Authentication failures are fraud prevention failures

How siloed authentication and fraud architecture creates the conditions fraudsters need to succeed

Ask your fraud team who owns an account takeover (ATO) failure, and they'll probably point to authentication. Ask the authentication owner the same question, and they'll point to fraud prevention. Both answers are understandable, and neither team is wrong, which is exactly why this problem persists.

In a webinar with American Banker in April 2026, Sara Seguin, Alloy’s Principal Advisor on Fraud and Identity Risk, reported that 39% of banking professionals called siloed systems their most painful authentication challenge, and 39% said fraudsters are exploiting the gaps between those silos. 

These banking professionals were referring to the siloed fraud risk management architecture many financial institutions and fintechs rely on, where authentication and fraud prevention run on separate tracks, with separate teams, separate metrics, and separate tools. On the one hand, Customer Identity & Access Management (CIAM) tools typically treat authentication as a deterministic process (credentials either match what's on file or they don't). Fraud teams, meanwhile, are measured on whether losses occurred. The two functions address different but related issues, and that gap is where account takeover lives.

Without a shared view of authentication and fraud data, no single team has enough information to know which control failed and why. The lack of clarity is a fraudster’s way in.

Fraudsters have evolved, but many fraud detection approaches haven’t

Siloed fraud detection architecture is a vestige of a time when fraud was a transactional problem — a far simpler threat than the cross-channel web it is today. All financial institutions had to do was authenticate access, and then fraud teams would catch bad actors at the point of payment, before any money moved.

But as fraud schemes evolved, the fraud risk management programs built to detect and stop them couldn't keep up. Account takeover attacks now run across the full customer lifecycle and the full span of customer-facing touchpoints, including digital banking, contact centers, mobile apps, and in-branch channels. This makes ATO among the types of fraud hardest to catch with channel-specific or point-in-time controls. 

So when authentication and fraud detection data aren't analyzed together, potential fraud patterns that span multiple channels are effectively invisible. 

An account takeover, step by step

We established that coordinated account takeover attacks can go unnoticed without a unified view across authentication and fraud data. The scenario below is an example of how a fraudster actually exploits an institution's disconnected systems to successfully stage ATO.

1. Building the target

A customer's data, exposed through phishing or a prior breach, surfaces on a dark web marketplace. A fraudster buys it, gaining enough compromised PII to pass weak identity checks: name, date of birth, address history, phone number, last four of SSN, recent transaction details, and answers to knowledge-based authentication questions.

2. Getting past the front door

Rather than attacking the login page directly, the fraudster calls the financial institution’s contact center and claims to be locked out of their online account. Using the breached PII, they pass the call center's KBA. Then, they successfully reset the account’s credentials and change the authorized phone number on file.

The call center has no reason to hesitate; the caller passed every check the script requires. What it can't see is the risk context sitting in the institution's own digital and account systems like device history, behavioral patterns from prior sessions, or any anomaly flags generated elsewhere in the customer's account. That data lives in a different system, monitored by a different team, and never reaches the rep on the phone. 

3. Taking over the identity and draining the account

Once inside the account, the fraudster turns off security notifications. The system treats this as a routine preference change, not a risk event, because it isn't connected to the recent call center context. From there, the fraudster adds a new payee and starts transferring funds. The fraud system may flag the activity and trigger an OTP challenge, but the fraudster, who changed the authorized phone number, passes it. By the time the fraud team catches the unauthorized access, the funds are gone.

What the bank’s controls failed to do was tighten in response to high-risk signals stacking across channels or scale the step-up challenge to match that accumulating risk. A standard OTP fired despite everything that had already gone wrong.

Siloed controls mean shared exposure

If risk exposure that leads to account takeovers is happening across multiple channels, a natural response might be to invest equally across physical and digital channels. Alloy's 2026 State of Fraud Report found that 81% of decision-makers at financial organizations with at least one physical branch do exactly that.

But channel investment isn't the same as closing the gap between authentication and fraud detection. An institution can fund its branch and its app equally and still run separate authentication stacks for each, with no shared view of a customer's risk across either one. The vulnerability isn't how much gets spent on a given channel; it's whether the systems inside those channels talk to each other.

The same disconnect shows up along the customer lifecycle. Onboarding is typically when a financial institution captures the richest identity signals it will ever have on a customer, including device fingerprint, IP reputation, behavioral biometrics, and KYC verification data. But most institutions don’t pass that data downstream to the workflows that govern login, credential changes, or transaction monitoring, so every interaction after account opening gets evaluated from scratch. Fraud exposure rises because authentication decisions are made in isolation, operational overhead rises as more legitimate activity gets kicked to manual review, and the customer experience suffers as good customers get treated like risks.

Cybercriminals who run coordinated ATO attacks are aware of these vulnerabilities. They route attacks deliberately across digital banking platforms, contact centers, and physical branches because each channel operates within its own detection perimeter. Suspicious behavior in one system won't generate red flags in another, and investment in each channel in isolation doesn't close those vulnerabilities. For senior management and other stakeholders, this limits fraud awareness and makes the risk of fraud across channel boundaries difficult to quantify.

According to Alloy's 2025 State of Scams Report, 87% of consumers say they would lose trust in their bank if it failed to notify them immediately of a scam attempt. When fraud investigations begin after money has already moved, the potential impact is already materializing: reputational damage, financial losses, and — in serious cases involving money laundering or coordinated financial crime — law enforcement involvement. Immediate notification requires real-time detection, and real-time detection requires authentication data and fraud outcomes evaluated together.

Authentication and fraud, unified

A fraud risk management program that evaluates authentication events in the context of other identity touchpoints in the customer lifecycle and across channels can identify the pattern of a coordinated attack before financial losses occur. This is what Alloy's risk-based authentication solution is built to do. It provides continuous, contextual, and adaptive assessment of every interaction across the full customer lifecycle, rather than treating authentication as an isolated decision.

In effect, risk-based authentication solves the issues caused by siloed fraud risk management architecture  in a few concrete ways:

• It carries context forward. The identity and fraud signals captured at onboarding, including device fingerprint, IP reputation, behavioral biometrics, and KYC data, don't expire. Alloy stores them as a persistent trust profile, so every subsequent interaction gets evaluated against what's typical for that customer, not a generic baseline.
• It provides omnichannel monitoring. Risk-based authentication applies unified, dynamic decisioning at every touchpoint across every channel, so call center agents see the same risk and trust signals that digital sessions already established, improving account security.
• It reads risk at the entity level to complement session-level risk. Using Fraud Signal, Alloy's machine learning predictive model, it takes into account signals across a customer's full account history, enabling decisions based on gradually escalating suspicious behavior that single-session context wouldn’t catch.
• It matches the response to the risk. With risk-based authentication, step-up scales to what a session actually presents rather than issuing the same challenge regardless of how much risk has already accumulated. Meanwhile, good customers with strong trust signals avoid unnecessary friction.

And when one of these signals does escalate into a flagged case, Alloy's AI Assistant takes over, triaging alerts and assembling context so fraud teams can take quick action, with full auditability.

Learn more about Alloy’s risk-based authentication solution 

The full picture

With Alloy, financial organizations approve more good customers, catch more fraud before money moves, and build a program where every confirmed case makes the next decision more accurate. Coordinated attacks surface at both the account and portfolio level, and confirmed fraud cases sharpen the machine learning models that govern future authentication decisions. Measuring authentication against confirmed fraud outcomes is what separates a program that passes every check from one that actually prevents fraud.