Digital payments now let customers move money instantly from almost anywhere. While this convenience has simplified global trade, it has also given fraudsters a faster route to their victims. Authorized push payment (APP) fraud has become a serious global threat. As scams become more common, the challenge lies in detecting manipulation—not merely suspicious transactions—because scammers exploit trust, urgency, and increasingly convincing AI-generated identities.
What is authorized push payment (APP) fraud?
Authorized push payment fraud occurs when a fraudster deceives a customer into willingly sending money to the fraudster’s account. The bank sees a genuine customer, valid credentials, and an authorized instruction, making the fraud difficult to distinguish from a legitimate payment.
Common APP fraud scenarios include:
- Romance scams: Fraudsters build emotional relationships before requesting money.
- CEO fraud: Criminals impersonate senior executives and create pressure for an urgent payment.
- Purchase scams: Fake sellers persuade customers to pay for goods or services that never arrive.
- Investment scams: Fraudsters promise unusually attractive returns to encourage large transfers.
- Impersonation scams: Criminals pose as banks, government agencies, attorneys, or other trusted organizations.
Payment scam prevention is particularly difficult because fraudsters exploit human trust and urgency rather than technical vulnerabilities. Generative AI and deepfakes can make impersonation more convincing, while real-time payments give victims less time to reconsider. Social stigma can also deter victims from reporting what happened.
Recovery presents another challenge. Because the customer authorized the transaction, reimbursement may be more complex than it is for an unauthorized payment, although applicable protections vary by market and regulatory framework.
How can banks detect and prevent APP fraud?
Traditional rule-based fraud prevention systems struggle with APP fraud because legitimate and fraudulent payments can look remarkably similar. A large transfer to a new account might signal fraud, but it could also represent the start of a genuine supplier relationship. A rule that treats either characteristic as inherently suspicious will produce false positives, while a rule that waits for multiple rigid conditions may miss sophisticated social engineering.
Banks therefore need payment scam prevention strategies that assess context, behavior, and relationships alongside transaction attributes.
Turn customer communication into a control
Customer communication can become a foundation of payment scam prevention, not just post-fraud education. Banks can use contextual warnings at the point of payment to challenge common manipulation tactics, particularly when a customer sends an unusual payment to a new recipient. Targeted prompts can add a moment of friction where it matters most, without subjecting every transaction to the same level of intervention.
Combine AI signals to separate risk from anomaly
AI and machine learning can assess multiple signals together rather than treating each anomaly as a standalone rule. A new payee, an unusually large amount, an unusual transaction time, and a new or suspicious receiving account may each occur in legitimate payments. Together, however, they can create a materially different risk profile. This approach helps banks distinguish a genuine anomaly from a pattern consistent with APP fraud.
Reconstruct the customer’s digital journey
Transaction data alone rarely explains why a customer is making a payment. Banks can enrich transaction risk assessments with digital interaction, device, session, and behavioral data to reconstruct the events leading up to a payment. For example, a customer who spends an unusually long time on a payment screen after a prolonged call or makes repeated corrections while entering payment details may warrant additional scrutiny. This context can help identify authorized push payment fraud before the transaction is completed.
Intervene while there is still time
Detection only creates value if the bank can act before funds leave the customer’s account. Real-time risk decisioning can trigger proportionate interventions, such as an additional verification step, a targeted warning, or a temporary payment review. Banks should calibrate these interventions to risk rather than applying blanket friction, supporting payment scam prevention without unnecessarily disrupting legitimate payments.
Strengthen investigations and case management
Efficient case management helps fraud teams move from detection to resolution without overwhelming analysts. Intelligent automation and agentic workflows can prioritize high-risk cases, assemble relevant evidence, and support faster investigations while maintaining appropriate human oversight.
Connect payment activity across the network
Fraud rarely exists within a single transaction or customer account. Inbound payment monitoring and graph-based analysis can help banks identify relationships among sending and receiving accounts, mule networks, and previously observed fraud patterns. Behavioral biometrics can add another layer of identity and interaction intelligence, while federated approaches can support pattern detection across institutions without requiring all underlying customer data to move into a single central repository.
Banks need AI and ML capabilities that work alongside robust processes, skilled analysts, and real-time decision-making. Infosys BPM combines technology-led transformation with business process management for financial services, supporting banking operations across payments, fraud investigations, commercial banking, and loan servicing. This integrated approach can help financial institutions strengthen APP fraud controls, improve operational efficiency, enhance payment scam prevention, and protect the customer experience.
Conclusion
The defining challenge of APP fraud is that the payment itself can look legitimate. Banks therefore need to shift from asking whether a transaction is authorized to understanding why the customer is making it, who is receiving the funds, and what happened immediately beforehand. AI, behavioral intelligence, and network analysis can bring these signals together in real time. The goal is to make fraud detection more contextual, adaptive, and precise so that intervention occurs while there is still time to prevent a loss.
Frequently asked questions
The difference lies in who initiates the payment. In unauthorized fraud, a criminal moves money without the customer’s involvement, usually through stolen credentials or a compromised device. In APP fraud, the customer makes the payment after being deceived, so the bank sees valid credentials and an authorized instruction. That distinction changes both how the fraud must be detected and how losses are treated afterward.
It depends on the market. Reimbursement obligations for APP fraud are set by national regulations rather than a single global standard. The UK’s Payment Systems Regulator, for example, introduced a mandatory reimbursement regime that shares the cost between sending and receiving firms, while other jurisdictions may still treat authorized payments as the customer’s responsibility. Banks operating across borders therefore need controls calibrated to the strictest regime to which they are exposed.
A mule account is used to receive and transfer the proceeds of fraud. It may be opened by someone recruited for that purpose or belong to a customer whose account has been taken over. Banks monitor mule accounts because scam patterns are often more visible on the receiving side. For example, a recently opened account that suddenly receives large inbound payments from unrelated senders looks very different from normal activity, even when each individual payment appears legitimate.
Behavioral biometrics analyze how a person interacts with a device—such as typing rhythm, mouse or touch movement, and hesitation on a screen—rather than what the person enters. They help detect APP fraud because the credentials and transaction are genuine, so useful signals may come from the customer’s behavior instead. Hesitation, repeated corrections, or signs that the customer is being coached during the payment can indicate manipulation.
Yes, provided the friction is proportionate rather than universal. Applying the same warnings and verification steps to every payment trains customers to click through them and slows legitimate business. Risk-based intervention concentrates friction on the small proportion of payments that carry unusual combinations of signals, leaving most transactions untouched.
