Banking fraud is becoming more sophisticated as criminals exploit digital banking channels, manipulate legitimate customers and use increasingly convincing methods to bypass traditional security controls. From account takeovers and authorised payment scams to synthetic identities and deepfakes, financial institutions face a growing range of threats that can be difficult to identify through conventional monitoring.
In an analysis of nine major types of banking fraud, ZIGRAM, a RegTech and financial crime technology provider, warns that banks need to look beyond individual transactions and isolated security checks. As fraudulent activity increasingly spans accounts, payment channels and customer interactions, connecting risk signals across the banking ecosystem is becoming essential to identifying suspicious behaviour.
The company defines banking fraud as deceptive or unauthorised activity involving financial institutions, customers, accounts, payments or services, carried out for financial gain. Perpetrators can include external criminals, organised networks, insiders and customers themselves.
Research from TransUnion also points to growing fraud risks within existing customer relationships, highlighting why institutions need to monitor activity beyond initial identity verification and account opening.
ZIGRAM identifies nine major categories of banking fraud, each exploiting different weaknesses across financial services. Account takeover occurs when criminals gain control of legitimate customer accounts through methods such as phishing, malware or SIM-related attacks. Once inside, they can access funds, change account details or initiate transactions.
Payment fraud covers unauthorised activity involving cards, bank transfers, Automated Clearing House (ACH) payments and instant payment systems. The Federal Reserve’s 2026 Risk Officer Report found that fraud challenges are increasing across these payment channels.
Authorised push payment (APP) fraud presents a different challenge. Rather than accessing an account without permission, criminals manipulate customers into approving transfers themselves. Scammers may impersonate bank employees, suppliers or senior executives, making conventional authentication checks less effective.
Mule accounts are another important part of the fraud ecosystem. Criminals use these accounts to receive and move stolen funds, sometimes recruiting individuals who knowingly participate and, in other cases, exploiting people who are unaware of their involvement.
First-party fraud involves customers deliberately abusing financial products or services for personal gain. This can include dishonest credit applications or fraudulent payment disputes. Because the activity may initially appear legitimate, institutions often need to identify behavioural changes over time to detect it.
Synthetic identity fraud involves combining genuine and fabricated personal information to create identities that can pass initial verification checks. Criminals may use these profiles to build financial histories before exploiting credit products or other banking services.
AI-generated voices and videos are also making impersonation more convincing. Deepfake technology can help criminals imitate customers, executives or other trusted individuals, increasing the difficulty of identifying fraudulent interactions.
Application fraud involves submitting false or manipulated information to obtain financial products, while insider fraud occurs when employees or other trusted individuals misuse their access to systems, information or funds.
The challenge for financial institutions is that modern fraud rarely depends on one obvious warning sign. A customer may log in using valid credentials, approve a payment themselves and complete a transaction that falls below conventional alert thresholds. However, when combined with other activity, these events may reveal a wider fraudulent pattern.
ZIGRAM argues that fragmented data makes it harder for banks to identify these connections. When identity information, device intelligence, payment activity and customer behaviour are held in separate systems, institutions can miss relationships that would otherwise expose suspicious activity.
The speed of digital banking compounds the problem. Mobile banking fraud losses increased by 33% in 2023, while instant payment infrastructure leaves financial institutions with less time to investigate and intervene before funds are transferred.
This creates pressure to move beyond isolated transaction checks towards more connected approaches to financial crime detection.
ZIGRAM recommends combining prevention and detection through several capabilities, including behavioural and transaction monitoring, beneficiary analysis, device intelligence, entity relationship mapping and real-time risk assessment.
These approaches allow banks to assess activity across different systems and identify links between customers, accounts, devices and transactions. The company’s Fraud Fighter platform provides real-time monitoring, risk scoring and behavioural analytics to help institutions identify suspicious activity. Its Complete FRAML System combines fraud monitoring with broader financial crime and compliance oversight.
For financial institutions, the objective is to build a more complete picture of customer and transaction risk rather than treating each event as an isolated incident. As criminals increasingly combine different fraud techniques, banks face the challenge of connecting signals that may appear harmless individually but reveal significant risk when viewed together.
Identifying individual fraud types remains important, but the ability to recognise how they intersect could prove central to strengthening financial crime controls.
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Copyright © 2026 RegTech Analyst
