In today’s sophisticated, technology-driven world, fraudsters have developed a variety of clever ways to trick people into giving them their hard-earned money. However, banks can leverage several different solutions for detecting fraud in financial transactions before it becomes an issue.
How do banks detect fraudsters before they can strike, and what challenges do financial institutions need to overcome? In the following blog you’ll discover how big data analytics in banking plays a key role in detecting fraud for banks and how simple open banking api solutions can help protect your savings from being spirited away into the night.
But first, let’s discuss the different types of fraud that can be perpetrated against you before we discuss the more technical aspects of how your bank detects it.
What is banking fraud detection?
Over the last 10-15 years banking has moved online at an astonishing rate. Gone are the days when you needed to go to your local bank to physically get things done. Now there’s an app for all those things. Whether it’s setting up a pension or getting a mortgage, the need for person-to-person interaction in the finance industry has exploded thanks to the unprecedented growth of financial software development services such as payment gateway integration.
This is great for busy customers but presents several challenges for banks. After all, how do you know the customer you’re talking to is really who they say they are? For banks, this is where the need for fraud detection solutions comes in.
Fraud detection in the finance sector is used to confirm identities, track, or flag suspicious transactions, and generally identify fraudsters before they can do real damage. This is crucial as any money fraudsters obtain could cost banks millions in legal fees and cybersecurity costs – not to mention the devastating impact it can have on customer trust. Therefore, outside of handling financial transactions, identifying fraudulent activity is probably the second most important role banks engage in.
But what exactly does this mean? Well, before answering that, it’s worth examining some of the different types of fraud your local bank must watch out for.
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Types of banking fraud
The three most common types of fraud that banks must always remain vigilant for are:
- Identity theft – when fraudsters gain access to personal information and use this to apply for a credit card in the victim’s name, before draining their account through a variety of purchases.
- ATM skimming – when fraudsters place a small skimming device under an ATM that can record all your card information, enabling them to use that stolen information to make fraudulent purchases of their own using your money
- Money laundering – when a fraudster uses a legitimate business to hide proceeds from a criminal enterprise – money received from customers at a legal business is money which can be claimed as income, while the cash register – filled with the money earned from illegal activities – provides a means to get rid of this money without anyone knowing anything about it.
Of course, there are many other different types of fraud, but for the purposes of time, this article will only discuss the three main sources of fraud your bank has to frequently watch out for. But how do banks address these issues, especially in an age where anonymity is more popular than ever, and technology makes it easier to steal identities?
How do banks detect fraud?
Of course, technology plays a big role in detecting fraud – especially artificial intelligence (AI) – but believe it or not, one of the biggest methods of detecting fraud for your bank remains the old– fashioned anonymous tip line. According to the Association of Certified Fraud Examiners (ACF), fraud losses were 50% lower at companies that used this method of fraud detection. So, while AI hasn’t taken over just yet, it is playing an increasingly key role in fraud detection.
Machine learning (ML) is one of the main weapons AI has in its fraud detecting arsenal. On its own, ML can read and interpret vast amounts of data and recognize patterns easily. However, thanks to the focus on open banking in recent years, and the sharing of information between banks and third parties through APIs, ML-based systems now have access to more data than ever. This makes it easier for them to compare different data sets at speed and quickly identify anything that could indicate fraud – meaning its able to identify possible cases of money laundering, identity theft, and ATM skimming much faster than its human counterparts.
For example, ML-based systems could take the average yearly income of a business and compare that figure to the transactions flowing into its account. This comparison of general yearly industry income vs. business account would help the ML-based system determine whether this business might be a front for criminal activity based on the amount of money it takes in annually. If this amount is found to be well above average, it can flag this to a banking agent who can further examine its accounts.
Additionally, by quickly comparing purchase patterns on a customer’s account or comparing transaction patterns around a given ATM, and flagging anything suspicious to a banking agent, ML technology enables swifter customer confirmation around perceived suspicious activity – and faster cancelation and reissuing of an ATM card, if necessary. This makes ML technology stand out from the crowd of fraud detection solutions for banks – especially when it comes to combating identity theft, ATM skimming, and money laundering.
While this is good news for your bank, detecting fraud still has its challenges.
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Fraud challenges for banks
First and foremost, among these challenges is the speed at which fraudsters evolve their strategies. Everyone’s heard of the fake texts people receive from their “banks” asking for their account numbers or to click on a link provided in the text to confirm their identity. But the reality is, professional fraudsters moved on from this tactic years ago. They’ve evolved, so banks need to do the same, if they want to maintain customer trust in their brand. But doing so is difficult.
Of course, ML technology is extremely helpful in this regard, but it is only as good as the data it was built on, and the people overseeing its further development. Flaws in this technology’s training data can lead to perfectly legal transactions being identified as fraud – or worse – which can lead to customer frustration and even a loss in trust for your bank if this case of mistaken fraud is especially egregious.
Therefore, to keep up with fraudsters, and ensure they have the best ML-based systems possible, banks need developers with ML expertise who are passionate about the fraud detection solutions they develop for the financial industry. This passion will ensure your local bank will be able to keep up with the ever-changing strategies of fraudsters and guarantee that any ML technology they develop will be trained on the highest quality data possible.
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Banking fraud trends
In conclusion, the best way to end this blog is to remind banks that “evil succeeds when good men do nothing.” The reality is that as the influence of technology grows in our world, fraudsters will continue to find more avenues to potentially steal from customers.
But dedicated employees that stay up to date on the latest technology and cybersecurity trends, work alongside better, more improved ML technology, and act on tips they receive from anonymous, well-meaning members of the public will be your local bank’s secret weapons against fraudsters in the years to come.
Do you work in banking? Still worried you’re letting some fraudsters slip through the cracks? Software Mind can help. Contact us to discover how we have provided banks with proven fraud detection solutions, and a first-rate, memorable experience for customers.
About the authorSoftware Mind
Software Mind provides companies with autonomous development teams who manage software life cycles from ideation to release and beyond. For over 20 years we’ve been enriching organizations with the talent they need to boost scalability, drive dynamic growth and bring disruptive ideas to life. Our top-notch engineering teams combine ownership with leading technologies, including cloud, AI, data science and embedded software to accelerate digital transformations and boost software delivery. A culture that embraces openness, craves more and acts with respect enables our bold and passionate people to create evolutive solutions that support scale-ups, unicorns and enterprise-level companies around the world.