiple purchases from the same credit card in a short period of time. Today, legacy systems accept about 300 different rules on average to approve a transaction. That’s why rule-based systems remain too straightforward. They entail adding/adjusting scenarios manually and can hardly detect implicit correlations. Also, these systems often use legacy software that can barely process the real-time data streams that are critical for the digital space and come with their own limitations, especially when aiming for big data fraud detection.
Why Machine Learning for Fraud Detection?
Machine Learning helps make fraud detection secure and more efficient. By implementing Machine Learning into your detection model, you can find out suspicious activity more easily, and get a greater accuracy than with the traditional rule-based methods. This allows for better pattern recognition among large amounts of data, instead of relying solely on “yes/no” factors to determine fraudulent users or transactions.
How does it work?
For Machine Learning to be effective in preventing fraud, it relies on
- Classification– Classification is the method of grouping data together according to certain criteria. The basic method of classification is detecting fraudulent transactions include spam detection, predicting loan defaults, and implementing recommendation systems, among others. The goal of these methods is to distinguish legitimate transactions from fraudulent ones based on classifications such as which merchant a customer is buying from, the location of both the merchant and buyer, time of day/year of the transaction, and the amount spent.
Types of Classification:
Age of the customer’s account, amount of characters in their email address, fraud rate of their IP address, number of devices they’ve accessed your site on, etc.
- Order history
How many orders were placed when the account was created, the dollar amount spent on each transaction, and how many failed orders were attempted.
The billing address matches the shipping address, the country of the customer’s IP address matches the shipping country, customer’s country, city, or zip code is not known for having fraudulent activity.
- Method of payment
Credit card and shipping address are from the same country, matching names between the customer and shipping information, the credit card is not issued from a bank with a reputation of fraudulent transactions by its customers.
2. Regression– Regression models are used to say continuous value. Predicting the cost of a house, given features of the house like size, price, etc is one of the common examples of Regression. It’s a supervised technique that tends to become more subtle. Once applied to fraud detection due to the number of variables and size of the data sets.
- Logistic Regression
In this technique, the authentic transactions compare with the fraud ones to create an algorithm. This model (algorithm) will say whether a new transaction is fraudulent or not. For very large merchants these models are specific to their customer base, but usually, general models will apply.
- Decision Tree
Decision trees are a method by which we split the data set’s based on different conditions. The creation of a tree ignores irrelevant features and does not need an extensive normalization of the data. A tree can be inspected and we can understand why a decision was made by following the list of rules triggered by a certain customer.
- Random Forest
The random forest algorithm is a supervised classification algorithm. As the name suggests, this algorithm creates the forest with a number of trees. In the same way in the random forest classifier, the higher the number of trees in the forest gives the higher the accuracy results.
- Neural Networks
It is an excellent complement to different techniques and improves with exposure to data. The neural network could be a part of cognitive computing technology where the machine mimics how the human brain works and how it observes patterns.
The Impact on customers
Machine Learning is not only helpful to the businesses who implement these models, but also to the subsequent customers who visit your site. By having a machine learning model in place, you will be able to remove the number of falsely flagged transactions, streamlining the acquisition method for legitimate users. This technique also helps to detect fraud that might otherwise be missed with rules-based models alone, improving inventory management and ensuring that available stock is always accurate and available on the market for people who are ready to buy.