Product deep dive: How Fraud Signal, Alloy’s machine learning model, powers better fraud detection with less friction

Revised Model Risk Management guidance serves as a reminder that financial institutions and fintechs should explore embedding high-quality machine learning models into their fraud stacks

Last month, the Federal Reserve, FDIC, and OCC issued updated Model Risk Management guidance, revising expectations around model governance and underscoring a key point: risk is not uniform across institutions, products, or markets.

The updated guidance calls for financial institutions and fintechs to adopt a risk-based approach to model governance, calibrated to the institution’s specific risk profile, customer base, and use cases, rather than a one-size-fits-all model. 

This regulatory development presents a strategic opportunity for financial institutions and fintechs to take a fresh look at the benefits of deploying machine learning models to manage identity and fraud risk. 

The truth of the matter is, if you’re not using AI and machine learning to fight fraud today, you’re already behind. 

However, for Alloy clients, Fraud Signal is readily available, battle-tested, and makes it easy to operationalize machine learning in fraud prevention. 

What is Alloy’s machine learning model, Fraud Signal? 

Fraud Signal is Alloy’s proprietary predictive model that continuously scores customers for fraud risk across their lifecycle, from first touch to every interaction thereafter. As part of Alloy’s suite of Actionable AI tools, Fraud Signal delivers a single, actionable score for customers from 0 - 0.99 in real-time, so you can prioritize alerts, initiate step-up verification, flag transactions, or trigger other fraud monitoring rules. 

How does Alloy’s Fraud Signal solution work?

Fraud Signal connects a series of point-in-time checks into a unified, continuous risk profile. Here’s how it works:

1. Ingest rich signals across the full lifecycle: Fraud Signal uses onboarding data, transactional patterns, and non-monetary events (such as logins, device, and account information changes) to establish a baseline risk profile.
2. Score in real time: As your customers use their accounts, Fraud Signal dynamically computes a single risk score and offers accompanying qualitative indicators that give your team a real-time representation of risk at any given time. 
3. Take action with confidence: Clients can use the Fraud Signal score to make more informed decisions: allow, step-up, or flag account activity based on risk level instead of applying blanket friction. 

Ultimately, Fraud Signal scores enable you to reward your ‘good’ customers by unlocking self-serve experiences and enabling money movement (ACH, wires, P2P) with confidence, while simultaneously detecting bad actors and containing risk through intelligent, real-time interdiction.

What makes Alloy’s Fraud Signal model different from other predictive machine learning models for fraud prevention?

• One model, not many: A holistic view across payment rails and fraud types means no vendor sprawl, no fragmented controls, and no blind spots when new typologies emerge. One score that your whole team can act on.
• Network intelligence: Models are only as good as the data they’re trained on. Fraud Signal is built on intelligence from across Alloy’s network. That means emerging fraud patterns are detected earlier and with greater confidence.
• Identity-centric, lifecycle model: Most models evaluate transactions in isolation. Fraud Signal unifies signals from first touch to every interaction thereafter, delivering a real-time, 360° view of risk. The model knows your customer’s full history, and it uses it to provide the most complete picture of each customer’s risk level in real-time.
• Natively integrated and actionable: Insights are embedded directly into your decisioning and investigation flows within the Alloy dashboard, so they’re easy to operationalize. Use the Fraud Signal score to automatically trigger step-ups, and add or remove friction — closing the loop between insight and action.

Real results: How leading financial institutions and fintechs win with Fraud Signal

Fraud Signal underpins Alloy’s end-to-end fraud prevention solution and is already deployed across some of the most sophisticated fraud programs in financial services. Here is a look into what Alloy clients are seeing from Fraud Signal.

Fraud Signal catches the fraud that rules-based systems often miss

Rules have blind spots, Fraud Signal finds what they don't. Fraud Signal is proven to uncover instances of fraud that have been historically difficult to detect, such as first-party fraud, check fraud, and dispute fraud.

In practice: A leading US consumer fintech stops first-party fraud with Fraud Signal

A leading US consumer fintech’s rules-only monitoring system was missing key indicators of first-party fraud. After adding Fraud Signal, the model immediately began uncovering hidden risk patterns among recently onboarded users — identifying a cohort with 80% precision and allowing the team to intervene before any monetary losses occurred.

Fraud Signal reduces alert noise and analyst burden

With the rise in generative AI-driven fraud, fraud teams are overwhelmed. With Fraud Signal, fraud teams spend less time on false positives and more time on real risk by prioritizing manual queues by model confidence. And that’s only the beginning: Fraud Signal’s precision gets better over time as you share alert outcomes.

In practice: A fast-growing digital provider boosts operational efficiency with Fraud Signal

A digital provider’s fraud investigation team was drowning in alerts, many of them being false positives. After implementing Fraud Signal, they not only achieved an 82% reduction in false positives,so analysts were able to redirect their time toward genuine investigations but also surfaced high-risk check fraud cases that existing rules missed. 

Fraud Signal protects the customer experience

Most fraud tools cast a wide net — flagging legitimate customers as collateral damage. Fraud Signal is precise enough to catch the majority of fraud losses while leaving your ‘good’ users completely unaffected.

In practice:  A leading digital bank captures the majority of fraud losses with minimal user friction

This institution needed to stop fraud losses without flagging legitimate customers or overwhelming their operations team. In a POC, the model captured 73% of fraud dollar losses while alerting on just 0.5% of users — meaning the vast majority of genuine customers would never experience any friction. The result: significant loss prevention with almost no operational overhead and no impact to the customer experience.

What next?

Financial institutions and fintechs already using machine learning in their fraud programs are proving these tools don’t just reduce losses, they drive growth. Better and faster decisions mean less friction, higher conversion across the lifecycle, and less overhead for fraud teams.

The industry is moving toward a new standard: adaptive, intelligence-driven fraud defense that enables safe growth.

The question is no longer if you should evolve your strategy. It’s whether you’re moving fast enough.