What Is The Role of Machine Learning In Financial Fraud Prevention?

What is the role of machine learning in financial fraud prevention?

The instances of fraud rose between 2015 and 2018, there was also an increase in the total value and volume of fraud, according to KPMG. Needless to say, financial fraud is alive and well, despite increasingly stringent measures to reduce numbers and tighten security.

Out of all fraud instances, banks reported that cyber-attacks were the most imminent risks, no doubt due to the burgeoning use of technology within the sector without adequate security. The solution is a tad ironic- to fight technology with more technology, like fighting fire with fire. Financial fraud prevention is best countered by machine learning, a subset of Artificial Intelligence.

Machine learning may be an alien concept to many, but it plays an intrinsic role in our daily functioning– even more so as times become for futuristic. Detection of email spam, product recommendations on your favorite video network or even image recognition algorithms on cellphones are examples of machine learning in everyday use.

For the banking industry, machine learning comes off much like a savior, especially in light of the many disadvantages of traditional fraud detection.

Traditional methods were heavily based on rules, which meant they could be inefficient, erroneous or hard to scale. This formed a weak buttress against sophisticated hackers who are well-versed in enhanced fraudulent methods and was as good as having no firewall after a point.

Against all of this, machine learning has an important role to play, as any Machine Learning course would tell you. Here are the benefits of using machine learning for financial fraud prevention:

Scalable, cost-effective algorithms

The benefit of machine learning algorithms is that more data means more precision. This is a definite step-up from traditional methods where bigger datasets could lead to incorrect data or even crashes. In machine learning, the algorithm learns with more data as it picks out outliers, nuances and different patterns to provide more accurate results in a more efficient manner.

It’s cost-effective, too– rule-based methods required a lot more in terms of costs to scale, whereas machine learning setups need a few tweaks to get back up and running after an update in datasets. An additional benefit is that machine learning algorithms can repeat menial, frustrating tasks 24/7 without a hitch– a feat highly impossible if a human employee were involved.

Real-time processing

In traditional methods of fraud detection and prevention, the focus was more on long-term processing, which often led to delays in resolving fraud reports and transactions. That naturally led to a lot of unhappy customers, but Machine Learning can successfully put an end to that saga.

The results from machine learning algorithms are real-time, which means instant updates and immediate results. Not only does this lead to quicker fraud resolutions, but it also helps banks identify loopholes in their system and fix them immediately.

Reduce time and increase accuracy

The main draws of machine learning are its ability to reduce the time and effort taken to detect fraud and prevent it. There are claims that a fully-ready machine learning algorithm can detect up to a whopping 95% of fraud, that too accurately. According to another Capgemini report, fraud detection software that uses machine learning can reduce the time taken to investigate by 75%, all the while improving the accuracy of fraud detection by 90%. Needless to say, machine learning purports a lot of benefits when used in the banking sector for fraud detection.

Applicable across industries

The banking sector liaises with multiple other industries, as do their customers. However, the fraud cases they deal with are often dissimilar in intensity, method, uses, and reasoning. It only makes sense, then, that a fraud detection setup is capable of handling fraud detection and resolution across all industries.

With machine learning, algorithms can be scaled or adapted for different industries including e-commerce, medicine, hospitality, retail, and insurance. By using unique datasets for each sector, similar machine learning algorithms can be used to cater to each, instead of limiting all claims to one or a few traditional fraud prevention methods.

The financial and banking sector has a lot to benefit from the use of machine learning in fraud detection– today’s machine learning courses today cover these uses in-depth, making aspirants ready for changing technology in a traditional industry.

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