How advanced authentication serves as a linchpin to fight fraud

Issuers face accelerating fraud on many fronts, with criminals using AI to take over accounts and open fake ones. In fact, fraudsters’ techniques are advancing so quickly that it’s a challenge for risk management departments to keep up.

What’s the right approach for banks to take to protect their customers?

To get ahead of fraudsters, there’s a case to make for investing in advanced authentication technology, which uses AI and biometrics. The right authentication solution offers issuers the ability to stop fraud before it starts, minimizing friction at the point of contact and ultimately driving customer satisfaction.

Let’s first examine where the industry stands.

For financial institutions (FIs), AI serves as a friend and a foe. It has enormous potential to help stamp out fraud by using machine learning and biometric data. Yet AI is also being used by criminals who use deepfakes for document submission and voice calls. Fraudsters are applying for credit and opening new accounts under synthetic identities by cobbling together stolen personally identifiable information (PII). They’re also using stolen PII for account takeovers and bombarding multiple channels with social engineering and bots to infiltrate call centers, mobile devices and websites.

The perpetrators, which tend to be well-organized fraud crime syndicates, will cost FIs $58 billion worldwide by 2030, up from $23 billion in 2025, according to Juniper Research.

FIs are taking the threat seriously, with an estimated $21.1 billion spent globally on fraud detection in 2025. That’s expected to rise to $39.1 billion annually by 2030.

A big part of that is identity theft. Account takeovers represented 53% of reports of identity misuse, with checking accounts being the most common at 22%, according to the Identity Theft Resource Center. New account creation made up 36% of identity misuse reports, with credit card accounts leading the way at 30%.

How can FIs build a unified fraud defense across all channels?

One big problem for FIs is the lack of a unified approach to combat intruders or imposters due to their legacy silos. For years, they have relied on many different vendors in the market to build a patchwork of defenses across their various channels.

There are, however, a few ways to confront this challenge. One approach to lowering risk is to integrate all their departments: call center, mobile device, website and branch. The result functions as sort of a neighborhood watch system, revealing what’s happening across all the channels once fraudulent activity begins. However, these integrations are extraordinarily expensive and can take years to implement.

Instead, advanced authentication technology offers a more efficient way to combat fraud. Advanced authentication focuses on each point of contact between the institution and the end-user. It uses intelligent authentication methods such as biometrics (facial and fingerprint recognition) to confirm a user’s identity. This offers enhanced security for digital transactional systems. It can also be done at a fraction of the cost of integration projects. Plus, new authentication methods can be layered continuously onto an issuer’s existing framework.

How can risk-based authentication reduce friction without compromising security?

One big advantage of using advanced authentication is how it allows issuers to adapt requirements to the specific content of each user interaction. For each situation, FIs can intensify authentication for high-risk situations using risk-based triggers for amounts, a new device or unusual geography, then decrease authentication for lower risk transactions or interactions. This helps reduce friction for cardholders.

Other key technological aspects of advanced authentication that offer security advantages include:

• Behavioral biometrics: AI analyzes behavioral patterns, such as a user’s typing rhythm, mouse movements or how they hold their phone or tap on their computer. This provides a continuous, passive layer of authentication that runs in the background.
• Machine learning: This improves accuracy by extracting user data from biometric data. It analyzes patterns and continuously adjusts to understand a user’s interactions. AI effectively combines and weighs data from multiple biometric inputs, such as a fingerprint or face, to create a more secure authentication process that is harder to compromise.
• Spoof detection: AI and machine learning algorithms are trained to differentiate between a live user and a spoofing attempt, such as a photograph, mask or recorded voice.
• Improved speed: AI-powered systems can process vast amounts of biometric data in milliseconds, enabling instantaneous identity verification for high-volume environments such as payments, inbound calls to customer service and account openings.

What are the key use cases for advanced authentication in financial services?

Advanced authentication gives financial institutions the tools to validate identities, detect risk signals and build connected fraud protection across three critical touchpoints:

• Originations: When customers apply for a new account, advanced authentication can analyze scanned documents to gauge how much an applicant’s information matches against databases with their PII. Authentication tools are also able to detect false documents. A system can intensify authentication when an anomaly is detected at the point of application. A processor’s authentication platform should also bring all of an issuer’s channels under a shared fraud ecosystem so device, IP address and behavioral profiling can be used across all endpoints.
• Call centers: Advanced authentication can detect voice patterns from recorded call center conversations. This helps verify callers and reduce the burden of collecting data for knowledge-based authentication. FIs could also use voice mismatch detection software to flag suspicious callers, such as technology that detects nuances in audio that indicate whether a voice is real or recorded.
• Loyalty fraud: In another scheme, fraudsters attempt to steal points from the vast trove of loyalty accounts nationwide. Advanced authentication techniques can use biometrics and behavioral data to provide multiple layers of protection throughout the customer journey, including rewards redemption at checkout. Authentication technology can monitor user behavior and device usage to determine known patterns and detect risk signals.

How does advanced authentication deliver measurable ROI for issuers?

Issuer processors, some of which have a global cross-section view of transaction activity, can be instrumental in providing these advanced authentication tools.

The right processor can use its fraud scoring engines and data from transaction flows to share context of an interaction. From behind the scenes, it can use biometric and AI-driven methods to sort out the authenticity of someone at the point of contact. Issuers can also utilize biometrics and AI to create transparent, adaptive and customer-centric strategies.

As a result, issuers can more clearly understand the customer journey, optimize performance and anticipate emerging fraud.

One example is FIS® Total Issuing™ Advanced Authentication, a machine learning and AI-driven technology. This solution supports FIs in implementing a risk-based approach that integrates seamlessly with channel interfaces, automating processes that would otherwise create friction with manual intervention. By using biometrics to help confirm genuine account holders, it offers multiple ways to protect users against fraud while enabling processors to develop and monitor a 360-degree view of the cardholder’s activity and identity.

“The key is understanding what is occurring with fraud and AI, then homing in on optimizing the experience for genuine cardholders,” said Kasey.