Speed has become a baseline in digital banking. Customers now expect to open accounts in minutes, move money in real time and authenticate without passwords. Every improvement makes digital banking more convenient. It also creates a larger surface for fraudsters to exploit.

Fraud has evolved alongside that experience. Identity fraud now operates as an industrialised ecosystem, with stolen credentials, synthetic identities and AI-generated deepfakes available as scalable tools. In 2025 alone, infostealer malware exfiltrated more than 1.8 billion credentials, an 800% increase over previous years.¹ Synthetic identity fraud is projected to generate more than US$3.1 billion in unsecured credit losses in 2026, growing at roughly 16% annually.¹

The economics of biometric fraud are changing too. Deepfake-as-a-Service tools can cost as little as US$10, giving attackers an inexpensive way to target biometric verification.¹ At a broader level, global cybercrime losses are expected to reach US$10.5-10.8 trillion in 2026.²

For financial institutions, this changes the equation behind every digital approval. The customer expects speed, while the institution needs confidence that the person, device and transaction are genuine. As the cost of impersonation falls, establishing that confidence becomes harder. Trust is becoming a bottleneck for digital growth.

As digital fraud becomes more sophisticated, the instinctive response is to add another layer of verification.

●        A password gives way to an OTP

●        An OTP is followed by document verification

●        A document check is reinforced with facial biometrics

Each step is designed to close another gap. Yet fraudsters are adapting to each signal in turn.

●        Credentials can be stolen at scale

●        Documents can be manufactured using AI

●        Biometric systems can be targeted with deepfakes

The result is a growing number of checks without a corresponding guarantee of trust. Device, network, behaviour, document and face each reveal something different about the legitimacy of an interaction.

The strength comes from the combination. Compromising one signal does not automatically give an attacker control over the others. For financial institutions, this creates a broader basis for making risk decisions before an account is opened, a login is approved or a payment is completed. Confidence comes when multiple independent signals point to the same conclusion. 

Each trust signal plays a different role, yet each is becoming a target for AI-enabled fraud.

The scale of the threat is already visible. Thousands of deepfake-enabled attempts to bypass liveness checks were recorded against a single financial institution, with Deepfake-as-a-Service making these attacks increasingly accessible.¹ More than 70% of cloud breaches are expected to originate from compromised identities rather than software vulnerabilities.²

For financial institutions, resilience comes from combining signals that attackers cannot compromise through the same route. Mobile network intelligence adds a distinct source of trust because its signals originate from carrier infrastructure rather than information supplied by the user.

The mobile network is emerging as another source of trust for digital authentication, giving financial institutions network-derived signals that sit outside customer-entered credentials and can complement existing verification methods.

The infrastructure to support this shift is already taking shape. CAMARA, an open-source initiative supported by the GSMA and Linux Foundation, standardises APIs that allow enterprises to access capabilities from mobile networks. Meanwhile, 86 operator groups, representing more than 300 mobile networks and 80% of global mobile connections, are aligned with the GSMA Open Gateway framework.³

One example is Number Verification, which allows a service to verify whether a handset is associated with a claimed mobile number. The model can deliver a more seamless authentication experience too. Meta reported 80-95% conversion rates with Number Verification APIs, compared with 60-75% for SMS OTP in its strongest markets.³

The role of network intelligence can extend further. Financial institutions increasingly see value in combining biometrics with device and SIM verification, creating two independent roots of trust that are harder to compromise together.³ Singapore's regulatory direction reflects the same focus on stronger authentication, with the Monetary Authority of Singapore working with banks on FIDO-compliant hardware tokens for higher-value internet banking payments and transfers.⁴

For financial institutions, network intelligence adds another layer of evidence to the risk decision, working alongside documents, biometrics, behavioural signals and device intelligence.

As fraud becomes faster and more sophisticated, verification timing matters as much as the method. Financial institutions need early signals to identify suspicious transactions while keeping legitimate customers moving.

Network intelligence provides that signal silently, using real-time mobile network data without requiring another code or verification step. It adds context alongside device, behavioural, document and biometric signals.

SingVerify brings this approach together as a suite of five Telco APIs. Each API addresses a distinct fraud risk, allowing organisations to apply the signals most relevant to a specific journey or combine them for broader, layered protection.

Together, these APIs bring network-derived signals into critical moments such as logins, onboarding and payments. The result is a more layered approach to identity and fraud decisioning, with trust built from signals that operate in the background before fraud has the opportunity to act.
 

The future of identity verification isn't another authentication step. It's another source of trust. Discover how SingVerify helps financial institutions strengthen authentication with trusted mobile network intelligence.