GOV 043640 Copy of emails.pdf

1
GOV-043640 – Received on 9 October 2025
Kia ora koutou,

Under the **Official Information Act 1982**, I request machine-readable information
about ACC’s **expenditure on external legal services** used to defend, manage or
represent ACC in dispute processes (e.g., **ICRA, Fairway, District Court/High Court**
matters) for the ﬁve most recent ﬁnancial years **2020/21–2024/25**.

This request focuses on information **held in ACC’s ﬁnance/ERP systems** and
related procurement records. It is made in the **public interest** to improve
transparency around representation in claimant disputes and cost-effectiveness of
external legal use. See ACC’s reported claims-handling costs ($**662m** in 2023/24)
and the “consulting and other professional services” line item, which indicate relevant
categories exist within ACC’s accounts.

Part A — Finance/ERP data extract (CSV under s 16)

Please provide a **CSV export** from ACC’s ﬁnance/ERP for the period **2020/21–
2024/25** containing **al payments to external legal suppliers** (law
ﬁrms/barristers/advocates) for dispute, review or litigation work, ﬁltered by the **GL
account(s)** or **spend categories** ACC uses for legal services and/or
litigation/appeals/reviews.

**Fields requested (where held):**

* Supplier legal name; NZBN (or other supplier ID)
* Invoice number; invoice date; payment date
* Line description / matter reference (with personal identiﬁers redacted if required)
* Amount (NZD ex-GST), GST, gross
* **GL Account code & description; Cost Centre; Project/Work Order; Business Unit**
* Any **matter type** or tag used (e.g., ICRA, Fairway, District Court, High Court,
Tribunal)
* (If held) **Claim number** (I accept redaction to protect third-party privacy;
aggregated totals per supplier/matter type suffice)

Format: Please provide as **CSV** (s 16 OIA). If multiple GL codes are used for legal
services, include **al relevant codes** (see Part B).

Why this is held: ACC has previously released **ERP extracts of supplier payments**
via FYI (showing supplier names/amounts — including *McCaw Lewis Limited*)
demonstrating such data can be exported without per-ﬁle col ation. ([FYI][1])

Part B — Chart of accounts & coding used for legal spend

Please provide:

1. A list of **GL Account codes/descriptions** used for **external legal services**
(including litigation/review/appeal costs) during **2020/21–2024/25**.
2. Any **Cost Centres/Programs/Projects** commonly used to record spend on
**ICRA, Fairway, or court appeals**.
3. Any **data dictionary** or ﬁeld deﬁnitions needed to interpret the Part A CSV.

Part C — Panel/contracting arrangements and supplier list

Please provide:

1. The **current list of panel or approved external legal suppliers** engaged to act for
ACC in reviews/appeals/disputes, and the **date ranges** of any panel or contract
arrangements (including when ACC established its own external legal panel for AC Act
litigation). ([FYI][2])
2. Any **standard engagement terms** or **service level expectations** for these
suppliers.
3. For **2020/21–2024/25**, a **summary by supplier** of **annual total paid** (fees
+ disbursements + GST) for dispute/litigation matters only. (If producing this summary
is onerous, please provide it **by the top 20 suppliers by value** for each year.)

Part D — Breakdown by forum / matter type (aggregates)

For 2020/21–2024/25, please provide **aggregated totals** (fees + disbursements +
GST) by:

Forum: ICRA, Fairway, District Court, High Court, other tribunals
Business area: (e.g., PIC/impairment, cover, treatment injury, AEP/NZDF-transferred
claims)
Outcome stage: mediation/settlement; hearing; appeal (if tagged in your system)

If not held as a canned report, please produce **one-off aggregated tables** based on
the same ERP/GL ﬁlters used in Part A (s 17 al ows reasonable extraction).

Part E — Narrowing options (to avoid s 18(f))

If my scope is assessed as potential y requiring “substantial col ation,” please **apply
s 13 duty to assist** and proceed with any of the fol owing **narrower options without
delay**:

1. Limit Part A to the **GL code(s)** explicitly titled **“Legal services” / “Litigation” /
“Reviews & Appeals”**;
2. Limit suppliers to a **panel list** or to the **top 20 legal suppliers by value** each
year;
3. Limit to **forums** (ICRA/Fairway/courts) rather than claim types; or
4. Provide **2023/24 and 2024/25** ﬁrst, with earlier years to fol ow.

Partial release is requested where possible.

Part F — My claim (context only)

For clarity, I have an **ICRA hearing on 16 October 2025** (Claim **#10038184163**).
While this OIA targets system-level data, please note I will seek **claim-speciﬁc
invoices/time entries** under the **Privacy Act 2020**. (If easier for ACC, you may
provide my claim’s **aggregate external-legal total to date** in this OIA, with personal
identiﬁers redacted.)

Administration

Format: CSV (Part A), PDF or CSV (Parts B–D).
Timeframe: Standard 20 working days; if an **extension** is needed, please advise
under s 15A and provide **rol ing/partial release**.
Withholdings: If any information is withheld, please **cite the exact subsection** and
explain the public-interest test (s 9(1)).
Charges: Please consider **waiver** due to public interest and reuse on FYI.

Kind regards,
Spencer Gerwyn Jones
2
GOV-043638 Received on 9 October 2025
Tēnā koutou,

Under the **Official Information Act 1982**, I request detailed information regarding
the Accident Compensation Corporation’s (ACC) **use of external legal counsel** –
particularly **McCaw Lewis Law** – for representation in **Independent Complaints
and Review Authority (ICRA)**, **Fairway Resolution Services**, and related
**Permanent Impairment Compensation (PIC)** hearings and mediations.

This request arises in connection with my own ongoing ICRA matter (**Claim
#10038184163**, hearing scheduled 16 October 2025), and also seeks broader
transparency on ACC’s systemic engagement practices and expenditure.

1. Engagement Details – Claim #10038184163

Please provide:
a. Copies of al  correspondence, instructions, contracts, or engagement
documentation between ACC and **McCaw Lewis Law** (including Afshan Afzaly and
Susannah Shaw) relating to their representation of ACC in my ICRA hearing.
b. Internal memoranda, emails, or brieﬁng notes recording the **decision rationale**
for engaging external counsel in this claim (e.g., internal workload constraints, conﬂict
of interest, precedent risk).
c. **Total costings** to date for McCaw Lewis’s services in this claim, including legal
fees, disbursements, and GST, separated into categories (preparation, mediation
attendance, advisory work, etc.).
d. Any **post-hearing summaries, invoices, or outcome reports** submitted by
McCaw Lewis to ACC in relation to this case.

*(I understand redaction of third-party personal identiﬁers under s 9(2)(a) is
permissible.)*

2. Policy, Guidance, and Criteria

Please release:
a. Al  current **policies, procedural instructions, or internal guidelines** governing
when and how ACC engages external law ﬁrms (including McCaw Lewis, Meredith
Connel , or Buddle Findlay) for:
  • Review hearings (Fairway / ICRA)
  • District Court or higher appeals
  • Disputes involving Permanent Impairment Compensation, medical-incapacity

termination, or AEP/NZDF-transferred claims.
b. Any **template engagement letters, terms of reference, or panel agreements** used
when instructing such ﬁrms.
c. The **roles or positions** of ACC officers authorised to approve these engagements
and any criteria, thresholds, or approval matrices used (e.g., claim complexity,
quantum, reputational risk).

3. Aggregated Expenditure and Case Data (2020 – 2025)

For the ﬁve ﬁnancial years 2020/21 – 2024/25, please provide:
a. The **annual number of cases** in which ACC engaged external legal counsel for:
  • ICRA or Fairway review hearings; and
  • PIC-related disputes (including 0 % WPI chal enges and ACC18 overrides).
b. The **total annual expenditure** on external legal counsel for those categories (fees
+ disbursements + GST).
c. The **average cost per case**, or, if unavailable in aggregate, an anonymised sample
of 5–10 recent PIC/ICRA cases showing ﬁrm engaged, dispute type, and cost band
(e.g., <$5 000, $5 000–$10 000, >$10 000).
d. Any internal or ministerial **brieﬁngs, audits, or ﬁnancial analyses** discussing the
cost-beneﬁt or proportionality of using external counsel for such hearings.

4. Oversight, Transparency, and Conﬂicts of Interest

Please release:
a. Any communications, reports, or advice between ACC and the Ministry for ACC, the
Ombudsman, or the Treasury concerning oversight of external legal counsel
engagements or concerns about independence of the review process.
b. Any internal guidance, declarations, or ethics documents dealing with **conﬂicts of
interest** or the **perceived independence** of ICRA/Fairway where ACC is
represented by a contracted law ﬁrm.

5. Public-Interest Considerations

This request is made in the **public interest** (OIA s 9(1)) to ensure transparency and
equity within New Zealand’s no-fault compensation system.

ACC’s 2024 Annual Report recorded approximately $662 mil ion in claims-handling
expenditure, and public reports (e.g., *Newsroom*, September 2024) have criticised
inconsistencies and access barriers in dispute resolution processes.

Disclosure of these costs and engagement practices wil  help assess whether ACC’s
legal-representation model upholds fairness for medical y impaired and self-
represented claimants.

6. Administrative Matters

Please provide the information electronical y where practicable.

If portions of this request require consultation or are subject to extension, please notify
me under **s 15A/15B**.

If ful  release is not possible, I request partial release with each withholding justiﬁed by
**speciﬁc statutory subsection**.

Under **s 28(1)(c)**, I request that any fees be waived on public-interest grounds.

I am happy to reﬁne the scope should that assist timely release.

Kind regards,
Spencer Jones
3
GOV-043667 Received on 10 October 2025
Pursuant to the Official Information Act 1982 (OIA), I request the fol owing information,
held by ACC, relating to the handling of claims by current and former New Zealand
Defence Force (NZDF) personnel (veterans) for service-related personal injuries under
the Accident Compensation Act 2001. This request focuses on systemic issues
identiﬁed in foundational reports such as the 1967 Woodhouse Report (principles of
community responsibility, comprehensive entitlement, complete rehabilitation, real
compensation, and administrative efficiency), the 1994 Trapski Report (adversarial
bias, expert selection ﬂaws, and imbalances for self-represented claimants), the 2015
Acclaim Otago Report, and the 2016 Dean Review, with particular emphasis on
coordination under the Veterans’ Affairs Approved Information Sharing Agreement
(AISA) effective 6 June 2024.

Please provide the information in electronic format (e.g., searchable PDF or Excel),
disaggregated by year (2020–2025 where possible), and anonymized to protect privacy
under the Privacy Act 2020. If any part requires transfer to another agency (e.g., VANZ
or NZDF), please notify me within 20 working days and provide a copy of the
transferred request.

1.  Policies and Guidelines:

•  Copies of al  internal policies, guidelines, or operational manuals (current and any
versions from 2020–2025) on processing veterans’ claims for service-related injuries
(e.g., PTSD, noise-induced hearing loss (NIHL), blast trauma, or physical injuries
sustained during or post-service), including criteria for recognizing “personal injury by
accident” under s20–26 of the Accident Compensation Act 2001 in a military context.
      •  Any guidance on applying Woodhouse principles (e.g., comprehensive
entitlement for delayed-onset injuries) to veterans’ claims, including references to
Trapski Report recommendations (e.g., independent expert monitoring, report-sharing
protocols).

2.  Expert Selection and Medical Assessments:

•  Data on the selection of medical experts or panels (e.g., Clinical Advisory Panels) for
veterans’ claims: number of assessments conducted annual y (2020–2025), list of
frequently used specialists (anonymized), and any complaints or overrides related to
perceived bias (e.g., “doctor shopping” as critiqued in Trapski, pp. 106–109).
      •  Copies of templates or instructions provided to experts for veterans’
assessments (e.g., ACC167 forms), including how they address military-speciﬁc
factors (e.g., service records from NZDF) and Whole Person Impairment (WPI) ratings
under Schedule 1.
      •  Statistics on WPI outcomes for veterans’ claims: average ratings awarded

(disaggregated by injury type, e.g., mental vs. physical), percentage of 0% ratings
appealed/overturned, and any internal reviews of low-rating patterns (2020–2025).

3.  Review and Appeal Outcomes:

•  Aggregated data on veterans’ review applications (via FairWay Resolution) and
appeals (District Court/High Court): number lodged annual y (2020–2025), success
rates (claimant wins), withdrawal rates, and reasons for declinature (e.g., causation,
evidence gaps). Disaggregate by self-represented vs. represented claimants, and by
injury type (e.g., PTSD/NIHL vs. physical trauma).
      •  Copies of any internal analyses or reports (2020–2025) on barriers for self-
represented veterans, including references to Acclaim Otago (2015) or Dean Review
(2016) ﬁndings (e.g., 30% success rate for self-reps vs. 50% with lawyers).

4.  VANZ Coordination and AISA Implementation:

•  Records of ACC-VANZ data-sharing under the 2024 AISA (e.g., number of veteran
claims transferred/shared, average processing time reductions, and any disputes over
eligibility mismatches, such as ACC declinatures later approved by VANZ).
      •  Any correspondence or reports (2020–2025) between ACC and VANZ/NZDF on
coordination issues for veterans’ claims, including handling of lost/incomplete service
records and top-up entitlements (e.g., 20% on weekly compensation).
      •  Statistics on dual ACC-VANZ claims: percentage where VANZ intervention led to
revised ACC decisions (under s64/65), and any audits of AISA effectiveness (e.g., post-
June 2024 implementation).

5.  Costs and Compensation Impacts:

•  Data on costs awards and compensation revisions for veterans’ claims (2020–2025):
average amounts granted in successful reviews/appeals, and any adjustments for
inﬂation (unchanged since 2008, per Dean Review).
      •  Internal evaluations of re-traumatization risks in veterans’ assessments (e.g.,
psychiatric exams), including compliance with UNCRPD obligations for disabled
veterans.
If withholding any information, please specify the grounds under s18 of the OIA and
consider partial release or consultation under s19. I request a response within 20
working days (by 7 November 2025). If extensions are needed, please notify me
promptly.

Thank you for your assistance in promoting transparency and fairness in ACC
processes for veterans.

Kind regards, Spencer Jones
4
GOV-043669 Received on 12 October 2025
Tēnā koutou,

Pursuant to section 5 of the Official Information Act 1982 (OIA), I request the release of
al  information held by the Accident Compensation Corporation (ACC) relating to the
acquisition, deployment, governance, evaluation, and planned expansion of artiﬁcial
intel igence (AI) tools and technologies across the entire organization. This request is
motivated by the public interest in transparency regarding ACC's adoption of AI,

particularly in light of the New Zealand Government's Public Service AI Framework
(released 29 January 2025) and the New Zealand AI Strategy (launched 8 July 2025),
which emphasize responsible, human-centered AI use to enhance public services
while safeguarding privacy, equity, and accountability.

As New Zealand's primary no-fault injury compensation provider, ACC's use of AI in
areas such as claims processing, customer service, risk assessment, and data
analytics has signiﬁcant implications for claimants' rights, operational efficiency, and
compliance with Te Tiriti o Waitangi principles. Recent disclosures from other
agencies, such as the Crown Law Office's OIA response dated 9 October 2025
(available
at https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Ffyi.org.nz%2
Frequest%2F32436&data=05%7C02%7Cgovernmentservices%40acc.co.nz%7C25728
9bff78b4c3e561708de092f17a0%7C8506768fa7d1475b901cfc1c222f496a%7C0%7C
0%7C638958295338002802%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOn
RydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3
D%7C60000%7C%7C%7C&sdata=%2FOhfIo%2FXd7BckgrDz8t%2FaNS6UDQ%2FnyF
84cj2icKVzP0%3D&reserved=0), highlight the need for comprehensive public oversight
of AI integration in government entities.

To ensure clarity and speciﬁcity, this request is structured into thematic sections with
numbered questions. Where applicable, please provide information in electronic
format (e.g., PDFs, spreadsheets, or datasets) for the period from 1 January 2020 to the
date of this request (12 October 2025), unless otherwise speciﬁed. If any part requires
clariﬁcation, please contact me promptly. I am happy to receive information in
summarized or aggregated form where it reduces volume without losing substantive
detail, and I consent to any reasonable charges for extensive copying or col ation,
provided they are notiﬁed in advance.

Please acknowledge receipt of this request within 5 working days and provide a
decision no later than 20 working days from receipt, as per sections 15 and 23 of the
OIA. If extensions are needed, please justify them under section 15A.

Section 1: Current AI Tools, Subscriptions, and Licenses
1. A list of al  AI tools, software, platforms, or services (including generative AI,
machine learning models, predictive analytics, natural language processing, computer
vision, or automation tools) for which ACC has purchased subscriptions, licenses, or
enterprise agreements since 1 January 2020, including:
   a. The ful  name and version of each tool;
   b. The vendor or provider (e.g., Microsoft, Google, OpenAI);
   c. The date of initial purchase, subscription start, or license activation;
   d. The number of licenses or user seats al ocated;
   e. Any integrations with existing ACC systems (e.g., claims management software,
CRM, or data warehouses).

2. For each tool listed in Question 1:
   a. The annual license or subscription cost (in NZD, including any GST);
   b. Any one-off setup, implementation, or customization fees paid;
   c. Breakdown of costs by category (e.g., per-user, enterprise-wide, cloud hosting);
   d. Total expenditure to date, including renewals and escalations.

3. Details of any free-tier, trial, or open-source AI tools in use at ACC since 1 January
2020, including:
   a. Names and purposes;
   b. Dates of adoption;
   c. Number of users or departments involved;
   d. Any associated indirect costs (e.g., staff time for setup).

4. Copies of al  contracts, terms of service, or service level agreements (SLAs) for the
top ﬁve most expensive or widely used AI tools identiﬁed in Question 1, redacting only
commercial y sensitive pricing if necessary under section 9(2)(i) of the OIA.

Section 2: Deployment and Usage Across Business Units and Departments
5. An organizational breakdown of AI tool usage across al  ACC business units,
branches, and departments (e.g., Claims, Customer Service, Rehabilitation, Strategy,
IT/Digital, Legal, Māori Strategy and Partnerships), including:
   a. Which tools are deployed in each unit;
   b. Number of active users per tool per unit (as of the latest available date);
   c. Primary use cases (e.g., claims triage, fraud detection, customer chatbots,
predictive modeling for recovery outcomes);
   d. Percentage of workﬂows or processes automated or augmented by AI in each unit.

6. Quantitative data on AI utilization since 1 January 2023, including:
   a. Total number of AI-generated outputs (e.g., decisions, reports, recommendations)
per tool per quarter;
   b. Metrics on adoption rates (e.g., % of staff trained vs. % actively using AI);
   c. Any dashboards, reports, or KPIs tracking AI performance across departments.

7. Examples of AI applications in sensitive areas, such as:
   a. Claims assessment or denial processes (e.g., automated eligibility scoring);
   b. Customer interactions (e.g., AI chatbots or virtual assistants);
   c. Risk proﬁling or fraud detection (e.g., anomaly detection models);
   d. Personalized rehabilitation plans (e.g., predictive analytics for recovery timelines).
Provide anonymized case studies or process ﬂows if ful  details are withheld.

8. Information on AI use in Māori-speciﬁc or cultural y responsive services, including:
   a. Tools adapted for te ao Māori principles (e.g., data sovereignty compliance);
   b. Involvement of Te Puni Kōkiri or iwi partners in AI design or evaluation;
   c. Any disparities in AI outcomes for Māori claimants (e.g., bias audits).

Section 3: Trials, Pilots, and Evaluations
9. A register of al  AI trials, pilots, or proof-of-concept projects conducted by ACC since
1 January 2020, including:
   a. Project name, objectives, and scope;
   b. Start and end dates;
   c. Participating departments and external partners;
   d. Tools or technologies tested;
   e. Total costs incurred (broken down by category).

10. For each trial/pilot in Question 9:
  a. Key performance indicators (KPIs) measured (e.g., accuracy, efficiency gains, error
rates);

  b. Results and outcomes (e.g., success metrics, lessons learned);
  c. Any evaluation reports, including internal reviews or third-party audits;
  d. Decisions on scaling up, discontinuation, or modiﬁcation.

11. Details of any failed or discontinued AI initiatives, including:
  a. Reasons for failure (e.g., technical issues, ethical concerns, cost overruns);
  b. Costs sunk and any remediation measures;
  c. Changes to policies resulting from these experiences.

12. Copies of al  internal memos, emails, or meeting minutes discussing AI trial
outcomes from the past 12 months, redacting personal information under section
9(2)(a) if necessary.

Section 4: Costs and Budgeting
13. A comprehensive breakdown of ACC's total expenditure on AI tools, infrastructure,
and related activities since 1 January 2020, categorized by:
  a. Software licenses and subscriptions;
  b. Hardware/cloud computing (e.g., GPU resources for model training);
  c. Consulting, development, or integration services;
  d. Training and upskil ing programs;
  e. Ongoing maintenance and support.

14. Budget al ocations for AI in the current and next ﬁnancial year (2025/26 and
2026/27), including:
  a. Line-item details from ACC's annual plan or ICT budget;
  b. Projected ROI or cost savings from AI deployments;
  c. Any funding sourced from external grants (e.g., MBIE AI innovation funds).

15. Cost-beneﬁt analyses conducted for major AI implementations (e.g., those
exceeding $50,000), including assumptions, methodologies, and outcomes.

Section 5: Policies, Governance, and Risk Management
16. Copies of ACC's internal AI policies, guidelines, or frameworks (e.g., AI ethics
charter, governance deck), including:
  a. Alignment with the Public Service AI Framework and OECD AI Principles;
  b. Processes for AI procurement approval and vendor selection;
  c. Requirements for human oversight in AI decisions (e.g., "human-in-the-loop"
protocols).

17. Details of AI governance structures at ACC, such as:
  a. Committees, working groups, or roles responsible for AI oversight (e.g., AI Ethics
Board);
  b. Reporting lines to executive leadership or the Board;
  c. Frequency of AI risk reviews or audits.

18. Risk management practices for AI, including:
  a. Assessments for bias, fairness, and discrimination (e.g., using Stats NZ's
Algorithm Impact Assessment tool);
  b. Privacy impact assessments under the Privacy Act 2020 for AI data processing;
  c. Incident logs of AI-related issues (e.g., hal ucinations, erroneous decisions) since
1 January 2023, anonymized.

19. Compliance reporting on AI use, such as:
  a. Submissions to the Government Chief Digital Office (GCDO) or MBIE on AI
maturity;
  b. Adherence to the Algorithm Charter for Aotearoa;
  c. Any ﬁndings from internal or external audits on AI risks.

Section 6: Training, Capability, and Workforce Impact
20. Programs for building AI capabilities at ACC, including:
  a. Training courses, workshops, or certiﬁcations provided to staff (e.g., prompt
engineering for GenAI);
  b. Number of staff trained per department since 1 January 2023;
  c. Budget al ocated to AI skil s development;
  d. Partnerships with external providers (e.g., universities, TechHub).

21. Impact of AI on ACC's workforce, such as:
  a. Job roles affected (e.g., automation of administrative tasks);
  b. Upskil ing or redeployment initiatives;
  c. Any col ective agreements or consultations with unions on AI-induced changes.

Section 7: Future Uses, Plans, and Strategic Integration
22. Strategic plans for AI expansion at ACC through 2030, including:
  a. Roadmap for new tool acquisitions or upgrades;
  b. Intended use cases (e.g., AI in predictive healthcare analytics);
  c. Projected budget and staffing needs.

23. Upcoming procurements or RFPs for AI technologies, including:
  a. Timelines and scopes;
  b. Evaluation criteria (e.g., ethical AI standards);
  c. Shortlisted vendors.

24. Integration plans with national initiatives, such as:
  a. Col aboration with other agencies (e.g., Health NZ on shared AI for injury data);
  b. Participation in GCDO's AI sandboxes or innovation chal enges.

Section 8: Data, Analytics, and Broader Impacts
25. Datasets or models developed or used by ACC for AI purposes, including:
  a. Descriptions of key datasets (e.g., claims history for training models);
  b. Data governance protocols (e.g., anonymization, Māori data sovereignty);
  c. Any open-sourcing or sharing of AI models.

26. Analytics on AI's impact on ACC outcomes, such as:
  a. Improvements in claims processing times or accuracy;
  b. Claimant satisfaction metrics related to AI interactions;
  c. Equity analyses (e.g., outcomes by demographics).

27. Any research, white papers, or studies commissioned by ACC on AI's role in injury
compensation, including executive summaries.

Kind regards,

Spencer Jones

5
GOV-043772 Received on 15 October 2025
Dear Accident Compensation Corporation,

Under the Official Information Act 1982, I request the fol owing information regarding
ACC’s handling of claims related to New Zealand veterans’ service-related injuries,
with emphasis on issues identiﬁed in the Paterson Report (2018), Acclaim Otago
Report (2015), Dean Review (2016), and the Veterans’ Advisory Board Interim Report
(2019):

1.  Veteran Claim Statistics (2020–2025):
      •  Annual number of claims submitted by or on behalf of NZDF veterans (current or
former) for service-related injuries or conditions.
      •  Breakdown by approval and denial rates, including common reasons for denials
(e.g., insufficient evidence, non-qualifying under Scheme Two of the Veterans’ Support
Act 2014).

2.  Impact of ACC Framework on Veterans (Paterson Report Implementation):
      •  Any internal assessments, policy updates, or cost analyses (2023–2025)
addressing the inﬂuence of ACC’s no-fault framework on Scheme Two entitlements, as
highlighted in Recommendation 63 of the Paterson Report (e.g., proposals to address
inequities for post-1974 veterans).
      •  Estimated number of veteran claims affected by eligibility barriers or overlaps
with Veterans’ Affairs New Zealand (VANZ).

3.  Coordination with VANZ and Dispute Resolution:
      •  Number of veteran claims involving referrals or transfers to/from VANZ (2023–
2025), including rates of disputes or appeals.
      •  Details of current data-sharing agreements, protocols, or identiﬁed gaps with
VANZ, including any improvements made in response to the 2019 Veterans’ Advisory
Board Report or Dean Review recommendations (e.g., expert selection, alternative
resolution options).

Please provide data in electronic format (e.g., Excel or CSV) where possible, with
sensitive personal information redacted. If any part is too broad, I am wil ing to reﬁne it.

This request supports community advocacy for veterans experiencing delays, denials,
and unmet needs in health and compensation due to systemic overlaps and resource
constraints.

Kind regards,
Spencer Jones