Industry example · Financial services and superannuation

Apply self-documenting compliance to a complex regulated industry.

This page is one industry example of Brad's broader approach. He works with financial services leaders and Australian superannuation funds to translate regulatory obligations into measurable data and AI controls, then move evidence from manual collection toward an automated, self-documenting operating model.

See the operating model See the cross-industry approach

The shift

FromPoint-in-time evidence

ToContinuous, traceable evidence

Automation supports evidence and control monitoring. Accountability, judgement and independent assurance stay explicit.

Regulatory traceabilityObligation to control to evidence

Critical dataOwnership, quality and lineage

Responsible AIInventory, risk tiering and monitoring

Board assuranceLive measures, evidence and accountability

Why this matters

Regulators increasingly expect evidence of control, not a statement that control exists.

Superannuation funds operate across member administration, insurance, investment, advice, digital service, cyber security and material service providers. The same member or regulatory outcome often depends on data moving across several of those domains.

A manual compliance model reconstructs ownership, lineage, control execution and evidence after the event. A governed data and AI model keeps those connections current as work happens.

The objective is not to hand compliance to technology. The objective is to make technology produce reliable evidence while accountable people retain responsibility for decisions and outcomes.

Regulatory framework

Understand the weight of each obligation, then engineer the evidence.

Binding prudential standards, statutory regimes and supervisory guidance do different jobs. Treating them as one undifferentiated checklist weakens both governance and implementation.

Binding standardCPS 230

Operational Risk Management

Operational risk, critical operations, business continuity and service-provider risk. The data and AI response is to expose dependencies, controls, incidents, tolerances, provider relationships and recovery evidence.

Binding standardCPS 234

Information Security

Information-asset criticality, security capability, control effectiveness and incident response depend on complete inventories, classification, access evidence and control testing.

APRA guidanceCPG 235

Managing Data Risk

APRA guidance focused on data risk. Strong implementation connects ownership, quality, lifecycle, security, metadata and reporting evidence rather than treating data controls as separate projects.

Binding standardSPS 220

Risk Management

RSE licensees need a risk management framework aligned to business operations and material risks. Data quality, issues and control results should feed risk reporting from governed operational records.

Binding standardSPS 515

Strategic Planning & Member Outcomes

Member-outcome strategy and performance assessment depend on trusted member, contribution, product, investment, service and cohort data that management and boards can trace.

Accountability regimeFAR

Financial Accountability Regime

Accountability maps should connect to operational ownership. Data and AI governance should make decisions, delegations, control ownership and evidence traceable to accountable people.

Supervisory expectationAPRA AI

AI Risk & Governance

APRA's April 2026 industry letter calls for stronger board understanding, lifecycle oversight, assurance, supplier risk management and operational resilience as AI adoption grows.

Privacy obligationPrivacy Act

Automated Decision Transparency

From 10 December 2026, relevant APP entities have additional privacy-policy transparency obligations for automated decision-making that significantly affects rights or interests.

Regulatory interpretation sits with each organisation's legal, risk, compliance and privacy functions. Brad's focus is translating agreed obligations and control objectives into data, technology, operating controls and evidence.

Self-documenting compliance

Maintain the evidence chain as work happens.

For each material obligation, keep a current connection between the requirement, accountable owner, policy, process, critical data, source, transformation, control, quality result, issue, approval and retained evidence.

ObligationOwnerPolicyProcessCritical dataSourceTransformationControlQuality resultIssueApprovalEvidence

Manual regime

Reconstruct evidence when someone asks.

Spreadsheet control registers
Evidence scattered across email, tickets and documents
Repeated attestations and duplicate requests
Lineage reconstructed for reviews
Issues discovered late

Self-documenting regime

Evidence exists because the control operated.

Obligations mapped to controls and owners
Data and AI assets linked to accountable domains
Automated quality, access and security results retained
Lineage and change history preserved
Board, audit and regulator evidence assembled from live records

Foundational capabilities

Eight capabilities form one operating system for trusted data, governed AI and evidence.

Data governance & catalogue

Definitions, ownership, policies, critical data elements and control evidence.

Master data management

Trusted records for members, employers, suppliers and products.

Reference data management

Controlled codes, classifications, product and investment reference values.

Data quality management

Preventative and detective controls, thresholds, remediation and scorecards.

Metadata, lineage & traceability

Source-to-report visibility for regulatory reporting, board measures and member decisions.

Privacy & information lifecycle

Purpose, consent, access, retention, disposal and breach-management evidence.

Responsible AI governance

Inventory, risk classification, testing, human oversight, monitoring and supplier assurance.

Regulatory evidence management

Reusable evidence linking obligations, controls, decisions, issues and approvals.

Operating model

Keep accountability with the business. Share standards, platforms and evidence.

Board and committees

Approve risk appetite, oversee data and AI risk, receive scorecards and require independent assurance.

Accountable executives

Own outcomes in their areas, with data and AI accountabilities consistent with FAR accountability arrangements.

Chief Data Officer function

Sets enterprise standards, runs shared platforms and services, provides assurance and reports performance.

Business and data domains

Own data, definitions, controls, quality results, risks and remediation because they own the processes that create them.

Risk, compliance, privacy, cyber, legal and audit

Provide independent challenge and assurance over design and operating effectiveness.

Material service providers

Process data and provide services, while the regulated entity retains accountability and needs suitable access, assurance and exit rights.

Board assurance

Measure implementation, control and business outcomes separately.

Start with verified baselines. Set targets through risk appetite and business cases. Report RAG status and trend, then schedule independent assurance rather than waiting for an incident.

Strategic delivery

Roadmap milestones delivered with agreed outcomes.

Data accountability

Critical domains with executive owners, stewards, definitions and control responsibilities.

Critical data quality

Critical data meeting completeness, accuracy, validity, timeliness and reconciliation thresholds.

Regulatory traceability

Material reports, board measures and member decisions with verified lineage and evidence.

Data risk remediation

Material data, privacy, lineage and control issues resolved within approved timeframes.

Responsible AI compliance

Production AI registered, risk assessed, approved, tested, overseen and monitored.

Member & operational outcomes

Measures tied to member journeys, service, claims, complaints, retirement engagement and operational efficiency.

Value realisation

Financial and non-financial benefits delivered, including avoided manual regulatory effort.

Primary regulatory sources

Built from the regulator's own material.

APRA CPS 230 Operational Risk Management ↗ APRA CPS 234 Information Security ↗ APRA CPG 235 Managing Data Risk ↗ APRA SPS 220 Risk Management ↗ APRA SPS 515 Strategic Planning and Member Outcomes ↗ APRA Financial Accountability Regime ↗ APRA Letter to Industry on Artificial Intelligence, 30 April 2026 ↗ OAIC automated decision-making transparency guidance consultation ↗

Board, CDO and executive sessions

Move your compliance model from manual evidence collection toward continuous, self-documenting assurance.

Executive briefings, board sessions and workshops for financial services and Australian superannuation leaders focused on data, AI, regulatory traceability and operating controls.

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