Every banking AI agent, approved with evidence, not demos
Fraud, collections, next best offer, underwriting support, service, and complaints, across customer-facing and risk and decisioning agents. GenGuardX is the shared environment where business teams and model risk, compliance, and fair-lending teams validate and monitor any AI agent, whoever builds it, with the evidence to approve before launch and the monitoring to trust it after.
Before any agent ships, two teams have to say yes
It's rarely one bot. It's a roadmap of customer-facing and risk and decisioning agents, from fraud and underwriting to service and collections, and every one clears the same gate. Business and growth leaders need confidence the AI grows the business accurately and on-brand. Model risk, compliance, and fair-lending teams need evidence it stays fair, explainable, and within regulation. Most pilots stall because neither side has a shared, defensible way to test, decide, and document.
Fraud, lending, collections, and marketing leaders own the customer relationship, but are often sidelined during technical testing. GGX lets your analysts and SMEs interact with the AI, validate it against real scenarios and policy, and build evidence-based confidence to sign off.
Before banking GenAI goes live, model risk and compliance teams need more than a demo, they need evidence. GGX stress-tests models for unfair outcomes and unsafe behavior, tracks how gaps close, and builds an audit trail that holds up to examiners. SR 26-2 left generative AI out of scope, so here is how banks govern it now.
"In a regulated bank, confidence can't come from a demo, it has to come from evidence."
Where banks put GenGuardX to work
The agents on your roadmap span the customer journey, from acquisition to service to recovery, across customer-facing and risk and decisioning work. For each one, GGX defines the risks that matter, runs standardized evaluations, and produces the evidence to approve and monitor.
Fraud management
Surface fraud and scam signals across transactions and channels with explainable evidence, recommending safe step-up without over-blocking good customers.
Collections outreach calling
Automate compliant, on-tone collections conversations that follow policy and disclosure rules, escalating sensitive situations to human agents.
Next best offer
Recommend relevant, fair offers grounded in eligibility and suitability, improving conversion without unfair targeting or invented terms.
Underwriting decision support
Assist credit and underwriting review with grounded summaries and recommendations, with traceable, fair-lending-aware justification for every decision.
Customer service & contact center
Deflect and resolve servicing questions (balances, disputes, payments) with accurate, on-policy answers and clean handoff to agents.
Complaint & dispute handling
Draft clear, compliant complaint and dispute responses that cite policy and stay within UDAAP bounds, improving consistency and timeliness.
Turn banking AI review into a repeatable workflow
The same four steps clear the gate for any agent, customer-facing or risk and decisioning. Identify the risks that matter, measure them with standardized evaluations, mitigate the gaps, and monitor after launch, with evidence at every step.
Map the risks
Select use-case risks: policy adherence, fair lending and bias, hallucinated terms and rates, PII leakage, adverse-action explainability, prompt injection.
Run standardized evals
Repeatable tests against curated banking datasets, expected outcomes, policy documents, and thresholds, not one-off scripts or vibes.
Close gaps & retest
Apply guardrails, escalation logic, retrieval grounding to policy, or prompt changes, then prove the gap is actually closed.
Watch for drift
Turn production traces into alerts, evidence, and new test cases as policies, models, and customer behavior change after go-live.
Every analyst reaction becomes ground truth
Each flag, rating, and annotation from a fraud, lending, or compliance SME becomes reusable, structured ground truth, supporting objective measurement, faster iteration, monitoring, and future evaluation sets. SME time is scarce; GGX captures it once and reuses it across the whole AI lifecycle.
The risks GenGuardX tests for in banking AI
Pre-built, banking-specific evaluation categories, plus your own custom risks, thresholds, and datasets.
Policy & terms groundedness
Answers stay faithful to policy, product terms, and the evidence, not invented.
PII data leakage
Detect exposure of personal and account information and privacy violations.
Hallucinated terms & rates
Catch fabricated rates, fees, and product detail before they reach a customer.
Unfair lending & bias
Test for disparate outcomes and performance across customer populations.
Adverse-action explainability
Verify every adverse decision is justified, traceable, and defensible.
Prompt injection & jailbreak
Stress-test against manipulation, data exfiltration, and out-of-scope use.
One shared environment, built for banking AI
The same modules your business, model risk, and compliance teams use to test, approve, and monitor any AI agent, connected to your stack and your evidence.
Pipeline Builder
Compose multi-agent systems: an orchestrator routing to specialized sub-agents, each with its own model, tools, and grounding. Then compare whole configurations to find the safest setup for each banking use case.
Human Integrated Testing
Fraud, lending, and compliance SMEs run real scenarios against the AI, flag issues, and rate responses: human judgment combined with automated scale and a closed feedback loop.
Risk dashboards & curated reports
Starter banking tests and customizable reports for policy adherence, fair lending, PII, and explainability: versioned, reusable, and ready for audit.
Trust Score
A single, defensible readiness measure, rolled up from objective sub-metrics and tracked version over version, so progress and approval are provable.
Governed Workbench
Role-based access, lineage, versioning, and approval workflows so business, model risk, and compliance teams work from one shared, auditable record.
Connectivity
Out-of-the-box LLM connectors plus API hooks into core banking and contact-center platforms for pre- and post-production monitoring.
Trusted by a Tier 1 bank
A GSIB adopted GenGuardX as the shared testing and evidence layer across multiple AI initiatives. Details anonymized at the customer's request.
One evidence layer, multiple AI initiatives
The challenge: multiple AI initiatives across fraud, collections, and decision support, each stuck without a shared, defensible way to test and prove readiness. What GGX did: gave business teams a way to test and validate AI and gave model risk and compliance a consistent, audit-ready evidence trail, replacing ad-hoc, subjective testing with a repeatable workflow.
Compliant collections at scale
The challenge: a collections calling assistant with tone, disclosure, and fairness stakes. What GGX did: drove iterative SME validation and fairness testing until thresholds were met and the evidence stood up to compliance review.
Built to clear your security and compliance review
Banking AI doesn't ship until your CISO, model risk, and compliance teams sign off too. GenGuardX is built to pass that review: your data stays protected, your deployment stays in your control, and every action is logged and auditable.
Your data stays yours
We never train models on your data. Everything is encrypted in transit and at rest, with configurable retention, and your customer and account data never leaves your control.
Certified & model-risk aligned
SOC 2 Type II, with model-risk-aligned documentation and evidence packs built for examiners and model risk validation.
Deploy anywhere
Secure SaaS, your own VPC, or fully on-prem, with data-residency options.
Model-agnostic
OpenAI, Google, Anthropic, or your own private and self-hosted LLMs.
Access & audit
SSO/SAML, role-based access, and immutable audit logs across every test, approval, and version.
Enterprise cloud
Built on Google Cloud and AWS, with the isolation and reliability enterprise banks expect.