Automate, detect, respond, and scale without compromising your controls.
Whether you’re stopping fraud at origination or automating compliance work behind the scenes, Alloy helps you apply AI where it matters most. Catch more fraud and cut busywork to focus on growth.
Automate operationally-heavy work and eliminate manual friction in risk and compliance workflows.
Score customer risk to surface suspicious behavior throughout the full customer lifecycle.
Detect coordinated fraud attacks at account origination across your entire portfolio.
Agentic AI that lives inside your fraud and compliance workflows.
Spend less time investigating each case while adhering to AML, KYC, and CRA requirements.
Alloy’s agentic future: Building agentic AI tools for risk and identity
Understand why an application is pending and what should happen next. AI-generated summary analyses pull from data, policies, and prior actions, so you don’t have to track them down.
Uncover insights you might otherwise miss, like identifying trends across multiple cases and explaining anomalies clearly. Enact decisions to progress investigations, with every step captured with a complete audit trail.
Focus your time where risk is highest. AI Assistant conducts research on watchlist alerts and links to supporting sources so you can quickly separate true hits from false positives.
Run business research, website analysis, registration checks, and UBO verification at onboarding and beyond. The AI Assistant synthesizes findings with Alloy’s internal data to recommend a review decision.
Meet our proprietary machine learning models.
Most fraud tools look at behavioral or identity data in isolation. Fraud Signal connects customer signals across time and context for more accurate decisioning.
Pinpoint risk that might seem benign in isolation by factoring in the entire customer journey — from onboarding through account activity.
Evaluate a customer’s risk with a single score that reflects overall behavior, not one-off events. Fraud Signal aggregates risk signs across onboarding, account activity, and transactions.
Distill signal from noise. Fraud Signal helps you reduce unnecessary friction for legitimate customers while giving high-risk activity the attention it deserves.
Adapt to new fraud tactics by learning from historical and emerging patterns. Fraud Signal helps your team stay ahead without constantly rewriting rules.
Fraud Attack Radar is designed to identify coordinated fraud attacks as they appear in your origination funnel, then helps your team respond in real-time by enabling safe-mode controls.
Protect growth without taking channels offline. Timely notifications flag suspicious activity earlier so you can take prompt action to contain risk.
Secure channels with automated safe-mode or targeted controls. Keep legitimate customers moving while streamlining back-office operations.
See attack velocity, scope, and impact at a glance. Get explainable insights and leading indicators to help inform your next steps.
Stay on top of your funnel’s health with an evolving portfolio-level risk score. Connect thousands of applications to see systemic risk patterns in-motion.
Alloy’s machine learning models and AI Assistant operate on the same data, policies, and customer history as your identity and fraud prevention workflows. You can even bring your own third-party and internal AI models to our platform.
Deep data foundation: Powered by rich identity signals and insights from Alloy’s unique position in the industry
Identity-centric by design: Stop bad actors before money moves by connecting fragmented data under a central interface
Holistic view: Policy-makers and agents operate on richer context, referencing real policies, past decisions and agent actions, and customer history
Actionable insights: Close the loop between decisioning and action. Deploy fallback workflows and understand the policies in place
“Alloy's AI Assistant saves me a significant amount of time and gives me the confidence to quickly review and close watchlist alerts based on the insights it provides.”
"Fraud Signal delivers the precision and speed necessary to intercept emerging fraud, effectively augmenting the capabilities of our existing controls and leading to a tangible enhancement of our overall risk coverage."
"Not only will [Fraud Attack Radar] notifications allow us to take quick action and contain potential risk, they'll help us save time and resources in the long term so our teams can focus on having a unified and holistic view of identity within the Alloy platform."
Alloy uses portfolio- and account-level machine learning models to assess customer risk in real time. These models analyze identity, behavior, and activity signals to detect anomalies as they emerge, from coordinated onboarding attacks to downstream fraud. This approach allows teams to apply AI flexibly — combining models trained on historical fraud data with adaptive intelligence that surfaces new and evolving attack patterns.
Alloy pairs AI-powered detection with automated response workflows. Once risk is detected, actions like safe-mode policies, step-up checks, and investigations can be triggered in real time, allowing teams to control exposure while minimizing disruption to good customers.
Fraud Attack Radar (FAR) is a specialized AI fraud detection tool that is purpose-built to spot large-scale fraud attacks in your onboarding funnel. FAR connects patterns across applications to sound the alert when a financial organization is experiencing a coordinated attack. By using FAR within the Alloy platform, you can immediately contain mass-scale threats and remediate impacted channels while keeping legitimate customers moving forward in the funnel.
Sometimes, a fraud problem becomes too complex for static decision trees or rule-based systems. In situations like these, multiple weak indicators can combine into a high-risk pattern that no single data solution could detect alone.
Fraud Signal is a machine learning fraud model that scores risk at the customer level, looking beyond single transactions to capture behaviors across a customer’s application, account, and interactions. It works by combining real-time onboarding data, behavioral signals, and both monetary and non-monetary activity, enabling it to adapt continuously to new tactics and reducing false positives. Fraud Signal can help uncover patterns such as account takeover attempts, money mule activity, and new account fraud.
The Alloy AI Assistant is a native AI agent that delivers a set of actions across fraud and compliance workflows. Rather than deploying separate tools or copilots, Alloy provides one cohesive assistant that works across onboarding, investigations, and ongoing risk management. It’s designed to handle operationally heavy, non-predictive workflows that require completing many interconnected tasks. Alloy’s AI Assistant works in tandem with Alloy’s decisioning engine, using context from workflows to help prioritize high-risk alerts and progress decisions forward.
Alloy’s AI Assistant is designed to bring agentic automation to any action driven by a standard operating procedure (SOP), giving teams the option to delegate any manual step in the application and investigation review process to AI. It can help reviewers triage alerts, gather and check information, and directly implement suggested next steps — all with built-in controls, explainability, and auditability. With predictive intelligence handling probabilistic decisions (e.g., fraud scoring), Alloy’s agentic intelligence expedites procedural work that can’t be straight-through processed (e.g., investigations, data summaries, and verifications).
Alloy reduces manual reviews by automating clear decisions and routing only edge cases to analysts. Data enrichment resolves ambiguity upfront, while intelligent prioritization focuses teams on the highest risk files. Built-in explainability and step-up verification help self-resolve when possible. This shifts fraud and compliance teams from high-volume processing to higher-value investigations. It also makes it easier for these organizations to add on new products and scale operations without adding risk.
