Alloy’s agentic future: Building agentic AI tools for risk and identity

Alloy CTO Charles Hearn explains how Alloy’s native AI agent delivers enterprise-grade automation for identity, KYB, and fraud decisioning

GenAI is a groundbreaking technology with immense promise that, until recently, has been overshadowed by flawed implementations. From the start, we saw a real opportunity to solve some thorny problems where AI agents could be uniquely helpful.  We’ve been heads down on the mission to be the first company in our space to get it right.

What does getting it right mean to me? It means the input and output is simple, connected, reliable, and actionable. We use the term “actionable AI” at Alloy because the tools we launch, like Fraud Attack Radar and Fraud Signal, act on existing data and embed directly into our clients’ regular workflows so they don’t feel or function like standalone products. They work - they’re actionable! 

What makes agentic AI uniquely challenging in our industry is that without direct connections to the systems of record, the original decisioning methodology, and all the data generated from onboarding, transactions, logins, and other account events, the results consistently come up short. They feel like agents without a home. 

Alloy’s platform was built to bring multiple sources of data and intelligence together in one “home” and to help our clients get a clear picture of what those different inputs all mean. 

Introducing the Alloy AI Assistant

The Alloy AI Assistant runs and automates processes within the Alloy platform that require some human reasoning and cannot be handled through straight-through processing (STP) using orchestration rules or predictive machine learning.

This is not a tangled web of a dozen agents or another chatbot; it is an intelligent assistant with deep knowledge and a strong connection to your organization's context, including decisioning logic and custom rules and configurations. 

Alloy is known for its seamlessness. We’ve always prided ourselves on our ability to serve an array of use cases and types of financial organizations, all on the same rails. If we’re doing our jobs right, all of our products talk to each other and flow together to seamlessly understand end-to-end entity-level and portfolio-level risk. That’s hard! But we’ve always held ourselves to that standard.

The Alloy AI Assistant embodies this same philosophy. 

What can Alloy’s AI Assistant do? 

Alloy’s AI Assistant was built to handle any tasks that prevent financial institutions and fintechs from full automation. It improves the speed and accuracy of sanctions screening, KYB reviews, due diligence for consumer applications, and document reviews. 

A few examples of the AI Assistant in action:

• Faster application reviews: It will generate a clear, tailored summary of why an application is pending  and provide guidance on what should happen next so human reviewers don’t have to hunt through data sources, documents, and rules.
• Watchlist hit resolution: Alloy’s agentic summaries help analysts understand why an individual was flagged, review linked sources with context, and distinguish likely watchlist matches from false positives without ever leaving the Alloy platform.
• Faster, more confident KYB decisions: Instantly assess the business behind the application through Alloy-corroborated data and web research, supported by transparent sources and detailed evidence to skip hours of manual work.

These are just a few examples of our agentic solutions in action. The Alloy AI Assistant supports a growing set of use cases, and we’re constantly evolving it to handle more complex workflows. 

What sets the Alloy AI Assistant apart? 

If this feels like the hundredth “AI agents for identity” announcement you’ve read, I have good news. It’s the last one you’ll need. That’s because we have a fairly bold claim: Alloy is uniquely positioned to offer the most comprehensive agentic AI solution for risk management.

Rather than building our own solution, we could have simply partnered with a standalone AI agent. But we've learned over the years that when we need data, we partner. When we need automation, Alloy delivers. It's the same reason we built our own review and case management systems. Here’s why:

1. Agents need a deep understanding of the decisioning methodology

AI agents live and die by context. Without it, they guess, and guessing is not acceptable in risk management. 

When an applicant ends up in review, understanding why requires full visibility into complex, multi-step, versioned decisioning strategies. Really, this is the kiss of death for any external vendor that isn’t the core decisioning system. A  stand-alone AI tool can try to infer that context or import data from the decisioning system, but neither approach captures the full picture. 

Even with all of the proprietary decisioning logic in Alloy, we had to iterate on how best to pass rule logic to an LLM. LLMs are trained on language, so speaking naturally is more “in-distribution” (what the LLMs are trained on and more comfortable interpreting) vs passing our raw structured proprietary rules formats, which are “out-of-distribution” (the LLM doesn’t natively understand our proprietary formats because they weren’t originally trained on them).

That left us with a few options: either fine-tune our models to better understand Alloy’s proprietary complexity, or explain what happened in detail to our agent as it tries to understand it. 

So far, we’ve chosen something more like the latter. We can explain in plain English exactly what happened during the automated decisioning process to the agent, and we can explain Alloy terminology under the hood so the agent doesn’t get confused when we use “Alloy terms” like tags, workflows, or journeys. With this, our agent gains the unique benefit of truly understanding why something is in its current state, in a way no other product can replicate.

2. Agents must commit back to the system of record to complete the loop

An agent that can analyze but not act has limited value.

If, at the end of a stand-alone agent running a watchlist resolution or a KYB investigation, that agent can’t actually do anything, then your future automation potential is always going to be limited. We’ve seen teams pilot stand-alone agents that still require humans to upload results back into Alloy, click buttons, or reconcile the outcomes across systems. 

Alloy’s AI Assistant, on the other hand, doesn’t just recommend actions. As part of our actionable AI suite, it enables clients to execute recommendations directly within the platform, closing the loop while preserving a single source of truth for decisions. 

3. Agents need access to internal and historical data

Fraudsters thrive in data silos. 

Risk decisions require more than onboarding data; they require historical behavior, transactions, logins, and account events. If those datasets are fragmented, agents inherit the same blind spots humans do.

Alloy is built to unify that data. Our AI Assistant can reason across the full customer lifecycle, not just a single moment in time.

4. Agents need access to external and canonical data

Unfortunately, some of the most successful agentic companies today are simply ingesting Yelp reviews and making guesses or assumptions. 

To put that in perspective, despite Alloy’s long history at our current address, I tested a leading KYB agent with a slightly incorrect address. Without a canonical source for business addresses, the agent joyfully approved it, reasoning that Alloy has a NYC headquarters and that the address I provided, three blocks away, was likely correct.

This example is proof that LLMs are still too agreeable. Without authoritative data, they will confidently hallucinate. 

Alloy’s agent is grounded in the canonical data you’ve already deployed from over 250 of the industry’s leading identity and fraud solutions. These integrations are contractual, stable, and enterprise-grade.

5. Alloy has a long history of enterprise-grade success and deep technical expertise

One of the reasons over 700 of the world’s largest fintechs and financial institutions trust Alloy is that we don’t build new tools or solutions just because they are “innovative” or at the top of a hype cycle. While others may race to be first to launch, we focus on being the first to get it right. Our approach to AI is no exception.

The Alloy AI Assistant is configurable to client-specific policies, aligned with emerging AI governance frameworks, and designed to meet the same expertise standards as everything else we ship. 

Why now

This confluence of forces felt familiar. In 2015, when we founded Alloy, cloud infrastructure reached a point where banks trusted outside organizations to manage their third-party data and run mission-critical decisioning logic. If we’d started a year earlier, Alloy would have had to go on-prem. 

The timing was right then, and we got ahead of that shift. Over the past year, the timing became right for agentic AI, and we’ve shifted again. 

This is Alloy’s agentic future, built to deliver real automation without compromising trust.