What Machine Learning Is and How It Can Help Detect Fraud
While online shopping can be convenient, it is not without risk. It's possible that either party, the buyer or seller, will try to scam each other by selling online. Students who are in high school or colleges can also use essay-writing services to get help on their homework assignments and other coursework. The rapid evolution of the e-commerce industry has made fraud more difficult to detect. Scams range from online bank frauds, to identity thefts and money-laundering schemes. Fraudsters exploit every weakness in a company's system.
Fraud detection and prevention has become a top concern in the banking and e-commerce industries. By applying machine learning, you can prevent or eliminate this activity.
The field of Computer Science known as Machine Learning is built around algorithms and big data. It allows computers to learn the same way that humans do. If machine learning is done correctly, it can be used to easily distinguish between legitimate and fraudulent behavior. To learn more about how machine-learning can detect fraud, please read the following guide.
- Allow Data Entry
To begin with, the machine learning model needs to collect some data. For machine learning, data entry differs from that of humans. Humans usually find it challenging to absorb a vast amount of data within a limited time frame. For machine learning, this is an easy task. More data entered into a ML-model will allow it to learn more, and increase its accuracy.
- Allow It to Extract Features
Next, the Machine Learning Model will extract those features which usually contain information on customers such as identity and location. Moreover, the features which describe fraud and normal behavior by customers are added. These added features will vary based on your detection system’s complexity.
- Initialize The Training Algorithm
In the third step, an algorithmic training is implemented for the model. Over a specified period of time the model will use a set rules to assess whether an action is legal or illegal. ML engineers commonly use supervised and unsupervised algorithms:
- Supervised Learning
With supervised learning, the algorithm learns based on answers and a given dataset. Information must be classified as positive or negative. The model will then use the data to make predictions about fraud. Included in supervised algorithms are:
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- Determining Trees -- Algorithm which sets up different rules in order to check data. For fraud prevention, decision trees are used to identify activity.
- Random Forest_ Built on decision-trees, random forest computes the average prediction of decision-trees. In order to find out more about random forests, a trusted created a blog.
- Logistic-RegressionA simple algorithm which predicts an event's probability based on a set of variables. To guard against fraud, financial institutions use logistic analysis to prevent.
- Unsupervised learning
A model that uses an unsupervised algorithm does not work with labeled data. In an unsupervised learning algorithm, instead of using labeled data to learn the model analyzes and processes new data. The model learns to identify patterns and differentiate between legitimate and fraudulent activity. Included in unsupervised algorithms for learning are:
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- K Means Clustering -- A clustering method that can be used to learn from unknown datasets. It does this by classifying the data into groups that have similar characteristics.
- The Local Outlier Factor - Similar to K Means, LOF is a clustering method that surveys data.
- Isolation Trees-Algorithm that relies also on decision trees. The random forest algorithm, unlike the unsupervised version, uses different rules and isn't supervised.
Once the ML-model has finished its training, it is now ready to be put into action. It should be possible to detect fraud in real-time. Over time, fraudsters create new methods to perpetrate financial fraud. For the ML to continue to work, it must be constantly tested and updated.
What is the purpose of machine learning for fraud detection?
Most companies relied heavily on rule-based system for fraud prevention. When using a rule based system, companies identify fraudulent activity by comparing the data to those written by cyber security experts. Every transaction must pass hundreds of checks to be verified. In case any test fails the transaction could be required to undergo another set of testing. Even though this may be a secure approach, it can be difficult to detect complex patterns that machine-learning is able to detect.
Machine learning can detect patterns within financial transactions, and determine if it is legit or not. Also, they can handle large volumes of data and find patterns humans might miss. As a result, machines learning models outperform humans. More data means more accuracy. In addition, as time goes on, there will also be more fraud-detection algorithms.
In light of all this, machine-learning models perform better in terms accuracy and speed. This is because you don't have to pay for a full team of analysts.