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Can machine learning be used in order to identify fraud detection?
We are moving fast towards a cashless society. Non-cash transactions reached 482.6 billion in 2016. Electronic wallets alone represented 41.8 billion. This huge amount of transactions makes this type of payment appealing for frauds. Indeed, for scammers and thieves, credit cards are a holy grail.
There are several types of credit card frauds. The most important one is the credit card not present fraud (CNP). Any purchase without the use of a merchant terminal or an ATM is a CNP purchase.. CNP purchase amounts for 85% of all credit card frauds.
Banks rely on rule-based programmed systems to identify security breaches, resulting in 1% of the transactions being fraudulent.
Machine learning can provide a way to detect such CNP thefts in order to reduce this 1% of fraudulent transactions.
Online merchants, banks, credit card companies, credit reporting agencies, law enforcement agencies, can benefit from the use of predictive modeling in credit card fraud detection. Stolen card information concerning very small transactions are more likely to be unnoticed. Performing an automatic detection of such small fraudulent purchases is like looking for a needle in a haystack. It consists of identifying weak signals among a huge quantity of genuine purchases. Even though the overall data handled by the financial industry are Big Data (transactions are usually in millions or even billions), fraudulent transactions, less than 1%, are Small Data.
Traditional machine learning tools work well with Big Data but do not perform well on a Small Data subset within a Big Data collection. This is typically an imbalanced dataset where the actual data to identify and predict from is present in very small quantity among huge quantities of data. This is also referred to as ‘weak signals’.
MyDataModels allows the financial industry to automatically build Small Data predictive models. No need of specific training. Raw data can be used directly. There is no need to normalize the data, handle outliers or run feature engineering. The data can be used seamlessly.
The results from this specific dataset were obtained with a few clicks in less than 10 minutes on a standard laptop. Using TADA with this data set, a 99.78% accuracy of predicted fraudulent transactions was achieved.
MyDataModels brings a self-service solution for the professionals who have Small Data to analyze and predict from.
According to a report published by Nilsen, in 2017 worldwide losses in card fraud reached $22.8 billion and is expected to reach $32.96 billion by 2021.
In 2017 the average consumer experienced an average loss of $429. 21% of credit card users suffered such a theft. This results in heavy financial losses for the financial industry but also in a breach in customer trust.
“Predictive model allow
significantly reduce of illegal transactions”
By accurately predicting which transactions are likely to be fraudulent, online merchants, banks, credit card companies, credit reporting agencies, law enforcement agencies can significantly reduce illegal transactions and provide credit card users an excellent customer experience.
Fraudulent activity is a high-cost threat with multiple consequences, including eventually hurting compagnies bottom line. The use of fraud detection analytics based on an automatic machine learning solution such as TADA empowers companies to identify these malicious activities right away and put a stop to them.
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