as digital scammers rewrite rules, here’s how institutions can flip the script

A phone tap is all it takes to split a dinner bill, transfer funds across continents, or build a business from a kitchen table. This frictionless ease has made digital payments a cornerstone of modern daily life. Yet, the very platforms that make financial transactions smooth can also drain a lifetime of savings within seconds.

TransUnion’s Top Fraud Trends Report found that 26% of consumers across the globe lost money to digital fraud in 2025. Merchant losses from online payment fraud alone are projected to exceed $362 billion globally between 2023 and 2028, with $91 billion of that in 2028 alone, according to Juniper Research. Behind these numbers sits a pervasive problem: The channels consumers use to bank, pay and borrow are the same that criminals use to reach them.

Digital finance fraud has many faces. Phishing and smishing trick people into handing over credentials. Account takeover uses stolen logins to hijack a genuine account. Synthetic identity fraud stitches together real and fabricated data to create an entirely new, convincing identity. Authorised push payment fraud persuades a victim to transfer funds willingly, often through impersonation or urgency. Each form exploits a different point in the consumer’s journey.

Artificial intelligence (AI) has sharpened these attacks considerably. Advanced deception and AI-enabled identity fraud techniques grew by 180% year on year, according to CoinLaw’s 2026 digital payment fraud analysis. This is because generative tools make convincing fake identities, cloned voices and manipulated documents easier to produce at scale. 

Protecting consumers now calls for defences that learn and adapt as quickly as the threats do. Financial institutions must implement comprehensive trust and safety frameworks and invest in AI-powered fraud-prevention solutions to block scammers at the gate, while preserving consumer confidence.

Six ways to protect consumers

1. Real-time, network-wide monitoring: Machine learning (ML) models analyse transactions in real time using many behavioural and contextual signals instead of relying on fixed rules. This flags and halts suspicious activity before money is transferred. Sharing threat intelligence between financial institutions, payment networks and telecom operators helps prevent fraud from spreading across the wider network.
2. Behavioural biometrics: How a person types, swipes and handles their device creates a behavioural fingerprint unique to each user. If a session deviates from it, extra verification gets triggered, even when login credentials are entered correctly.
3. Multi-layered identity verification: Bad actors increasingly use generative AI to create convincing fake identities from a mix of real, stolen and fabricated credentials. Facial liveness checks and document verification catch these fake identities and stop fraud before an account is even opened. This reduces the number of mule accounts used to move and hide stolen money.
4. Adaptive, self-learning models: Unlike fixed rule-based systems, AI-driven fraud engines learn continuously from new fraud cases as they happen. This helps keep pace with changing scam tactics while reducing false alarms that inconvenience genuine customers and create extra work for investigation teams.
5. Instant consumer-led recourse: Built-in safety features in banking and digital wallet apps can help stop fraud immediately. For example, a transaction “kill switch” can instantly freeze outgoing transfers if suspicious activity is detected. Combined with real-time alerts and easy self-service tools to report fraud, these features give customers control right away instead of making them wait for customer support while stolen funds clear.
6. Disrupting social engineering: Static warnings shown periodically are easy to ignore because they often appear when there is no immediate risk. Instead, warnings displayed during high-risk transactions — such as reminders that a genuine bank representative will never ask a customer to transfer money to a “safe account” — can stop scams in real time. At the same time, advertising and search platforms can use automated content moderation to detect suspicious links and unrealistic claims to prevent fraudulent content from reaching consumers in the first place.

In short, the strongest consumer protection comes from stacking the above layers together, rather than relying on any single control.

Trust is the new currency

Consumers will not tolerate friction, nor will they forgive a provider that fails to protect them. Institutions that treat fraud prevention as an ongoing, data-led discipline rather than a periodic upgrade are the ones earning consumer confidence in digital finance. That means adopting a multi-layered defence, continuously updating detection models, sharing threat intelligence across the ecosystem and giving customers simple tools to act quickly when something goes wrong. Institutions that get this right will be the ones customers trust with their money.

How Infosys BPM can help

Securing digital financial systems against relentless, evolving threats requires dedicated expertise and scalable operational support. Infosys BPM offers robustfraud management solutions designed to help global financial institutions proactively manage risk and secure consumer accounts. By combining AI-driven detection with deep financial services experience, it helps institutions cut losses, reduce false positives and strengthen the trust consumers place in their digital channels.