Online transactions have shot up manifolds and so have cases of online fraud. The Consumer Sentinel Network maintained by FTC received 3.2 million reports of identity theft and online fraud in 2019. With fraudsters becoming more adept at finding and exploiting loopholes in systems, fraud management has turned painful for the banking and finance industry. Thankfully, machine learning for fraud detection has come to the rescue of financial organizations.
Machine learning has been instrumental in solving some of the important business problems such as detecting email spam, focused product recommendation, accurate medical diagnosis etc. The adoption of machine learning (ML) has been accelerated with increasing processing power, availability of big data and advancements in statistical modeling.
Data scientists have been successful in authenticating transactions with machine learning and predictive analytics. Automated fraud screening systems powered by machine learning can help businesses in reducing fraud.
How to Detect Fraud Using Machine Learning?
Extract Data
Generally, the data will be split into three different segments – training, testing, and cross-validation. The algorithm will be trained on a partial set of data and parameters tweaked on a testing set. The performance of the data is measured using a cross-validation set. The high performing models will be then tested for various random splits of data to ensure consistency in results.
Provide Training sets
The main application of machine learning used in fraud detection is the prediction. We want to predict the value of some output (in this case, a boolean value that is true if the payment is fraudulent and false otherwise) given some input values (for example, the country the card was issued in and the number of distinct countries the card was used in the past day). The data that is used to train the ML models consists of records with both the output values for various input values. The records are often obtained from historical data.
Building Models
Building models is an essential step in predicting the fraud or anomaly in the data sets. We determine how to make that prediction based on previous examples of input and output data. We can further divide the prediction problem into two types of tasks:
- Classification
- Regression
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