Embedded AI stays off the inventory
Models now arrive inside SaaS products, internal applications, copilots, and agent workflows. Each entry point adds another provider and dependency to understand.
Financial services
AI is already inside the financial stack. Can you account for it?
Proxon gives product, risk, security, finance, and operations teams one shared record of observed and registered AI systems, attributable adoption where identity is available, registered workflows, and available spend context.
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The control gap
AI adoption is moving faster than the control environment.
AI now supports customer-facing and internal work across financial services. The systems behind that work often enter through different budgets, vendors, teams, and review processes.
Accountability fragments
Product, security, risk, compliance, procurement, and finance each hold part of the picture. Reviews slow down because the underlying records do not line up.
Spend loses business context
Model APIs, cloud services, licenses, and team experiments create separate cost trails. Finance sees the bill before it sees the work behind it.
The management layer
One operating view of observed and registered AI
Proxon brings the records behind observed AI adoption, registered systems and workflows, and captured or provider-reported spend into one product. Each function can use the view its responsibilities require.
AI tools, models, agents, and connected services
Bring observed activity and registered systems into a shared inventory, with providers and data sources recorded alongside them when that context exists.
Attributable usage across the organization
Where identity is available, compare observed adoption across people, teams, and departments without turning presence into a performance judgment.
AI spend with organizational context
Review captured and provider-reported spend by provider, model, department, team, and registered workflow when that context is available.
Registered use cases and outcome signals
Keep registered workflows and their curated or reported outcome signals alongside the systems and teams involved in the work.
Shared operating model
A common record across the lines of defense
The same observed and registered AI record looks different depending on the job. Proxon gives each stakeholder current evidence to bring into the governance, risk, procurement, and assurance processes they already run.
Product, engineering, and operations
Understand which observed and registered AI systems support customer and internal workflows, where attributable adoption exists, and where spend context is available.
Risk, security, and compliance
Bring a current view of registered and observed AI activity into model, technology, third-party, and operational-risk reviews.
Internal audit and assurance
Review the same observed inventory, attributable adoption, registered workflow, and available spend records used by the business and its oversight functions.
Finance and procurement
See where captured AI spend concentrates across providers, models, and teams before budgets and vendor commitments are set.
Where AI enters financial work
One estate, many material workflows
Financial institutions are applying AI across customer, control, and operating functions. Proxon keeps observed and registered systems, attributable adoption where identity is available, registered workflows, and available spend context together around that work.
Lending and underwriting
Document review, analyst copilots, research, and decision support
Financial crime operations
Alert triage, case summaries, investigator assistance, and quality review
Customer service and complaints
Agent assistance, summarization, knowledge retrieval, and interaction review
Markets and wealth
Research, meeting preparation, communications support, and advisor workflows
Engineering and resilience
Code assistants, incident analysis, runbooks, and back-office automation
Risk and compliance
Policy search, regulatory research, control testing support, and reporting preparation
Proxon manages the record around this work: observed and registered AI systems, attributable adoption where identity is available, registered workflows, and captured or provider-reported spend where context exists. Business owners remain responsible for lending, fraud, investment, and customer decisions.
The operating view
Answer the questions behind every review
Move from separate inventories and invoices to a connected view of attributable adoption where identity is available, captured or provider-reported spend where context exists, and registered workflow signals.
Adoption
See where AI is becoming part of the operating model
Where identity is available, compare observed adoption across people, teams, and departments so enablement leaders can see where uptake is spreading and where it has stalled.
Cost intelligence
Give finance an explainable view of AI spend
Review captured or provider-reported spend by provider, model, department, team, and registered workflow when those dimensions are available.
Workflow outcomes
Review the work alongside the outcome signals teams report
Keep registered workflows and curated or reported outcome signals connected to attributable adoption and available spend context.
Bring the estate into view
Bring a defensible AI record to the next budget, risk, or board review.
See observed and registered AI systems, attributable adoption where identity is available, registered workflows, and available spend context already shaping your institution.
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