The fintech industry has seen tremendous growth in the past five years, with new, innovative businesses disrupting the financial services and banking world like never before. But fraudsters are evolving at the same pace, making it critical for fintech businesses to equip themselves with the best tools to protect their company—and their customers.
In this blog, we’ll explore trends in fintech fraud and how to implement a comprehensive solution that accounts for the entire user journey.
Types of Fintech Fraud
No matter the financial service, whether it be banking, crypto, PSPs, or remittances, nearly all financial services are faced with multiple pain points when it comes to fraud. Cybercriminals can take advantage of every step along the user journey, including account creation, login, withdrawal, and the deposit of funds. So, for many fintech businesses, the only way to effectively fight fraud is to take into account the entire user journey and ensure that all of these bases are covered.
Although some types of fraud, such as account takeovers and fraudulent card transactions, may be fairly traditional fraud tactics experienced in a variety of industries, fintech businesses are seeing even more vectors of attack as new technologies and modes of payment are introduced. A few of the many types of fraud fintech businesses may encounter include:
- Stolen/synthetic identity sign-ups (third-party fraud)
- Fraudulent ACH deposits
- Fraudulent withdrawals or transfers
- Account takeover
- Fraudulent card transactions
- Money laundering
- Micro deposit
Why Fintech is So Attractive to Fraudsters
Typically, when a particular industry or business experiences a growth spurt, fraudsters follow. But there are many other reasons that fintech is so attractive to fraudsters. For one, fintech is a growth-oriented industry and strives to gain as many users as possible to maximize profit. The industry is also focused on accessibility and equity—removing barriers to entry historically experienced with traditional banking and providing services to a population that may have been underserved in the past.
The risk of removing these barriers and points of friction, such as offering low or no fees and no minimum balance requirements, is that there are more opportunities for fraudsters to manipulate the system. And fintech being inherently mobile or digital-first provides all the more ways for bad actors to launch attacks across more surfaces.
It’s critical to get some form of controls in place because fraudsters are looking for the least friction. Fintech is super attractive for fraudsters—they are well sophisticated. And knowing that you have some deterrents in place will help ward them off.
Why Payment Service Providers are Especially Vulnerable
Payment service providers (PSPs) must be extra vigilant, as fraudsters can target both merchants and payers. This increased risk has caught regulators’ attention. In 2023, the UK’s Financial Conduct Authority (FCA) sent a letter to PSP CEOs, expressing concerns about inadequate controls. And in the U.S., the Department of Justice (DOJ) and the Federal Trade Commission (FTC) have imposed $200 million in fines on payment firms over the past few years due to insufficient fraud controls.
PSPs face unique challenges compared to other fintech organizations. They are vulnerable to risks from both sides of transactions but often lack direct access to key risk signals to identify fraud. Additionally, since PSPs serve merchants across various sectors, assessing risk in such diverse environments further complicates fraud prevention efforts.
The vulnerabilities unique to PSPs are not only drawing increasing regulatory scrutiny but also having a direct impact on their bottom line as PSPs often absorb the costs of chargebacks resulting from fraud t o stay competitive.
To mitigate these risks, some PSPs have implemented high verification barriers like 3DS, MFA, and 2FA. However, these measures can lead to high block rates or create a high-friction customer experience, placing PSPs in a difficult position as merchants push for a smoother customer journey. This is particularly challenging for PSPs in the U.K., who must also comply with PSD2 requirements.
In essence, PSPs are increasingly responsible for fraud prevention but often lack the real-time insights needed to effectively address it. This lack of visibility makes optimizing 3DS requirements especially difficult for PSPs in the EMEA region.
Rethinking Fraud Prevention: A Holistic Approach
Faced with such a complex fraud landscape, or Fraud Economy, coupled with the unique risk factors within the fintech industry, businesses must have solutions in place that are capable of protecting the entire customer lifecycle. In the process of blocking bad actors, businesses must be intentional about enabling trusted customers as well. The best solutions don’t just block fraud, but enable better consumer experiences. If you have a poor user experience on your platform, it’s easy to lose customers to competitors. When looking for fraud prevention solutions, it’s crucial to find a holistic solution that helps protect and grow your business.
AI and the Importance of Global and Localized Insights
AI is essential for simplifying fraud prevention in fintech, especially as businesses expand their digital experiences to stay competitive, creating new vulnerabilities for increasingly sophisticated global fraud rings.
Sift’s AI-Powered Fraud Solution: A Three-Pronged Approach that Brings Simplicity to an Increasingly Complex Environment.
Sift leverages a multi-layered approach (detailed below) to automatically equip fintech companies with deeper insights into real-time fraud patterns unique to their industry/digital experience while staying ahead of evolving global threats. Sift’s AI-powered approach to risk assessments include:
- 1 Trillion Global Events Annually: Sift gathers real-time data from 1 trillion global events each year to provide businesses with insights into global fraud trends. This helps identify international fraud rings before they infiltrate the fintech ecosystem.
- Fintech-Specific Cohort Models: Fraud patterns in fintech differ from other industries. For instance, interaction velocity in iGaming is higher than in fintech. Sift’s Fintech-Specific Cohort model refines risk behaviors unique to fintech, improving risk accuracy by 23%.
- 16k Risk Signals Across the Entire Digital Journey: Sift uses a custom model to capture, analyze, and extract behavior trends using 16,000 risk signals specific to each fintech’s digital experience. In addition, businesses have the ability to easily incorporate custom signals that are automatically incorporated into the machine learning model to derive insight on unique interaction trends. This detailed analysis distinguishes between legitimate and risky behavior.
This approach is particularly crucial for PSPs and rapidly growing crypto environments where user behavior data may be limited by automatically providing a strong baseline of risk and enabling insight to be uncovered based on their specific use cases.
In doing so, fintech providers can not only prevent fraud across the entire digital customer journey, but also streamline the customer experience through finely-tuned, dynamic friction strategies.
This is especially true for fintech organizations in the EMEA regions where having accurate Transaction Risk Analysis (TRA) and deep behavior insights are key to optimizing 3DS exemptions.
How AI can help accelerate competitive offerings
Incorporating Alternative Payment Methods (APMs) is crucial for managing fintech fraud. As businesses expand their payment offerings to stay competitive—embracing digital wallets, peer-to-peer payments, and other emerging methods—they also introduce new layers of risk that must be effectively managed.
Fraud behavior not only varies between industries but also across different payment methods. Identifying and understanding the nuances within each payment type can be extremely challenging. This is where AI is once again playing a transformative role.
Sift, for example, offers a payment-agnostic solution, allowing businesses to integrate new payment types without overhauling their fraud operations. The activity of each payment type is automatically analyzed using a machine learning model, which extracts payment-specific risk trends. These insights are then used to enhance risk assessments and inform automation strategies.
By leveraging AI, businesses can stay ahead of fraudsters, ensuring that the addition of new payment methods does not compromise security. This approach not only simplifies fraud management but also provides the flexibility needed to adapt to an evolving payment landscape.
Check out this webinar to discover how to navigate the next generation of payment fraud.
