Financial services is one of the few industries where the rules around model governance predate the current generative-AI wave. SR 11-7 has been on the books since 2011; it already requires banks to understand, validate, monitor, and challenge the models they put into production. The arrival of large language models did not create a new regime — it pushed an existing regime into a technology that is substantially harder to govern.
Neuraphic builds for institutions that take that regime seriously. Our systems are designed so that the model risk team, not just the engineering team, can answer the questions a regulator will ask.
"A detection system that performs well in backtesting and then collapses on contact with a motivated attacker is worse than a system that was never deployed."
Fraud detection without brittleness
Fraud is an adversarial problem by definition. A detection system that performs well in backtesting and then collapses on contact with a motivated attacker is worse than a system that was never deployed, because it teaches the organization to trust the alerting. Our research program includes an explicit focus on adversarial robustness for classification systems, and our inference-time defense product Prion is available to harden language-model components of a fraud pipeline against prompt injection and related attacks.
Where the detection stack blends classical models with language-model reasoning — for example, narrative-based case review or analyst assistance — we treat the language-model layer as an attack surface and defend it accordingly.
Model risk management, SR 11-7 aligned
We design our systems so that the artifacts a model validation team needs are available, not reconstructed after the fact. That means versioned model identifiers, reproducible evaluation runs, documented data lineage, challenge-model patterns, and a decision trace that can be sampled and reviewed. It is not enough for a model to perform well; the institution has to be able to prove it performs well, to a person whose job is to disagree.
We do not sell "compliance in a box." We sell infrastructure that makes the institution's own model risk team effective, because we have not met a serious bank that would accept anything else.
Customer-facing agents that resist prompt injection
A customer-facing assistant is, among other things, a system that accepts untrusted input from the public internet and turns it into action inside a regulated enterprise. That is a risk profile most institutions would never accept from a human agent, and it should not be accepted quietly from a software agent either. Prion sits in front of language-model endpoints and classifies adversarial input at the inference layer, where it can be blocked before the model reasons about it.
Continuous architecture defense
Beyond the model edge, Claeth operates as an autonomous cybersecurity analyst tailored for financial infrastructure. Capable of reasoning about complex networks without requiring outbound internet telemetry, Claeth continuously audits SWIFT endpoints, payment gateways, and core banking dependencies, mathematically verifying patches against ephemeral shadow twins before production deployment.
Autonomous compliance workflows
Compliance operations inside a large institution involve enormous amounts of document review, correspondence drafting, and cross-referencing against policy. These are the places where a well-scoped AI assistant can meaningfully reduce cycle time without introducing new risk — provided it is built on an architecture that keeps data inside the institution's boundary and preserves an auditable record of what the assistant was asked, what it did, and why.
Compliance and trust
We are working toward the formal attestations financial-services customers expect, and we publish our posture honestly at our Trust Center. We do not claim certifications we do not yet hold. We are comfortable engaging directly with a customer's model risk, information security, and third-party risk teams — and we prefer to do so early.
Get started
Institutions evaluating AI for fraud, compliance, customer operations, or research can reach us at enterprise@neuraphic.com. We are comfortable beginning under non-disclosure and engaging with the risk and architecture teams before any commercial step.