How Machine Learning Helps With Fraud Detection In correlation with the advancements in computer technology, e-commerce has also spread its wings simultaneously increasing the vulnerability rate of fraud. Unethical hackers are continuously finding new ways to target undeserving victims, from stolen credit card details to false accounts. Any kind online payment sources are open to fraud whether done by an individual or any business.
In the previous financial year, financial fraud that included card payment, remote banking and cheques took a spike of
26% from the previous year, totaling a cost of ÂŁ755 million. It was the fourth consecutive year that has seen an increase in this area is one of the most common crime these days. In the UK, with 2.47 million offenses reported in 2016-2017 alone.
Machine learning has been recognized as one of the keys to measuring fraud detections. A huge amount of is been transferred during online transaction processes that might actually result in two binary results:
Genuine Transactions Fraudulent Transactions
Many online businesses are now capable to identify fraudulent transactions accurately by the chargebacks received on them. However, the entire process happens once the transaction process is over making it reactive rather than being proactive.
Machine learning works on the basis of large, historical datasets that have been created using a collection of data from many clients and industries. Companies that are actually processing a lower amount of transactions are also implementing data sets for their vertical to take full advantage and get some accurate decisions on each
transaction. This kind of data aggregation helps you with some highly accurate set of training data that can be accessed enabling businesses to choose the right model to optimize the levels of recall and precision out from the transactions.
Where do such predictions come from? Features are created within the data sets such as the age and the value in the customer's account along with the credit card origin. There are numerous features that each of them contributes towards the fraud probability. Point be noted that each and every feature that contributes to the fraud score cannot be determined by a fraud analyst, but might have been generated by the artificial intelligence of the machine which is driven by the training set. Such proven features of machine learning-based systems make it possible for fraud analysts to identify the most significant contributors. Feedback from users to confirm the systemâ€™s decisions by marking customers as genuine or fraudster improves the machineâ€™s learning ability, adding to accuracy. So, what you are waiting for? Get your hands on this awesome tutorial for you to get started with machine learning in no time. This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.
Projects you will learn in this tutorial:
Stock Market Clustering Breast cancer malignancies Diabetes onset detection Credit card fraud detection Predicting board game reviews
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