Account Takeover (ATO) Attacks: Are Banks Prepared?

Account takeover (ATO) fraud poses a significant threat to financial institutions, with banks often being prime targets. This form of fraud, a specific type of payment fraud, not only impacts financial assets but also undermines customer trust and institutional compliance. By accessing and manipulating customer accounts, fraudsters can perform unauthorized transactions and siphon funds, causing significant financial and reputational damage. This article delves into the intricacies of account takeover, offering banks actionable insights on detection and prevention methods.

What is account takeover fraud?

Account takeover involves unauthorized individuals accessing and manipulating a bank account to perform illicit transactions. The effects of such fraud are multifaceted, resulting in direct financial losses and potential long-term damage to a bank's reputation.

What are the signs of account takeover?

Banks must be vigilant in recognizing signs that may indicate an account has been compromised:

• Unusual account activity: any abrupt changes in account behavior, such as atypical withdrawal patterns or large transactions to unfamiliar beneficiaries, should trigger a review.
• Multiple failed login attempts: a spike in failed login attempts can suggest that someone is attempting to crack a customer’s password, indicating a possible attack in progress.
• Strange geographical login patterns: logins from geographical locations that do not align with the customer’s known patterns can be suspicious, especially if they occur in quick succession from vastly different regions.
• Alterations in account details: unauthorized changes to key account information like contact details, linked emails, or phone numbers often precede fraudulent transactions.

Early detection of these signs is critical for banks to intervene effectively and prevent further exploitation.

Impact of account takeover on the financial market

Online fraud reached a considerable surge and continues to rise with each passing year. However, quite a substantial amount of this fraud is related to fake accounts and account takeover. It has created a financial impact and has imposed a powerful toll on banks and their customers.

A study conducted by Javelin unveils a massive increase in traditional identity fraud losses in 2021, reaching USD 24 billion in the United States alone. Identity fraud scams added $28 billion in losses, in addition to a marked 109% increase in new account fraud and a 90% rise in account takeover losses. 

Account takeover is also affecting people directly and more personally than ever. According to a recent survey (over 1000 people), 29% of people have experienced account takeover, which marks a rise from 22% from the previous year. Additionally, social media accounts were most frequently hacked, accounting for 53% of ATO.
 
Another result according to recent research is that data breaches increased 15% YoY during 2023. It was reported that this was the highest it’s ever been. With the rise in fraudulent attacks comes the alarming statistic that their favored methods are stolen identities or synthetic identity fraud, but their top choice is Account Takeover (ATO), account login, and fake account creation.

But how do these fraudulent accounts even get created and what are the methods of the attackers? Continue reading to find out.

How do attackers execute account takeovers?

Attackers employ a variety of sophisticated methods to execute account takeovers, exploiting both technological vulnerabilities and human factors.

Techniques used in account takeovers

The evolution and growing sophistication of fraudsters, particularly in account takeover, has led to several advanced methods and tactics that may not be that easy to spot, let alone prevent.

Here are some of these methods:

• Phishing and social engineering: social engineering relies on human interaction and often involves deceiving people into breaking normal security procedures. Fraudsters use psychological manipulation to trick individuals into revealing sensitive information such as passwords or banking details. Common strategies include pretexting, where the fraudster creates a fabricated scenario to obtain needed information, or baiting, where they offer a false promise to lure the victim into a trap. Fake accounts are not a new problem in social media. In fact, 21.6 million fake LinkedIn accounts were either removed or blocked during Q1 2019 and 2 billion fake Facebook accounts were closed during that same year. Phishing is perhaps the most familiar form of social engineering, phishing involves sending fraudulent communications that appear to come from reputable sources. It is often carried out via email, but also through phone calls, text messages, or social media. These messages are designed to instill urgency, fear, or curiosity in the victim, prompting them to reveal confidential information, click on malicious links, or download harmful attachments.
• Malware and spyware: malicious software can be installed on a user’s device without their knowledge, designed to steal credentials directly or intercept them during transactions.
• Credential stuffing: attackers use previously breached username and password pairs to gain unauthorized access to accounts, banking on the fact that many people reuse their passwords across multiple services.
• Exploiting security gaps: Any security vulnerability within the bank’s systems can be an entry point for attackers, such as outdated software or poorly secured databases.

Technological solutions to combat account takeover

As account takeover fraud evolves, so too must the technologies designed to combat it. Modern banks are increasingly relying on a suite of advanced tools that leverage machine learning, artificial intelligence, and real-time data analytics to detect and prevent fraudulent activities before they result in significant losses.

How ML and AI can help

Machine learning (ML) and artificial intelligence (AI) are at the forefront of detecting fraudulent patterns and behaviors that human analysts might miss. These technologies can process vast amounts of data in real-time, learning from new inputs to continuously improve their detection capabilities.

Anomaly detection: ML algorithms can quickly identify deviations from normal behavioral patterns, flagging them for further investigation.

Predictive risk modeling: AI can predict the likelihood of fraud by analyzing transaction characteristics and user behavior, adjusting risk scores dynamically based on evolving data.

Secure communication technologies

To protect data transmissions within their networks and with external parties, banks employ secure communication protocols such as:

End-to-end encryption: ensuring that data is encrypted during transmission prevents interception by unauthorized parties.

Secure access layers: technologies like VPNs and secure gateways provide safe communication channels for remote access, a necessity in today's increasingly mobile world.

Enhanced authentication technologies

Apart from traditional security measures, banks are implementing next-generation authentication methods:

Biometric verification: using fingerprints, facial recognition, or retina scans adds a layer of security that is difficult to replicate or forge.

Tokenization: this method replaces sensitive account information with unique identifiers that cannot be reversed without specific decryption keys held only by the bank.

Integration with existing systems

For these technological solutions to be effective, they must be seamlessly integrated into the bank’s existing infrastructure. This requires not only advanced IT architecture but also a commitment to ongoing training and development to ensure that staff can effectively utilize these tools.

Through the adoption of these cutting-edge technologies, banks can significantly enhance their ability to detect and prevent account takeover attempts, thereby protecting their assets and maintaining customer trust.

Other account types susceptible to fraud

Account takeover fraud isn't limited to traditional banking accounts; it spans a wide array of account types across different sectors. Understanding the diversity of these accounts and their specific vulnerabilities can help institutions and individuals better prepare and protect themselves. Here’s a closer look at the types of accounts most commonly targeted by fraudsters:

Store loyalty accounts
Additionally, they can provide another vector to access more sensitive accounts linked to the same user. These accounts often contain preloaded funds or credit points, which can be exploited. 

Social media accounts
While not directly linked to financial theft, compromised social media accounts can be used to spread malware or phishing attempts or to breach the user’s other accounts through shared information.

E-commerce accounts
E-commerce platforms often store payment information, which can be used for unauthorized purchases or to extract financial details.

Gaming accounts
Popular with younger demographics, these accounts may be linked to credit card information or contain valuable in-game purchases and can be sold or exploited for financial gain.

Government benefits accounts
Accounts that receive government deposits, such as unemployment or social security benefits, are increasingly targeted, especially during times when benefits are expanded or modified.

Detecting ATO fraud in banking

Account takeover represents a serious threat to the financial sector, requiring an informed, proactive response from banks and their customers. As fraud tactics become more sophisticated, so too must the strategies to combat them. Banks that adopt comprehensive preventive measures, leverage advanced technologies, and educate their customers can significantly enhance their defenses against these fraudulent activities. Protecting against account takeover is not just a technical challenge but a continuous commitment to maintaining the trust and safety of financial transactions.

The important step for financial organizations is to integrate additional protection methods easily with their existing Account Takeover (ATO) protection strategies. Ideally, the level of protection rises thanks to customized rules, parameters, and authentication processes catered to desired unique security standards. 

Through ML-powered scoring and automated pattern detection, companies can identify and prioritize the most relevant trust and risk signals based on historical data or specific training sets.

More advanced tools are available:

• Phone number intelligence: allowing organizations to tap into detailed analytics for deeper insights into user phone data.
• Email address analytics: this gives companies the ability to utilize comprehensive evaluations of email data to enhance security measures.
• Browser fingerprinting: the possibility of exploring advanced browser biometrics and insights, unlocks a more nuanced security approach.

By incorporating these sophisticated tools, you can fortify your defenses against account takeover, thus automating your protection strategy to focus on more important aspects of your business.

To discover even more fraud solutions to help you counter account takeover fraud and other types of online fraud, don't hesitate to contact our team of experts.

Questions & Answers

What makes social media accounts particularly vulnerable to account takeover fraud?

Social media accounts are particularly susceptible to ATO fraud due to the vast amount of personal information available, which can be exploited for security questions or used in phishing attacks. Additionally, because users often link social media with other services, gaining access can provide a backdoor to more sensitive accounts.

How do real-time data analytics specifically help banks combat ATO fraud?

Real-time data analytics help banks identify and respond to fraudulent activities almost instantly by analyzing transaction patterns and behaviors as they occur. This allows for the detection of anomalies that deviate from a user's typical behavior, enabling quicker interventions and reducing the window of opportunity for fraudsters to inflict financial damage.

Can you explain how ML and AI technologies differentiate between normal customer behavior and potential fraudulent activity?

Machine learning (ML) and artificial intelligence (AI) systems learn from vast datasets of historical transactions to understand typical user behaviors and detect deviations. They use predictive models to assess risk dynamically, factoring in new behaviors and emerging trends in fraud tactics. This ongoing learning process allows these systems to stay ahead of sophisticated fraud schemes by constantly updating their understanding of what constitutes suspicious activity.

Why is biometric verification considered more secure than traditional authentication methods?

Biometric verification methods like fingerprint scans, facial recognition, or iris scans provide a higher security level because they rely on unique biological characteristics that are extremely difficult to replicate or steal. Unlike passwords or PINs, which can be guessed or phished, biometric identifiers are intrinsically linked to the individual, making unauthorized access considerably more challenging for attackers.