Finance Ops Playbook

Why this playbook exists

Most finance automation fires rules and leaves reviewers to stitch together the story. In regulated environments, the questions are: Why was this flagged? Which policy or control applies? Who approved the outcome, and where's the evidence? This playbook answers those at the architecture level. We combine outcome-ranked retrieval (linking facts to policies and prior decisions), governed orchestration (segregation of duties, maker-checker, role-based access), and a learning memory that converts reviewer actions into signals-so signal quality, speed, and assurance improve together.

What it delivers

A governed, evidence-backed assistant that reduces manual review, cuts false positives, and improves audit readiness-without weakening control. Decisions and recommendations are grounded in policy/control references and linked to source documents. Segregation of duties and maker-checker ensure critical steps are always reviewed. Reviewer approvals, edits, and reversals feed the learning loop, improving triage and retrieval over time. Exportable evidence packs (what was retrieved, which control applied, who approved, and when). Positioning: Not just an alerting layer-this is a governed finance workflow that learns, built on The NeuralHue Framework.

How it works

A case (transaction anomaly, claim, or reconciliation break) lands in the console. The agent queries NeuralHue Knowledge Integration to retrieve anchored policies, prior rulings, and relevant documentation (invoices, bank lines, journal entries) with provenance. The Triage Assistant classifies the case, proposes a rationale, and suggests next steps (approve/decline/escalate) with explicit references to policy and past accepted outcomes. Before any action, Governance & Alignment enforces controls: high-risk or high-value cases route to maker-checker; obligations such as SoD and monetary thresholds are applied automatically; redaction protects sensitive fields by role. Reviewer decisions and notes are captured by the Memory & Feedback Engine, which improves retrieval ranking and case routing. Over time, the system focuses human attention where it matters and provides richer evidence for audit.

Core agents & workflows

Transaction Monitor
Detects anomalies and patterns, explains why a case is flagged (features, thresholds, matching failures), and links to source records.

Claims Triage Assistant
Segments claims, proposes outcomes with supporting artefacts (policy clauses, historic decisions), and routes edge cases to reviewers.

Policy & Control Checker
Applies internal control libraries (ICFR/SOX-aligned where applicable) to narratives and disclosures; tracks exceptions and prepares an exceptions register.

Reconciliation Copilot
Matches entries across bank feeds, GL, and subledgers; explains breaks; drafts resolution memos with citations to transactions and controls.

(All agents inherit memory, governance, and orchestration policies from The NeuralHue Framework.)

Governance, risk, and assurance

Governance is embedded, not bolted on: Segregation of duties and maker-checker for payouts, write-offs, control exceptions. Role-based access and field-level redaction for sensitive data (PII, salaries, supplier terms). Immutable audit trails: policies applied, evidence retrieved, thresholds, reviewers, and final decisions. Fairness & drift monitors for model thresholds and routing; alerts on bias or deteriorating precision. Evidence packs exportable for internal audit, external audit, and regulatory review.

Data & integrations

Systems: ERP/GL (SAP/Oracle/MS Dynamics), AP/AR, procurement, T&E, bank feeds, claims platforms, KYC/AML providers, data lakes/warehouses. Identity & security: SSO, SCIM, RBAC; data residency controls; VPC/on-prem when required. Models: OpenAI/Anthropic/Llama or local inference via Ollama; selection is policy-controlled. Events: Queues/bus (Kafka/SNS/SQS) for asynchronous case creation and updates.

Outcomes & indicative KPIs

Manual review effort: ≥30% reduction in reviewer minutes per case after 6–8 weeks of learning. False positives: ≥15% reduction through outcome-ranked triage and policy-aware routing. Reconciliation cycle time: 20–40% faster on targeted match types. SLA adherence: ≥95% with traceable evidence for each decision. (Targets are indicative; final acceptance criteria are set during pilot planning.)

90-day pilot plan

Weeks 1–3 - Foundations
Connect two systems (e.g., claims + ERP or bank feeds + GL). Ingest policy/control libraries. Define exception taxonomies and approval gates. Baseline metrics.

Weeks 4–6 - Review mode
Enable Transaction Monitor and Claims/Reconciliation triage in shadow mode. Capture reviewer outcomes and rationales. Publish first governance dashboards.

Weeks 7–10 - Policy & calibration
Turn on Policy & Control Checker with thresholds by risk tier. Calibrate routing, SoD, and maker-checker. Tune fairness/precision monitors.

Weeks 11–13 - Selective automation
Enable auto-approval for low-risk cohorts with strict rollback. Harden audit exports. KPI review with Finance/Controllership; go/no-go and scale plan.

Deliverables: anchored policy corpus, agent configs, exception taxonomy, governance dashboards, evidence pack templates, KPI report, rollout runbook.

Architecture (where this sits)

FAQs

Will our data leave our environment?
Not if you don't want it to. We support VPC/on-prem deployment with local models (Ollama); content need not egress.

Can we start retrieval-only?
Yes. Begin with Policy & Control Checker (evidence retrieval and exceptions register) and add triage/automation once governance is proven.

Does this replace existing GRC/controls tooling?
No. It complements GRC by adding explainable retrieval, learning triage, and stronger evidence capture; we integrate with your existing control libraries and workflows.