Financial fraud costs consumers, merchants, and big banks billions of dollars every year. It's not just the money that's stolen, either, investor Daniel Calugar says; it's the associated costs of trying to recover or to cover the money lost.
A recent study from LexisNexis Risk Solutions found that every $1 lost to fraud in the United States costs financial services firms $4.23. That's an increase of 16.2% over 2020 levels, according to the study "Trust Cost of Fraud Study: Financial Services and Lending."
The FTC reported roughly 2.8 million U.S. consumers filed a report of fraud with the agency that year, which was the highest number since 2001. Of those reported scams, approximately 25% led to a loss of money, with an average loss of $500.
Fraudsters today target multiple sources, including mobile devices and the apps that run on them, an increasing number of bot attacks, and various "traditional" scams that steal a lot of money.
To counter these scams, financial firms are integrating artificial intelligence (AI) to be proactive in the fight against unauthorized banking access and fraudulent charges.
Through AI and machine learning, suspicious charges are caught and stopped before the merchant loses the product to fraud, while the consumer is protected from monetary loss.
Banks can also automate account locks, which makes fraud detection much faster and more efficient.
Reasons Why AI is Crucial to Protecting Your Financial Accounts
It's clear, according to Daniel Calugar, that financial fraud is a complicated and complex problem for all financial institutions. It's something that isn't easy to tackle, yet it's essential for them to get a handle on it and be proactive about it if they want to be successful in what they do.
Today, a successful fraud prevention system can only work to the best of its ability if it integrates AI and machine learning technology. Here are some main reasons why this is the case.
Rules-Based Prevention is Outdated
For many years, financial institutions, e-commerce businesses, retailers, and other types of companies relied on fraud prevention approaches that were rules-based. This worked for a while, at least in the early days of the explosion of e-commerce.
Today, though, financial fraud and scammers have evolved to be more sophisticated and nuanced, Dan Calugar says. As a result, these entities required a system for payment fraud prevention that could keep up with the advanced fraud itself.
One of the challenges of fraud attacks today is not only are they increasingly complex, but they do not leave the same digital footprint as fraud of the past. In addition, there isn't a set structure, sequence, or pattern -- at least not compared to what there used to be -- which makes them hard to detect using simple predictive models or a rules-based approach.
AI, teamed with machine learning, can keep up, though, identifying new and unique patterns or deviations from patterns that could be the early signs of fraud.
Advanced data analytics powered by machine learning and AI can help firms identify when fraud attempts are made while simultaneously avoiding what would be considered "acceptable" deviations from the data. In other words, these advanced analytics will prevent actual fraud attempts while allowing legitimate transactions to go through.
Think about this from the consumer standpoint. Daniel Calugar points out that consumers would be okay with their debit card provider blocking transactions that they obviously didn't initiate -- whether it be because of purchases that were done either outside their home location or their normal spending range.
At the same time, consumers don't want to go through the hassle of verifying every transaction they try to make simply because the purchase didn’t fit into a pattern of past purchases. AI and machine learning can help wade through all of these transactions to prevent fraudulent transactions while not erroneously stopping legitimate transactions.
AI Works Quickly and Accurately
The two most significant benefits that AI provides for financial fraud detection are speed and accuracy. AI can pour through a considerable amount of raw data very quickly. It can also compare that data to other information to help determine whether something is inside or outside an individual consumer's behaviors.
This all happens in real-time as well, which exponentially increases the practice of fraud detection, according to Dan Calugar. In essence, the fact that the analyses are being done in real-time means that fraud will be prevented rather than just detected. This reduces the threat of schemes that could be instituted on an ongoing basis at different accounts at the firm.
Moreover, due to the increased speed at which AI processes the data -- and the increased efficiency it brings to fraud prevention -- it's also much more accurate than any manual human-based system or even rules-based software.
As mentioned before, accuracy is a key determining factor in ensuring that fraud is detected properly without shutting down accounts or denying transactions that shouldn’t be. Old software that is subpar tends to flag legitimate transactions as fraudulent, causing headaches for both the consumer and the financial institution.
Consumers appreciate that financial institutions are doing all they can to prevent them from losing money due to fraud. But, at the same time, they don’t want to have to jump through hoops just to get legitimate transactions approved.
Risk Managed Based on Algorithms
Financial fraud doesn't only occur through transaction attempts. It can also happen with full-blown breaches of systems at financial firms. A recent Flashpoint study showed the enormous impact of these breaches.
In 2022 alone, nearly 80 financial services firms in the United States reported a data breach that affected at least 1,000 consumers. More than 254 million consumer records were leaked in breaches last year, Flashpoint said. The Attorney General of Maine noted that roughly 9.4 million U.S. consumers were affected in some way by the data breaches that occurred at financial firms.
Consumer website Comparitech conducted a study that found that, between 2018 and 2022, there were 982 financial breaches, with more than 153 million individual records affected. The leading sector for these breaches was insurance, followed by banks and investment companies.
With these data breaches seemingly becoming commonplace, financial firms in every sector are starting to ramp up their risk management practices by integrating algorithms into the process, Daniel Calugar says.
Using machine learning, the algorithms can help firms strengthen their risk management practices, in essence, centralizing the risks the firm could face and offering tips for mitigating and managing those risks.
Many financial institutions have teams of workers who are dedicated to regulatory compliance. While AI can't completely replace these people, it helps to keep the team on task and ahead of the curve.
As regulatory compliance requirements change, AI helps compliance teams review the requirements and improve their decision-making to help the firms maintain maximum levels of protection.
This helps financial firms protect consumer data by keeping them in compliance with up-to-date regulations.
AI Thrives on Excessive Amounts of Data
When humans are given a large amount of information and statistics, it can be very easy for them to get overwhelmed. More doesn't necessarily mean better in this regard.
However, with AI and machine learning, the more information you feed them, the better they perform. This is because this type of technology thrives on data that is expansive and deep. An algorithm based on machine learning, for instance, will learn more and become more effective the more data it has and the more analysis it conducts.
This is especially true in terms of detecting financial fraud. Algorithms can quickly learn to differentiate between legitimate and fraudulent transactions based on the contextual intelligence it deduces from the data.
The machine learning technology will analyze individual account data over history, fed by a centralized data network. When supervised by trained human security personnel, these algorithms will gain a significant level of predictability and accuracy.
These learning models truly have no limits, either. They can be used across different geographies, verticals, and complexities for all types of financial transactions. Moreover, with the right inputs, AI models can be trained to predict risk accurately.
Moreover, the technology can combine data produced by physical, in-person, and digital transactions. This gives the system a fully integrated look at complete customer behavior.
Dan Calugar says this is why AI is essential in protecting consumer financial accounts.
Types of Financial Fraud
One of the biggest challenges that financial institutions today face is there are so many different types of financial fraud. It doesn't take just one form or come from just one source. This fact necessitates advanced technology to identify and prevent fraud from happening to consumer accounts.
Bad actors can gain access to customers' information when they're doing everyday transactions. Even more complicated transactions, such as opening a new account or sending a wire transfer, don't require customers to travel to a branch anymore.
With so many financial transactions happening online today, this presents a significant opportunity for information to be stolen. As a result, financial institutions must have a robust cybersecurity framework in place to prevent this type of fraud. This includes safeguards from having accounts hacked and identifying online transactions that could be fraudulent.
While branch locations aren't as essential to the operation of financial institutions today, they still serve as potential access points for hackers. Almost all financial institutions have centralized their critical data off-site and in the cloud, but branches still have systems hooked into the main network that could provide a way in.
This necessitates the usage of advanced security software to monitor the network infrastructure at each branch. In addition, AI and machine learning can be loaded onto these systems as well as the main centralized network to identify any potential irregular or extraordinary sets of transactions that need to be prevented before consumer data and money is stolen.
The most common financial fraud affecting individual consumers happens on debit and credit cards. Almost all financial institutions have to meet compliance requirements for PCI DSS, and payment cards are consistently the subject of financial fraud.
Fraud prevention software has been in place for a while now to prevent card numbers from being taken from POS machines at stores, but it's become much more complicated today. Even secure chips and tap-to-pay cards still have vulnerabilities -- if only because consumers use their cards online so often by entering their numbers, which bypasses those extra security measures.
This is why something more advanced, in the form of AI, is needed to help protect your finances.
ATMs present a huge risk for breaches of customer information. This is because bad actors have been able to attach what are known as skimmers to ATMs so they can read both the card information and the PINs for every card used at the machine.
While this type of financial fraud is typically segregated to one individual ATM, it presents a considerable fraud prevention challenge, specifically because the machines process information from many financial institutions. This is especially true of ATMs in high-traffic public places such as gas stations, rest stops, and shopping malls -- compared to ATMs located at bank branches.
While it's next to impossible for AI to prevent this information from being stolen or compromised from ATMs, it can be put to work to prevent the bad actors from successfully conducting fraudulent transactions with that information in the future.
So much attention in fraud prevention is focused on the point of the actual payment. Yet, a lot of fraud occurs through identity theft. If someone can steal another person's banking login information, for instance, they could easily transfer money out of that account and into their own -- or make payments or purchases on their behalf.
This is a significant fraud prevention challenge for financial institutions today. Luckily, anti-fraud software powered by AI can help identify whether identity theft has occurred, including analyzing when and how a user might have changed their password and contact details. This is one of the most common patterns that can be tracked for account takeovers.
According to Dan Calugar, AI systems will learn the usual patterns that customers take and compare that to patterns that bad actors take. Then, the system will be able to crunch the data it's fed to search for any action likely to signal identity theft.
With this information in hand, the system can take automatic action, such as freezing an account with suspicious activity, until the consumer can verify that they made the changes. This can prevent a significant amount of financial loss to both the consumer and the financial institution.
While forgery isn't as big of a problem today as it was in years past, it's still something that financial institutions need to pay attention to. When checks were omnipresent, forged signatures were a major issue for consumers worldwide.
Today, the number of checks being written has dwindled significantly among consumers. However, small- and mid-sized businesses still deal in checks in large quantities, providing ample opportunity for bad actors to conduct fraud.
It may seem unlikely that AI and machine learning technology could help with something like forged signatures or fake IDs, but they certainly can. Humans may not be very adept at spotting inconsistencies in signatures or whether an ID is real or fake. However, AI-powered systems can do this in an instant.
For instance, tellers can scan signed checks into a back-end system and have the AI and machine learning technology compare what's written to what's on file. It goes one step further, too, identifying whether the signature is a legitimate variance on an individual's typical signature or whether it's a complete forgery altogether.
This reduces the risk that someone could forge your signature and cash a check using an ID that isn't real.
In other words, as Daniel Calugar says, there isn't an aspect of financial fraud prevention today that can't be positively aided by AI and machine learning technology.
About Daniel Calugar
Dan Calugar is a highly experienced and multi-talented investor with a diverse background in computer science, business, and law. He discovered his passion for investing while working as a pension lawyer and utilized his technical expertise to create sophisticated computer programs that helped him uncover more profitable investment opportunities. In his free time, Dan enjoys staying physically active, spending quality time with loved ones, and giving back to his community through volunteering with Angel Flight.