AI for fraud in financial services and banking: use cases and solutions

Navigating the complex landscape of AI in fraud perpetuation and detection within the scope of the financial sector.

The November 2022 report by ABA Banking Journal reflects that the financial industry and banking sector have been significantly impacted by fraud, with every  $1 lost to fraud now costing $4.36 in related expenses, such as legal fees. As these costs continue to rise, Artificial Intelligence (AI) has emerged as a double-edged sword – a tool that can be used for both perpetuating fraud and detecting fraud as a service itself. This article explores this dual role of AI and our  key focus is to show how AI can be used to promote fraud, but also  how the technology can help prevent that from happening in the first place.

Cost of fraud in the banking and financial sector

The cost of fraud in the banking and financial sector is a multifaceted issue which affects the financial health of those institutions as well as their customers’ trust in them. It’s true that banking fraud is a burgeoning  concern that requires constant vigilance, but sophisticated solutions to mitigate the fraud are also advancing.

Top 5 recent cases of fraud among financial institutions

Lately, the financial sector has witnessed some very high-profile banking fraud cases. These incidents are a stark reminder of the pervasive nature of fraud within financial institutions and the need for robust fraud prevention and mechanisms.

1. T.D. Bank, Capital One, and Wells Fargo Lawsuits. These banks faced lawsuits pressing them to take responsibility for the money customers lost to fraudsters.
2. Theft of COVID-Relief Funds. The massive theft of COVID-relief funds continued to play out in 2021, affecting numerous financial institutions.
3. Imposter Scams. The FTC reported that the most commonly reported frauds were imposter scams, followed by online shopping and negative reviews.
4. Trade-Based Money Laundering (TBML). TBML was one of the new challenges and criminal schemes that fraud and financial crime professionals had to deal with in 2021.
5. Danske Bank Fraud. Danske Bank pleaded guilty to defrauding U.S. banks in a multi-billion dollar scheme to access the U.S. financial system.

The trajectory of fraud can veer into two different directions. On the one hand, AI can potentially enhance fraud risk and fraudulent activities by providing sophisticated tools to fraudsters. But on the other hand, AI can mitigate the risk of fraudulent activities by bolstering the capabilities of fraud detection systems. Therefore, it is crucial for financial institutions to invest in advanced AI-based fraud detection and prevention systems, and to advocate for more compliance standards, fraud detection mechanisms, proper tools, and regulatory rules that guide the use of AI.

AI-based fraud use cases

AI-based fraud in the financial industry and the online banking sector is a growing concern. Fraudsters increasingly leverage advanced technologies and specialized tools for sophisticated scams and credit card fraud. Here are some examples of how Artificial Intelligence is used in fraudulent activities.

Synthetic identity fraud

Fraudsters use AI to generate synthetic identities, which are combinations of real and fabricated information. They use these identities to open fraudulent customer accounts and to transact within the customer’s account. AI can generate realistic personal details, making it difficult for traditional fraud detection systems to identify these synthetic identities as dishonest. For example, according to VentureBeat, attackers harvest large quantities of available PII (personally identifiable information), starting with identification numbers and birth dates, in order to create new synthetic identities.

To delve deeper, these blended synthetic identities are often created using legitimate information from stolen data, such as Social Security numbers, while forged information can include made-up names, addresses, or birth dates. The AI algorithms are capable of creating synthetic identities that are highly convincing and that can even pass traditional verification checks. These identities are used to open bank accounts, apply for credit, make fraudulent transactions, and/or make fraudulent purchases, causing significant financial losses by means of an account takeover.

Deepfakes

AI can create deepfakes, which are real fake videos or audio recordings. In the financial sector, fraudsters can use deepfakes to impersonate executives or other key personnel so as to authorize fraudulent or unauthorized transactions in online accounts or to manipulate stock prices. For instance, as reported by FinTech Futures, a criminal can create a deepfake of an applicant and use it to open an account, bypassing many legal requirements and the usual checks. These all can lead to a rapid account takeover with a person not even realizing what is happening.

Deepfakes leverage AI and Machine Learning (ML) techniques to manipulate or fabricate visual and audio content with the high potential to deceive. The technology can create realistic-looking photos and videos of people saying and doing things they never did, which can result in identity theft and manipulation. A notable example of this is the viral deepfake of Tom Cruise, created by VFX artist Chris Ume, which garnered millions of views on TikTok. The deepfake was so convincing that it sparked debates about the authenticity of the video. This CNN article and this YouTube video provide more insights into the creation of the Tom Cruise deepfake.

Automated Hacking

AI and ML can even be used to automate hacking attempts. For example, AI can be used to carry out brute force attacks, where the system tries every possible combination to crack a password. AI can also be used to identify vulnerabilities in a system that can be exploited. As per MDPI, bank hackers might employ AI algorithms to carry out data breaches or discover weak points in the banks’ security systems.

In more technical terms, AI can be used to automate the process of discovering and exploiting weaknesses  in software and hardware systems. This can include anything from identifying weak passwords using brute force attacks to scanning for unpatched software vulnerabilities that can be exploited. AI can also be used to automate the creation and distribution of malware, making it more efficient and effective at infecting systems and evading detection. For example, AI could be used to create polymorphic malware, which changes its code to evade signature-based detection systems.

Social Engineering

AI can be used to carry out sophisticated social engineering attacks. For instance, AI can analyze a person’s social media profiles and other online activities to create personalized phishing emails that are more likely to be successful. AI is changing social engineering by making it easier for threat actors to mine behavioral data to manipulate, influence, or deceive users in order to gain control over a computer system, which can often result in identity theft.

For instance, AI tools can be weaponized for phishing. Researchers used a complex technique called Indirect Prompt Injection to manipulate a Bing chatbot into impersonating a Microsoft employee, then generated phishing messages that requested credit card login information to be verified back by the users, as reported by Forbes.

These examples illustrate the potential threats posed to other financial institutions by the misuse of AI in the financial sector. It is crucial for financial institutions to stay updated on these trends and invest in advanced security measures to counter these threats.

Potential future developments in AI for fraud detection and banking fraud detection

While we have explored how AI can be exploited for fraudulent activities, it is equally important to highlight the potential of AI as a powerful tool for fraud detection in banking and its application into fraud prevention solutions. As technology evolves, so do the methods of detecting fraud. This section delves into the promising developments in AI that could significantly enhance fraud detection and prevention within the financial sector.

1. Integration of AI and Blockchain. One of the most exciting developments in the future of AI is financial fraud detection which includes the integration of blockchain technology. Blockchain provides a secure and transparent ledger that can be used to store transaction data, while AI can be used to analyze the data and detect fraud.
2. Real-Time Fraud Detection. AI technologies are expected to improve real-time banking fraud detection capabilities. By identifying anomalies, AI can help banks and their bank fraud detection systems take immediate preventive actions when suspicious activities occur.
3. Improved Machine Learning Algorithms. Machine Learning algorithms are expected to become more sophisticated, enabling them to detect more complex patterns and behaviors which indicate banking fraud.
4. Personalized Fraud Detection. AI can also be used to create personalized fraud detection systems that take into account a user’s specific behaviors and patterns so as to identify fraudulent activities more accurately.

As we look to the future within the financial services industry, it is clear that AI will play a pivotal role in shaping the landscape of fraud detection in banking. However, the journey does not stop here. The next section will delve into the broader implications of AI in shaping the future of the financial industry and banking fraud. Stay tuned to explore how AI is set to revolutionize the financial world beyond just bank fraud detection.

The role of AI in shaping the future of the financial industry and fraud detection in banking

AI is expected to play a crucial role in molding the future of the financial industry and banking sector. As AI technologies continue to evolve, they will likely become an integral part of these sectors, helping to improve efficiency, reduce costs, and enhance customer experience and service. However, as AI becomes more prevalent, it is also likely to be used more frequently for fraudulent activities. Therefore, financial institutions must invest in advanced analytics, and AI-based fraud detection and prevention systems.

Of course there are several standards and regulations for the usage of AI in the financial sector and banking already in existence. While these regulations may vary by country, they generally aim to ensure the responsible and ethical use of AI. Here are some important credible sources exploring the subject matter in greater detail:

1. Humans keeping AI in check – emerging regulatory expectations in the financial sector is a paper by the Bank for International Settlements that discusses the surfacing common themes on AI governance in the financial sector and the potential for financial standard-setting bodies to develop regulations.
2. The case for placing AI at the heart of digitally robust financial regulation is an article by Brookings that argues for the importance of regulating the use of AI in the financial sector.
3. Does Your Current Use of AI in Financial Services Align with the US “AI Bill of Rights”? is an article by BCLP Law that discusses the existing US bank regulatory guidance on AI and how it aligns with the US AI Bill of Rights.
4. AI in Financial Services in 2022: US, EU and UK Regulation is an article by Pymnts that provides an overview of the current state of AI regulations in these entities.
5. The State of Responsible AI in Financial Services is a report by FICO that discusses the importance of responsible AI in financial services and the need for regulation-ready AI.
6. AI Regulation of financial institutions: EU and beyond is an article by Norton Rose Fulbright that discusses the proposed EU Commission AI regulation that would apply to financial institutions.

These sources provide a wealth of information on the topic of Artificial Intelligence. They should be helpful in understanding the current state of AI regulations within the financial sector and banking.

Wrapping up

As we navigate the evolving landscape, it is evident that AI can be used for good and bad. However, while the potential for misuse of AI technologies is a growing concern, the advancements in AI-based fraud detection and prevention systems offer hope. These systems are becoming increasingly sophisticated, and are capable of detecting complex patterns and behaviors that indicate fraud.

As we look forward, it is crucial for financial institutions to continue investing in these advanced systems and to advocate for more compliance standards, fraud prevention mechanisms, as well as rules that guide the use of AI. By doing so, we can ensure that AI serves as a force for good, helping to enhance the financial industry and banking sector rather than harm it.

If you want to learn more about how AI can be leveraged to keep banking customers, combat fraud, and prevent money laundering, contact us at Avenga to get more information and insights.