The development of new technologies — especially generative AI — gives bad actors more advanced, high-tech tools to carry out complex fraud plans, but those same technologies can also be leveraged to protect the financial sector from fraudsters.
Unfortunately, fraud is a ubiquitous problem. In 2023, 98% of surveyed organizations reported experiencing fraud, and nearly 60% suffered at least half a million dollars in losses. Reputational damage, lost customers, and investigation expenses compound these losses.
Fintechs and financial institutions must enhance their security strategies by keeping tabs on threat trends and implementing robust prevention, detection, and mitigation plans and tools.
Smarter tech means smarter crimes The democratization of hacking tools presents a troubling reality: the barrier to entry for fraud has significantly lowered. Affordable and readily available resources, like online communities, pre-built phishing kits, and malware and account takeover tools, empower even individuals with limited technical knowledge to orchestrate complex scams.
The growing popularity of digital privacy tools like private browsing, VPNs, and anti-tracking settings is a double-edged sword. While these tools can empower users by allowing them to stay anonymous online, these technologies can also make it easier for malicious actors to hide their tracks. Fraudsters can evade detection by masking IP addresses, location data, and other identifying information.
Generative AI presents opportunities for fraudsters to scale their crimes. With GenAI, specifically large language models (LLMs), phishing emails are elevated from plain text with glaring typos and stilted English to realistic, personalized messages that are far more refined and believable. GenAI can churn out vast quantities of these messages in just minutes. Experts from Perception Point blame the technology for the rise in Business Email Compromise attacks, which increased by 1,760% in 2023.
Another GenAI-fueled threat evolution is malicious code creation. These AI models can automate code writing so criminals can produce countless malware variations in a fraction of the time it would take a human. As organizations develop defenses against specific malware threats, cybercriminals can quickly build new variants. Some LLMs are even programmed to stay abreast of the latest programming techniques and security measures so they can generate malware that deliberately evades detection, further challenging security software’s ability to identify and neutralize these threats.
Realistic AI-generated videos or audio recordings, known as deepfakes, also pose a significant risk to the financial industry. Fraudsters could use deepfakes to impersonate executives, customers, support agents, or even vendors in an attempt to steal sensitive data or spread misinformation.
AI-powered bots can scrape data from various sources, enabling them to imitate human communication patterns, replicate voices for phone scams, or even create convincing fake online identities, allowing criminals to bypass traditional security measures built on identifying patterns in human behavior. AI automation can also be used to generate fraudulent refund requests or complaints, potentially leading to financial losses for institutions through chargebacks.
Additionally, while open banking APIs, mobile payments, and Buy Now, Pay Later (BNPL) financing options offer consumers convenience and customization, they also introduce new avenues for fraudsters. Open banking APIs expose consumers to the risk of fraudulent account linking, where unauthorized connections are made between financial accounts and third-party applications. BNPL providers face challenges like credential stuffing and account takeover, while mobile payments introduce the possibility of unauthorized access to digital cards through phishing scams or data breaches.
Though many organizations have responded to these evolving trends and threats by updating their policies and practices, some companies have been slower to upgrade to the security measures necessary to combat these increasingly sophisticated fraud tactics, making them vulnerable targets.
But there’s good news: Fintechs and financial institutions can leverage high-tech tools to combat fraud and stay one step ahead of fraudsters.
Fighting financial fraud with innovative tech tools Fighting fraud requires multiple layers of defense. A combination of the following approaches can help mitigate a wide variety of threats.
- Know Your Customer (KYC) practices. KYC regulations are a critical and mandatory line of defense for financial institutions. Organizations must verify a customer's identity before establishing a relationship and monitor customer behavior for potentially risky activities. Once a cumbersome process, cutting-edge technologies like automated report generation, account monitoring, and suspicious activity flagging help simplify identity verification while taking up fewer resources.
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Multifactor and two-factor authentication (MFA and 2FA). MFA and 2FA go beyond just putting in a password. Users have to provide more than one verification factor to log in. These factors can include something you know (password, PIN), something you have (device or security token), or something you are (fingerprint, facial recognition). This additional layer of protection makes it more difficult for bad actors to access an account, even if they have the password. To uphold security standards, MFA should be required for all accounts at financial institutions.
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Single Sign On (SSO). This technique simplifies logins by letting users access multiple accounts with a single username and password, eliminating the need to remember numerous credentials for different platforms. Additionally, SSO often implements security features like suspicious activity detection and two-factor authentication prompts. SSO is a valuable tool for financial institutions to strengthen internal application security.
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Device intelligence. Companies use device fingerprinting to identify suspicious login activity. This technique assigns a unique ID to a user's device based on factors like IP address and screen resolution. The technology will flag suspicious activity, like a single device logging in from what appears to be multiple locations or repeated login attempts. Additionally, verified users experience a smooth login process, while unrecognized devices require additional verification.
Device intelligence methods expand upon fingerprinting by incorporating additional signals to generate a more precise and persistent identifier. These techniques also detect bot-like behavior to identify and prevent malicious activity from bad actors.
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Transaction monitoring systems. Real-time transaction monitoring systems identify potential fraud as it happens. These systems analyze every transaction and look for red flags like unusually large purchases, frequent changes to account details (password or address), or high volumes of chargebacks. AI allows these systems to analyze vast amounts of data quickly and efficiently, enabling them to identify and prevent fraudulent activity.
A culture of security Building an internal culture centered on security helps organizations withstand threats. These practices include:
- Regularly scheduled security updates and assessments. The combination of consistent security updates and regular audits creates a multi-layered defense against sophisticated fraud. Updates keep systems patched and defenses current, while audits ensure vulnerabilities are identified and addressed before they can be exploited. This continuous cycle of improvement helps financial institutions stay ahead of evolving threats and protect their customers’ sensitive information and financial assets.
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Employee education. Human error causes nearly 90% of security breaches, so ensuring staff is trained on what to look out for should be critical to any organization’s security strategy. Training should cover how to recognize suspicious emails and other types of correspondence, give an overview of trending fraud schemes, offer best practices for strong passwords and safe web browsing, and other relevant security protocols.
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Robust internal processes. Strong internal processes are the cornerstone of effective fraud prevention within organizations, including establishing clear whistleblower protocols for employees who suspect fraud or security concerns, and empowering employees to be the first line of defense while maintaining open lines of communication. Automating routine tasks like invoice processing and payments minimizes the potential for human error or manipulation, a common entry point for fraud. Automated systems can also be programmed to enforce spending limits and adhere to established approval hierarchies, reducing the opportunities for fraudulent activity to go undetected.
Future-proofing finance: Key takeaways
The fight against financial fraud is ongoing, and fintech companies and financial institutions must be proactive, not reactive. Understanding not only current threats but also emerging trends is crucial for building a long-term defense strategy.
A robust fraud prevention program requires a multi-layered approach. Investing in cutting-edge solutions allows institutions to stay one step ahead of sophisticated fraudsters. This includes embracing new technologies like AI and machine learning to identify and prevent fraudulent activity.
But technology alone isn't enough. Cultivating a security-conscious culture is equally important. Encouraging employee vigilance and implementing clear security protocols empower everyone within the organization to be part of the solution. By combining advanced tools with a security-focused culture, fintech companies and financial institutions can significantly minimize the impact of fraud and protect both their customers and their business.
About the Author
Dan Pinto is CEO and co-founder of Fingerprint and brings over a decade of experience in tech. He began his career in software engineering, where he developed an interest in creating bots, but quickly shifted his focus to entrepreneurship. Dan has founded many small startups, including eBay stores, a tech blog, and even a forum for TV shows.
In 2014, Dan co-founded Machinio, a search engine for used machinery, which was later acquired by NASDAQ:LQDT in 2018. After this success, he co-founded Fingerprint, the world’s most accurate device identifier, which has raised over $77 million since its first funding round in 2020. Fingerprint currently employs over 100 people and is dedicated to solving the complex issue of online fraud.
When he's not busy building companies, Dan enjoys spending time with his family — he lives in Chicago with his wife and son.