Support controlled AI adoption across service, payments, fraud operations and internal productivity workflows.
The following figures represent telemetry outputs recorded during a structured pilot deployment with a real-time payment platform operating under Australian regulatory requirements. Figures are illustrative of the telemetry range.
Payment workflows where AI-assisted decision support was recorded across the pilot period.
Policy evaluation events applied across transaction lifecycle stages during the engagement.
Operational incidents where telemetry signals triggered manual review or elevated intervention.
Average time to rebuild a complete AI decision pathway during post-incident investigation.
Workflows recorded interacting with more than one automation or AI provider in a single transaction journey.
Workflow exceptions requiring operational team involvement outside automated processing paths.
Proportion of AI-influenced workflows with retained reviewable telemetry records at pilot close.
Requests from risk and compliance functions for decision-level telemetry during the engagement period.
Figures are illustrative of telemetry output ranges observed during a structured pilot engagement in a real-time payments environment. They do not constitute financial performance data or guaranteed operational outcomes.
Real-time payment adoption in Australia is increasing decision speed across servicing and fraud workflows. As AI becomes embedded in operational processes, structured telemetry helps institutions maintain governance visibility alongside automation.
Supports telemetry visibility across AI-assisted payment workflows and routine transaction handling.
Enables structured review of automated financial decisions before they reach core settlement rails.
Integrates without core banking replacement, acting as a non-invasive telemetry and policy layer.
Provides exportable operational evidence for governance teams, risk committees and regulatory audits.
Supports role-based policy ownership models, separating technical config from risk-based rule sets.
AIxSafe is designed to fit into existing regulated infrastructure with minimal friction, providing a dedicated control domain between internal apps and model providers.
Deploy as a managed service within your VPC or as a dedicated SaaS instance with private link connectivity.
A centralized inspection layer that scrutinizes traffic for PII, prompt injection and policy violations.
Granular logs are routed to your existing SIEM or internal data lake for permanent retention and analysis.
Reduce uncontrolled transfer of customer and payment data into model providers. AIxSafe enforces strict data inspection and redaction policies at the point of egress, ensuring PII remains within approved environments.
Read Governance ArticleSeparate sensitive workflows from lower risk productivity use cases. Use policy-driven routing to ensure high-value payment instructions are handled only by approved, high-trust model endpoints.
Detecting Goal HijackGive first and second line teams a common view of AI activity and exceptions. Support committee packs, assurance reviews and investigations with immutable, decision-level telemetry.
CPS 230 AlignmentPayment platforms operating on real-time rails experience compressed decision windows, increased servicing automation and growing reliance on AI-assisted tools.
These operational conditions create demand for telemetry signals that support governance, escalation and review across regulated financial workflows.
Compressed decision timelines increase reliance on automated servicing and routing systems.
Regulatory frameworks such as APRA CPS 230 require demonstrable controls over automated financial decision processes.
Multi-vendor payment architectures create fragmented decision trails without a central telemetry layer.