AI & Data Built for Regulated Banking
AI and data you can put in front of an examiner
ACM Global Tech turns siloed banking data into a single, governed foundation, then applies agentic AI and predictive analytics where they move the numbers: lending, risk, fraud, and member experience. Regulated-first, explainable, and client-owned.
Most institutions have data everywhere and insight nowhere
Core, lending, payments, and member channels each hold a piece of the truth. The result is duplicated records, slow reporting, and AI projects that stall because the underlying data was never trustworthy.
ACM treats data as the foundation, not an afterthought. We unify your sources into a governed, real-time layer with lineage and access controls built in, so every model, dashboard, and decision draws from the same audited record. AI only becomes safe to deploy once the data beneath it is.
From raw data to governed intelligence
A connected stack that takes your institution from scattered systems to decisions you can defend.
Unified Data Foundation
One governed layer across core, lending, payments, and channels, with lineage, quality controls, and real-time access for every downstream use.
Predictive Analytics
Forecasting for credit risk, deposit behavior, liquidity, and member attrition, surfaced where your teams already work.
Agentic AI
AI agents that automate document-heavy workflows, underwriting prep, servicing, and operations, with a human in the loop on every consequential step.
Real-Time Fraud & Risk
Behavioral models scoring transactions as they happen, layered with post-quantum cryptography to protect long-lived records.
Member & Customer Intelligence
Segmentation and next-best-action that deepen relationships and reduce churn, without crossing privacy or compliance lines.
Decisioning APIs
Scores and signals delivered as clean, brandable APIs your applications and partners can consume directly.
Explainable, auditable, and yours to own
AI in a regulated institution has to survive a model-risk review, not just a demo. Ours is engineered for that conversation.
- Explainability by default: every score and recommendation carries the reasoning and inputs behind it, so model-risk and compliance teams can validate decisions.
- Governance and lineage: access controls, audit trails, and full data lineage make outputs traceable end to end.
- Human-in-the-loop: agentic workflows are designed to assist staff and escalate consequential calls to people, not replace judgment.
- Post-quantum security: sensitive data is protected with post-quantum cryptography against "harvest now, decrypt later" exposure.
- Client-owned: the data foundation, models, and outputs are yours, white-labeled and portable, never locked to a vendor.
Enterprise-grade AI, shipped on an Agile Speed timeline
ACM's platform plugs into the Hanzo.ai agentic-AI stack and Lux Network settlement infrastructure, putting capabilities once reserved for the largest banks within reach of credit unions, community and mid-sized banks, and healthcare organizations.
Our Agile Speed Framework gets the data foundation stood up and a first high-value use case into production quickly, then iterates. You start with the model that matters most to your institution, prove it against your own data, and expand from there, all on infrastructure you brand and own.
Most teams begin with data analytics to unify their foundation, then extend AI into lending modernization for faster, fairer, more defensible credit decisions.
Connects to the systems your data already lives in
The unified data foundation reads from what you run today, so AI does not wait on a rip-and-replace. Ingestion, enrichment, and decisioning are exposed as documented, brandable APIs and SDKs.
Core & ledger connectors
Stream or batch records from your core, loan origination, and general ledger into the governed layer, with field-level lineage preserved from source to model input.
KYC, card & fraud signals
Join KYC/CIP, card-network, and existing fraud-tool feeds so scoring sees the full picture without forcing you to abandon current vendors.
Warehouse & BI
Bidirectional sync with your data warehouse and BI tools means analysts keep their dashboards while drawing from one audited record.
Decisioning APIs & events
Consume scores, segments, and agent outputs over REST and event streams, or push them back into servicing and channel systems in real time.
Connectors are versioned and mapped during onboarding so an integration change never silently breaks a model or a report downstream.
You decide where your data and models run
Because the foundation, models, and outputs are client-owned, the institution controls where training data sits, where inference happens, and who holds the keys.
- Flexible hosting: deploy in dedicated cloud, hybrid, or single-tenant private environments so regulated data never shares infrastructure you have not approved.
- Data residency: pin storage, training, and inference to specific regions to meet examiner and jurisdictional requirements.
- Bring-your-own-key: post-quantum encryption with customer-managed keys keeps long-lived financial records under your control, not the platform's.
- Environment isolation: separate development, validation, and production data planes give model-risk teams a controlled path before anything reaches members.
- Compliance-ready: the architecture is designed to support SOC 2, ISO 27001, PCI-DSS, and HIPAA requirements across the AI and data layer.
Rollout follows the Agile Speed Framework, standing up the foundation in your chosen environment before extending into lending or fraud use cases.
Ready to make your data decision-ready?
Tell us where your data lives today and what you want it to do. We will map a practical path from unified foundation to AI in production, built for the scrutiny your institution faces.
Talk to ACMFrequently asked questions
Is ACM's AI explainable enough for a model-risk review?
Yes. Explainability is the default, not an add-on. Every score and recommendation carries the inputs and reasoning behind it, backed by full data lineage and audit trails, so model-risk and compliance teams can validate how a decision was reached. Agentic workflows keep a human in the loop on consequential calls.
Do we own the data foundation and models, or are they locked to ACM?
You own them. The unified data foundation, models, and outputs are client-owned, white-labeled, and portable. ACM builds on infrastructure you brand and control, with no vendor lock-in on your data or decisioning logic.
How does ACM keep sensitive financial data secure?
Sensitive data is protected with post-quantum cryptography to guard against "harvest now, decrypt later" attacks on long-lived records, layered with governance, access controls, and real-time fraud and risk scoring across transactions.
Where should an institution start with AI and data?
Most begin by unifying their data foundation through data analytics, then extend AI into a single high-value use case, often lending modernization or fraud, and expand from there. The Agile Speed Framework is built to get a first use case into production quickly and iterate.