Architecture for evidence, risk and accountability.

Banking & Financial Services

Banks need AI-assisted workflows that can be evaluated alongside existing governance, data, model-risk, operational-risk and control arrangements. ARVEC helps structure the decision, design evidence-backed systems and identify where human authority and assurance must remain explicit.

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01Decisions in context

The questions an AI-assisted workflow has to answer.

  • Is this document set complete and consistent enough to proceed?
  • Can this reported figure be reconciled to source and explained?
  • Which policy applies to this case, and where is that stated?
  • Who may approve the next action, and what must they see first?

Evidence and accountability in an AI-assisted review

  1. 01RequestCase and applicable policy identified
  2. 02Evidence assembledSources retrieved with provenance Control point
  3. 03Draft findingAI proposes; constraints checked Control point
  4. 04Authorised reviewAccountable person decidesOwner: Line approver Control point
  5. 05Record retainedEvidence, rules and decision logged
Architectural illustration. Control points and owners follow the institution’s own governance.

02Applications

Where ARVEC’s expertise applies

Application examples, not case studies or client engagements.

Document review and reconciliation
Findings across credit, policy and onboarding documentation, each linked to its source passage and routed to a reviewer.Document & Knowledge IntelligenceAI Architecture & Engineering
Risk reporting and analytical evidence
Measures with documented definitions, lineage and reconciliation controls behind management and risk reporting.Data & Risk Analytics
Controlled enterprise knowledge retrieval
Assistants that answer from approved sources, cite them and operate within restricted actions.AI Architecture & EngineeringOntology & Knowledge Engineering
AI governance and model-evaluation interfaces
Evaluation evidence, ownership and review points designed to fit the institution’s existing approval processes.Enterprise AI Transformation & IndustrialisationAI Capability, Maturity & Target Operating Model

03Context

Regulatory context, stated carefully

Depending on the use and jurisdiction, an AI-assisted workflow may sit within a bank’s model-risk management, operational-resilience, data-aggregation and reporting, or AI-specific obligations. Whether and how any framework applies is for the institution and its advisers to determine.

ARVEC’s contribution is architectural: designing systems so that the evidence, definitions, controls and decision records such reviews need are available. It is not legal advice, and it does not imply regulatory approval.

04Prior experience

Selected experience of our founders. Prior professional experience of ARVEC’s founders, before ARVEC AI’s incorporation. Not ARVEC client engagements; organisation and product names omitted.

Enterprise AI capability assessment

Banking · Prior independent architecture assignment

Designed and executed an enterprise-wide AI capability assessment spanning technical, product, governance, risk and business roles. Combined structured scoring, capability heatmaps and gap analysis to inform investment priorities and operating-model recommendations.

Prior experience of Utpal Kant

Banking reporting and analytics

Banking · Prior data analytics roles

Contributed to reporting automation, ETL pipeline development, dashboard creation and improvement, and KPI analysis and monitoring. Responsibilities also included managing data-product requests to support business reporting needs.

Prior experience of Akanchha Choudhary

05Related insights

Banking & Financial Services

Which decision would you need to defend?

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