Launching a banking product through Banking-as-a-Service can look deceptively simple from the outside. A FinTech company can build an attractive mobile application, connect to a BaaS provider, integrate APIs, create an onboarding journey, and prepare its product for customers without owning a traditional banking charter. But the technology is only one part of the launch. The more difficult question is whether the product can operate within the regulatory, compliance, risk-management, and operational framework required by the sponsor bank and the applicable regulators. This is where BaaS compliance becomes a launch-critical issue rather than a back-office function. In a typical BaaS arrangement, a FinTech may own the customer experience and much of the product operation, while a regulated bank provides banking services and maintains the underlying regulated relationship. That does not mean the FinTech can treat compliance as something the bank will handle after the product goes live. U.S. banking regulators have repeatedly emphasized that a bank’s use of third parties does not remove the bank’s responsibility to comply with applicable laws and regulations. The Federal Reserve, FDIC, and OCC’s interagency guidance specifically addresses third-party relationships involving FinTech companies and emphasizes due diligence, risk management, monitoring, and governance. For FinTechs, the practical consequence is important: the sponsor bank will expect evidence that the FinTech can operate its part of the program safely and compliantly before customers are onboarded. That means having appropriate customer identification procedures, Know Your Customer controls, business verification where relevant, customer due diligence, transaction monitoring, sanctions screening, suspicious-activity escalation processes, consumer-protection controls, complaint handling, data security, record keeping, training, independent testing, and clear accountability. The exact obligations vary according to the product, jurisdiction, customer type, and contractual structure. A FinTech offering a prepaid card program will not face exactly the same compliance requirements as a company offering business accounts, lending, international payments, or investment services. However, the central lesson is consistent: compliance must be designed around the actual financial product before launch, not added after the technology is finished. What BaaS Compliance Actually Means BaaS compliance refers to the policies, controls, processes, governance, and monitoring mechanisms required for a FinTech to deliver banking or financial services through a Banking-as-a-Service arrangement while meeting applicable regulatory and contractual obligations. The phrase can be confusing because responsibility is divided between multiple organizations. A simplified BaaS structure looks like this: Customer → FinTech Platform → BaaS / Program Infrastructure → Sponsor Bank → Banking Rails The customer may primarily interact with the FinTech’s application. The sponsor bank may hold the relevant charter and provide regulated banking services. A BaaS provider or technology company may provide APIs, account infrastructure, payment processing, card infrastructure, or other technology. That creates multiple layers of responsibility. Area FinTech BaaS Provider Sponsor Bank Customer experience Usually primary Supporting Oversight Product design Primary Supporting Approval/oversight KYC/KYB Often operationally involved May provide tools Regulatory oversight BSA/AML Program obligations Technology/support Bank responsibility Transaction monitoring Often operationally involved May provide technology Oversight Sanctions screening Often operationally involved May provide infrastructure Oversight Consumer complaints Usually customer-facing Support Oversight Data security Major responsibility Major responsibility Vendor oversight Regulatory reporting Depends on structure Support Bank responsibility Third-party risk Must manage vendors Must manage vendors Must oversee relationship The exact allocation depends on the program agreement and regulatory structure. The key point is that outsourcing a compliance activity does not automatically outsource accountability. Why Compliance Can Stop a BaaS Launch One of the biggest mistakes a FinTech can make is treating compliance as something to complete after the product is technically ready. In BaaS, the order often needs to be reversed. The sponsor bank needs to understand the FinTech’s product, customer base, markets, transaction flows, risk profile, compliance program, technology environment, vendors, and operational capabilities before it is comfortable allowing the program to go live. The bank’s concern is straightforward. If its banking infrastructure is being used to provide services to customers acquired and managed by a FinTech, weaknesses in the FinTech’s controls can become risks for the bank. The Federal Reserve’s interagency third-party guidance states that a bank’s use of a third party does not diminish its responsibility to comply with applicable laws and regulations. The guidance also emphasizes due diligence before entering a third-party relationship and ongoing monitoring throughout the relationship. This is why sponsor-bank diligence can become a major launch gate. Pre-Launch Question Why It Matters Who are the customers? Determines customer and financial-crime risk What products are being offered? Determines regulatory and operational requirements Where are customers located? Affects jurisdiction and compliance obligations How are customers verified? Establishes identity and eligibility controls How are transactions monitored? Detects suspicious or unusual activity Who investigates alerts? Establishes accountability Who handles complaints? Supports consumer-protection requirements Who controls customer data? Creates privacy and security responsibilities Which vendors are involved? Creates third-party risk What happens when something fails? Tests operational resilience A FinTech that cannot answer these questions clearly may struggle to pass sponsor-bank diligence. 1. Sponsor Bank Due Diligence Is a Major BaaS Compliance Challenge Before a FinTech can launch a banking product, it usually needs a relationship with a sponsor bank or another regulated financial institution capable of providing the relevant services. The sponsor bank is unlikely to evaluate the FinTech solely on its product idea or technical capabilities. It needs to understand whether the company can operate within the bank’s risk appetite and compliance framework. The due-diligence process can cover corporate information, ownership, business model, target customers, products, jurisdictions, financial projections, compliance policies, technology, cybersecurity, vendors, complaint procedures, transaction monitoring, and management capabilities. Regulatory guidance emphasizes that third-party due diligence should be tailored to the specific relationship and should assess the third party’s ability to perform the activity safely, comply with applicable laws, and meet the bank’s requirements. This means a FinTech should prepare for sponsor-bank diligence as if it were part of the product launch itself. What the Sponsor Bank Will Want to Understand Diligence Area Typical Question Business model What exactly does the FinTech provide? Customer base Who will use the

Banking fraud has always changed as financial technology changed. When banks went from paper records to credit cards to online banking, mobile apps, instant payments and digital wallets criminals found new ways to take advantage of each change. Today, the problem is becoming even more complicated because artificial intelligence is changing both sides of the fraud equation. Banks can use AI to identify suspicious activity faster, understand unusual transaction patterns, and strengthen fraud prevention, but criminals can also use AI to create more convincing phishing messages, deepfake identities, synthetic voices, and highly personalized scams. This creates a new reality for financial institutions. Traditional fraud prevention systems that depend heavily on fixed rules and historical patterns may struggle when fraudulent behavior changes quickly. A transaction can look normal on its own while becoming suspicious when combined with account behavior, device information, payment history, location, merchant activity, and other signals. This is where AI fraud prevention in banking becomes increasingly important. Financial authorities are paying close attention to this shift. In 2026, the Bank for International Settlements highlighted that AI can strengthen financial defenses while simultaneously increasing the speed, scale, and complexity of cyberattacks. The BIS has also pointed to deepfakes, synthetic identities, and AI-enabled deception as emerging risks for financial stability. The challenge for banks is therefore not simply to “use AI.” The real challenge is to use artificial intelligence responsibly to understand suspicious behavior earlier, reduce false alarms, protect legitimate customers, and respond to new forms of financial crime without creating an opaque system that nobody can properly explain. The future of banking security will increasingly depend on that balance. What Is AI Fraud Prevention in Banking? AI fraud prevention in banking refers to the use of artificial intelligence, machine intelligence, behavioral analytics, automation, and related technologies to identify, prevent, detect, and respond to potentially fraudulent financial activity . Traditional fraud systems typically operate through predetermined guidelines. A financial institution can create a rule that flags a transaction above a positive amount, a price out of unusual territory, or multiple transactions that take place within a quick time frame are still useful because rules can be true and predictable, but they can conflict when scammers change their pace. AI introduces a more adaptive approach. Instead of looking at the single easiest transaction, an AI-powered fraud detection engine can analyze the relationships between multiple signals. It can analyze based on behavior patterns and detect behaviors that seem out of sync with the customer’s daily interests. This does not mean that the AI model will robotically know if a transaction is fraudulent. Rather, it may provide an opportunity or threat assessment to determine whether additional verification or investigation is important. This distinction is important because banking decisions can have serious consequences. A legitimate transaction incorrectly blocked by a fraud system can create frustration for customers, while a fraudulent transaction that goes undetected can cause financial loss and damage trust. The strongest approach is therefore not simply automation. It is a combination of AI detection, risk-based controls, human oversight, and continuous monitoring. Why Banking Fraud Is Becoming Harder to Detect The biggest change in modern financial fraud is not simply that criminals have more tools. It is that fraud can now be personalized and scaled much more easily. A traditional phishing email is likely to contain obvious spelling mistakes or a message that looks suspicious. AI could make fraudulent conversations much extra convincing. A scammer may possibly give messages that suit the chosen victim’s language, tone, and context. Voice cloning can make a mobile name appear to come from a dependent man or woman, while a deepfake video can create a convincing impersonation. The Bank for International Settlements has warned that AI-enabled scams can make phishing more personalized and persuasive at scale, including through deepfake impersonation and customized scam messages. This matters for banks because many traditional fraud controls assume that identity signals are relatively difficult to imitate. If a criminal can create convincing synthetic identities or manipulate voice and visual information, financial institutions need more than a single authentication signal. A modern fraud prevention system therefore needs to understand behavior, not just identity. Traditional Fraud Detection vs AI Fraud Detection Area Traditional Fraud Detection AI Fraud Detection Primary approach Rules and thresholds Machine learning and behavioral analysis Data analysis Often transaction-focused Multi-signal analysis Adaptability Requires rule updates Can identify changing patterns Fraud patterns Known scenarios Known and emerging patterns Real-time analysis Possible Highly suited to continuous scoring False positives Can be significant Can be reduced with better modeling Customer behavior Limited context Behavioral profiling Network relationships Limited Graph and relationship analysis Deepfake detection Limited Can incorporate multiple signals Investigation Manual-heavy AI-assisted Monitoring Rule-based Continuous and adaptive The difference does not mean banks should abandon rules. Rules are still valuable for clearly defined scenarios and regulatory controls. Instead, AI can sit alongside traditional controls and provide a more dynamic understanding of risk. How AI Detects Fraudulent Transactions At the center of modern AI fraud detection in banking is the ability to analyze large amounts of information quickly. Imagine that a customer normally uses a banking application from one device, makes payments within a particular geographic region, and typically transfers relatively small amounts. Suddenly, a transaction is initiated from a new device, the beneficiary has never been used before, the transaction amount is significantly larger than usual, and several account details have changed within a short period. None of these signals necessarily proves fraud. But together, they may indicate unusual behavior. An AI model can combine these signals and generate a risk assessment. The bank can then decide what action is appropriate. A low-risk transaction might continue normally. A moderately risky transaction might require additional authentication. A high-risk transaction could be paused for review. This approach is often more effective than relying on a single rule because fraud is rarely defined by one isolated event. It is usually the combination of behaviors that creates the warning sign. Behavioral Analytics Is Becoming Central to

Open banking has changed the way people and businesses interact with services. Customers can now link accounts start payments share details and use new financial products without always going through traditional banking channels. For banks, fintech companies and payment providers this opens the door to faster connected and more convenient services. But there is another side to this transformation. As payments become faster and more automated, fraud prevention becomes more difficult. A traditional payment system may have given financial institutions more time to review unusual transactions. Modern open banking payments can move quickly through API-based journeys, while customers increasingly expect transactions to be completed almost instantly. That creates a difficult challenge: How can banks stop fraudulent payments without slowing down legitimate ones? This question sits at the heart of open banking fraud prevention. Recent data from Open Banking Limited shows that open banking fraud remains lower by transaction volume than fraud across the wider UK payments industry. However, fraud volumes increased during the first quarter of 2026, and Authorised Push Payment (APP) fraud accounted for more than two-thirds of reported open banking-related fraud cases. Open Banking Limited also reports that fraud techniques are becoming more sophisticated, including impersonation, phishing, smishing and fake-refund scams. The answer is not just adding login screens or blocking more transactions. Banks and fintechs need risk controls. These controls must understand the picture. Transaction context, customer behaviour, payment patterns and real-time fraud signals. This article looks at why open banking fraud’s changing why faster payments make fraud harder to stop and how financial institutions can build a smarter way to protect payments. What Is Open Banking Fraud? Open banking fraud refers to fraudulent activity that occurs through open banking-enabled financial services, particularly account information and payment initiation services. Open banking allows authorised third-party providers to connect with financial institutions through secure APIs. Depending on the service, customers can give permission for a third party to access account information or initiate payments from their bank account. The model creates significant benefits. For example, a customer might: However, every additional connection can introduce another point where fraudsters may attempt to manipulate a customer, compromise credentials or exploit weaknesses in the payment journey. Importantly, open banking fraud is not limited to someone breaking into a bank account. A fraudster may instead manipulate the customer into making a legitimate-looking payment. This is particularly important in Authorised Push Payment fraud, where the customer authorises the payment themselves after being deceived. Open Banking Limited’s latest fraud monitor found that APP fraud remains the dominant fraud category in open banking payments, accounting for more than two-thirds of reported cases. That changes the fraud-prevention problem. The system is no longer asking only: “Is this customer authorised to make this payment?” It also needs to ask: “Does this payment make sense given the customer’s behaviour, transaction context and relationship with the recipient?” Why Faster Payments Are Changing the Fraud Landscape Speed is one of the biggest advantages of modern payments. Consumers want instant confirmation. Businesses want faster settlement. Merchants want fewer abandoned transactions. Fintech platforms want seamless payment experiences. But speed also reduces the time available for intervention. Consider a simplified example. A customer receives a convincing message claiming that their investment account requires an urgent payment. They follow a link, authenticate with their bank and send €10,000. From a conventional authentication perspective, the transaction may look legitimate. This is one of the biggest challenges in modern payment security. Fraudsters increasingly attack people and processes, not just technical infrastructure Faster payments create several challenges: Challenge Why It Matters Instant execution Less time to intervene before funds move Social engineering Customers may authorise fraudulent transactions themselves API connectivity More participants can interact with payment journeys Automated fraud Attackers can scale campaigns quickly Cross-platform activity Fraud signals may exist across multiple providers Account takeover Compromised accounts can be used for rapid transfers Mule accounts Fraudulent funds can move through legitimate-looking accounts The result is a shift from traditional transaction monitoring toward real-time payment risk management. Why Open Banking Fraud Is Different Open banking introduces a more connected financial ecosystem. A payment journey can involve several parties, including: Each participant may have information that another participant does not. For example, a payment initiation provider may understand the merchant relationship, while the bank may have detailed knowledge of the customer’s historical behaviour. If those signals remain isolated, fraud detection becomes weaker. This is why data sharing and transaction context are becoming increasingly important. Open Banking Limited has highlighted the importance of transaction-level information such as Transaction Risk Indicators (TRIs) and enhanced fraud data to strengthen open banking fraud prevention. The goal is not simply to collect more data. The goal is to collect the right data at the right moment. The Growing Threat of Authorised Push Payment Fraud Authorised Push Payment fraud deserves particular attention because it challenges traditional fraud models. In an unauthorised transaction, a criminal may access an account and initiate a payment without the customer’s permission. In APP fraud, the customer is manipulated into authorising the payment. The fraud can therefore pass several traditional security checks. Common APP fraud scenarios include: Open Banking Limited reported that investment fraud remains one of the largest identified APP categories by value in its June 2026 fraud monitor. This demonstrates why authentication alone cannot solve modern payment fraud. A system can successfully confirm that the person making the payment is the account holder while still failing to determine whether the reason for the payment is fraudulent. That is where smarter risk controls become valuable. Common Types of Open Banking Fraud Understanding the different forms of fraud is essential for designing effective controls. 1. Authorised Push Payment Fraud The customer is manipulated into approving a fraudulent transaction. The payment may appear completely legitimate from a technical perspective. 2. Account Takeover A fraudster gains control of a customer’s account and uses it to initiate transactions. Attack methods can include: 3. Phishing and Smishing Fraudsters use email or SMS messages to convince