Going Deep With Deep Learning: The Science Behind Fraud Detection

Going Deep With Deep Learning: The Science Behind Fraud Detection

Machine learning has been sticking with us for a while now. It has been disrupting all possible sectors with its solution-based approach. With better processing power, availability of big data and advancements in statistical modeling, the power of science has been growing to calculate, predict and perform almost all the impossible functions; one out of which is fraud detection in the BFSI sector.


Yes, you read it just right! Fraud detection until the 20th century was never fully automated. With machine learning, the BFSI sector can distinguish between legitimate and fraudulent behaviors while recording and referring to previously unseen fraud tactics.


Generally, ninety percent of the fraud detection platforms/companies use the rules of accounting/query transaction which helps them in labeling a binary for authentic and fraud transactions. This is where it all goes wrong. The process of fraud detection is dependent on individual training and transaction guidelines, which can inevitably vary depending on the business type. Then how is a single rule book, the Bible for the entire BFSI sector?


Do you see some myths bursting? Hang on till the end to understand the process in and out.


Firstly, this is not how fraud detection works. Machine learning is all about algorithms that can learn from the past, access in the present and prepare for the future. The cycle of machine learning consists of Train-Test-Predict. That means better optimization of the cycle with big data; the better results can be obtained by applying cognitive computing technologies.


Sounds Interesting? Works Impressively!


Now, there are so many questions about why Machine Learning is better than general Human Fraud Detection because of the cost involved in setting up the whole technology.


Here is the answer which we call the’ SSE.’ 


  • Speed: Nothing can beat computers when it comes to solving problems at a larger scale. The work can be done in microseconds. Is it not fascinating? Machine learning can evaluate a good number of transactions in real time while continuously analyzing and processing new data.
  • Scale: Machine learning is like a process. It feeds on data and can be repeated several times. With the technology being at the disposal of the BFSI sector, it is easy to automate the process of segregating transactions based on their characteristics.
  • Efficiency: And yes! The human fraud detector loses here. The reason being the hungry machine learning technology being a pro at analyzing data in the least possible time with the highest precision.


So now when you know why it is done, let us tell you, how it is done?


  • Collection of Data: The basics of building a learning-based process for any sector or industry starts with collecting extravagant data and then dividing the data set into two parts. After analyzing the first half, cross-validation is done for assessing the second half.
  • Setting the Right Examples: The data used to train the ML models consists of records of both types of transactions (authentic and fraud) to train the algorithm right.
  • Building Models: Building models is one of the essential steps in predicting the fraud or anomaly in the data sets. These models can either be based on measuring the cause-and-effect relationship of the transactions, building a decision tree to automate the problem-solving assessment or constructing neural networks for making the machine mimic like the human brain by recording various patterns.


Now when you know how it is done, it is highly imperative to know who is doing it?


A prominent name in the industry, PayPal has started using Machine Learning for Fraud Detection. To detect fraudulent activities and to separate false alarms from true fraud, the company utilizes AI engines built internally with the help of open source tools. As a result, Paypal has decreased its false alarm rate by half. However, banks have been considerably slower in implementing machine learning and AI presently. However, situations are deemed to change.


Adding some last words of advice. It is not always feasible to implement homegrown solutions like PayPal to use machine learning for fraud detection. This is where Avyuct pitches in. Connect with our experts to know more about the scope of implementing this technology.


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