September 20, 2023
While not known to many, the first artificial intelligence (AI) program was successfully used for the first time way back in the 1950s at the University of Manchester on a machine as a check-playing machine. Ever since, technology developers have made significant breakthroughs in enhancing and spreading the use of AI into a handful of industries.
In finance and banking, AI is gaining reputation as an enabler of large-scale data analysis andenhancer of efficiency. Specifically in the fight against fraud, AI and ML models definitely help increase the accuracy in detection as well as the heavy reliant analysts had on strenuous manual work.
Within this article, we discuss both the traditional and modern approaches to fraud prevention and how the latter is supporting banking operations.
Fundamentally, the idea behind banks is to operate based on the trust of their customers. As their services become more convenient, banks expand to provide more products and services that can flexibly be digitized, such as CNP payments.
These new offerings give customers a more friendly and smooth user experience, however, owing to the fact that they're conducted entirely online/ not-present, bad actors quickly learn to take advantage of the loopholes.
Attacks leading to fraud can take many forms to deceive end users, they commonly are phishing email scams, account takeovers (partly identity theft), stolen cards/ accounts for fraudulent purchases.
It's generally known that fraud exists way before technology chimes in. Traditionally, banks and financial institutions rely heavily on manual review of transactions and activities. This process is done by allocating specialized personnel to delve deep into the transaction data and it is rather time-consuming.Apart from that, manual reviews are prone to human mistakes that arise from fatigue, these mistakes can product many false positives and negatives
In the long term, this manual method hinders the organization from preventing fraud in the case of misinterpreted data. As suggested by industry experts, financial institutions must adopt more efficient ways to secure accounts and detect fraud early instead of only reacting to them when they happen. This is where new technologies like AI come into play.
Today, there are many solutions supported by advanced technologies used to leverage large data sets. Systems will analyze the data, and give conclusions whether a specific transaction is genuine or fraudulent, with the approach mostly focused on tracking suspicious activities.
Owing to the large amount of data being processed every day in the banking and finance industry, data mining and analytics can be used to learn more about not only transactions but also web-browsing behaviors and device analytics.
Machine Learning (ML) and AI-based fraud detection is applied for banking through analyzing massive amounts of in and out-coming transaction data. What this does is that it leverages the data source to identify trends and detect abnormalities leaning towards fraud-like behavior.
Owing to the ability to train itself, ML models can, from the identified trends, get more informed about and get used to different types of fraud attacks. From continuous learning and adjustments, they can immediately notify of potential fraud and suggest, or be automated to take appropriate prevention actions.
Technically, ML relies heavily on the algorithm analysis of large datasets to constantly train and retrain the system. The application of these models into fraud prevention in banking is growing in popularity due to its accuracy and efficiency.
Generally, fraud fighting solution providers develop their tools around rule-based algorithms that can detect previously identified types of fraud, but from time to time they cannot completely avoid false positives and would require frequent updates. Other than that, more complex and sophisticated ML algorithms can work to examine datasets and predict future events (cases) based on them.
With support from ML and AI, the banking industry can continue to leverage fast-speed and accurate identification of fraud. On top of that, by replacing manual reviews with technology, banks can finally lift the burden from risk analysts and managers while shifting them into more strategic roles that would help safeguard transactions much more proactively.
As new threats emerge, providers of anti-fraud solutions will continue to innovate and elevate their tools to include more functions and features. Moving on, the industry has great expectations in AI/ ML technology to improve upon detection sensitivity as well as prediction accuracy.
Thinking of onboarding a fraud prevention program? Get in touch with us today for a one-on-one consultation for risk detection, fraud prevention, and safe authentication solutions.
Visit our website at www.hitrust.com
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