Machine Learning & Hadoop in Next-Generation Fraud Detection

Fraud detection and prevention is a real pain for the banking industry. The industry spends millions on technologies to reduce fraud, but most of the current mechanisms are based on static historical data. And it relies on pattern and signature matching based on this historical data, so first-time fraudulent acts are very difficult to detect and can cause a lot of financial loss. The only solution is to implement a mechanism based on both historical and real-time data. This is where the Hadoop platform and machine learning come into play.

Fraud and Banks

Banks are very vulnerable to fraud, as fraud is their main cause of money loss. An estimate suggests that more than $1.7 trillion is lost every year due to bank fraud. To prevent this, banks spend a lot of money on fraud prevention. However, they don’t spend much on protecting themselves. Therefore, current technologies with which banks today are equipped aren’t powerful enough. However, big data and machine learning can help to revamp the current system and lessen fraud to levels to an all-time low.

Current approaches to fraud detection have the following limitations:

Overlooking First-Time Fraud

The applications which are currently in place by banks for detecting scams are very old. In this method, the bank creates a very complex algorithm based on previous instances of fraud. This algorithm is then used in checking every transaction’s authenticity and legitimacy. This algorithm is very consistent and relies on older banking records and transaction signatures. Hence, due to this, many first-time frauds are overlooked, as they don’t have a signature. Furthermore, an algorithm isn’t very accurate, as only a small part of the whole fraud record is used for its derivation. Therefore, many frauds go undetected due to this reason.

Older Algorithms

In the case of current fraud prevention methods, proper updating of an algorithm according the most recent instances of fraud is necessary. However, often these models are updated annually because the cost and time required is so large. It is also very difficult to derive an accurate algorithm and use it. So, if the algorithm is not updated regularly, fraud can go unnoticed until the implementation of the newer algorithm, which may be deployed months or even years later.

Distinguishing Frauds From Genuine Transactions

Very often, many banks wrongly classify genuine transactions as fraudulent ones. This can be extremely harmful both for the bank’s reputation and the customer’s. This situation can really irk genuine customers who find that their transaction is canceled unnecessarily. For preventing this, the accuracy of the current fraud prevention system must be enhanced. These limitations of the current algorithmic system can be used for devising a newer system which will be much more accurate. So, such a solution is the need of the hour.

Solution to This Problem

A reliable and accurate solution is necessary to combat fraudulent transactions, while not hindering the genuine ones. This solution must be able to detect a wide variety of fraud types as each transaction takes place, and all in real time. The results must also be accurate so that legitimate transactions are not interrupted. But the real question is how the banking industry will reform its current fraud detection methods. How will it build a fraud detection application which is both efficient and fast, and can even stop those false positives that can disrupt the activities of genuine customers? The solution lies in machine learning based on big data platforms like Hadoop.

What Is Machine Learning?

Machine learning is the result of the integration between big data analysis and fraud detection. It happens when a system learns to process large data resources and also learn from its earlier experiences in the field. This helps the application to easily detect and intercept fraudulent transactions, even learning to recognize a specific kind of fraud for quicker detection in the future.

How Can Machine Learning in Hadoop Prevent Fraud?

Processing large amounts of data accurately used to be a herculean task, but with the advent of big data, several faster and more powerful data processing applications have been created. One of the most powerful of these applications is the Hadoop platform. Hadoop is extremely powerful because of its MapR feature, which allows it to easily process large amounts of data in real time, and very cheaply at that.

As Hadoop can easily process large amounts of data at once, it can be used to process all the older transaction records and signatures, and make an extremely accurate mathematical model. These transaction details can also be used to extract signatures, which will allow the bank to intercept first-time fraud transactions. However, the question which arises now is what tool can be used for processing the data and devising a perfect algorithm?

Tools for Preventing Bank Fraud

With the increase in bank fraud, a good fraud management application is the need of the hour. One of these tools is Skytree. Skytree is actually a special machine learning platform which promises to offer high accuracy and performance, even when the problem is processing large bank transaction data records. It is based on Hadoop’s MapR-type data clusters, which ensures big data processing in real time. It also can use a large variety of machine learning procedures, including supervised and unsupervised methods. Because of such efficient machine learning procedures, Skytree is able to stop fraudulent transactions with the help of an advanced model and even stop first-time frauds on the basis of its ability to intercept suspicious transactions. Skytree can automatically select the best information and use it to create a highly accurate model. It can easily analyze large amounts of data too, so it is easier to update the current model with its help.

Cons of Machine Learning

Machine learning may be a very powerful solution for fraud detection, but it can be a major challenge too. The concept is directly related to artificial intelligence. The fact that our machines will make the decisions for us may raise moral implications. However, there is no need to worry, as the application will work for us, and will make the best decisions when supervised by a human employee. Rest assured, machine learning will produce smarter fraud prevention techniques and will help prevent loss of money in the future.

Conclusion

The best fraud management application must be powerful, fast and accurate and must adapt to a variety of situations. For achieving this, the application must be able to churn out transaction details and signatures while keeping the database updated with the newest fraud types. Only a platform based on Hadoop will be able to do this, as platforms based on Hadoop are extremely fast machine learning applications which can support many different kinds of machine learning algorithms. Along with this, Hadoop-based platforms are very accurate too, so they can easily stop many instances of fraud from happening, as they can detect fraud in real time. This means that if a dedicated machine learning application is by the bank’s side, that bank has the power to be nearly invulnerable to fraud!