Synthetic Australian Financial Planning Datasets — Complete Guide (2026)

Synthetic data notice: This dataset is artificially generated for training and research. It does not contain real client information and is not personal financial advice. Use for model development only.

What Is Synthetic Financial Planning Data?

Synthetic financial planning data is artificially created to mirror real Australian advice scenarios without using real client records. Each record is a DatasetRecord with a payload (e.g. a client fact-find or SOA, ROA or CAR) and rich metadata (model, temp, s961B cues). It lets fintechs and AI labs train AU-specific models for discovery chat, risk profiling and SOA compliance without privacy risk or 12-month collection. For what good advice documents must contain, see our RG 175 guide and RG 90 example.

Unlike generic US datasets, AU synthetic must handle preservation age, concessional $32,500 and non-concessional $130,000 caps (from July 2026), HECS/HELP, Centrelink deeming (0.25% to $60,400 then 2.25%), Division 293/296, FHSS $15k, SMSF LRBA ban from 1 July 2024, and RG175 clear concise effective duties.

The Five Dataset Types — Detailed Schema

Each file is JSONL where one line = one record with a typed payload plus metadata.synthetic:true. No real PII. Not personal advice. For model training and research only.

1. Client profiles — who the advice is for

client_id, age, employment_status, annual_income, super_balance, other_assets, has_smsf, has_mortgage, mortgage_balance, family_situation, goals[2], risk_tolerance, advice_scope, preservation_age

Simulate diverse AU clients — HECS/HELP balances, SMSF $300k viability, transition-to-retirement, downsizer contributions, single parents, new migrants — for onboarding flows and super-cap testing. Risk tolerance spans conservative to high_growth; advice scope covers full, scaled and limited.

2. Discovery conversations — how advice starts

conversation_id, client_profile_id, turns[5 x speaker/text/intent], summary, compliance_notes

Five-turn adviser-client dialogues with intents for fact-find, risk profiling and compliance, plus s961B safe-harbour notes. Fine-tune discovery chatbots to handle FHSS $15k, LRBA ban, Division 296, deeming and fee disclosure. Intents train NLU.

3. Risk scenarios — suitability in numbers

scenario_id, client_age, stated_risk, capacity_for_loss, questionnaire_score 35-85, adviser_assessment, recommended_portfolio 30/70-90/10, conflicts

Train risk engines to spot mismatch — for example growth stated but low capacity for loss triggers scaled advice. Balanced across profiles for benchmarking.

4. SOA-style content — RG175 grounded advice text

soa_id, client_profile_id, scope, strategy, fee_disclosure $3,300/$4,700, risk_section, compliance_statement, body 200w with RG175 and s961B cues

Scope, strategy (salary sacrifice, debt recycling, TTR), fee disclosure in dollars, risk section and s961B/s961G compliance statement. Train SOA drafting and test strategy-vs-goals alignment and fee disclosure.

5. Compliance judgements — labels for your checker

judgement_id, record_ref to soa_id, s961b_compliant bool, s961g_compliant bool, fee_disclosure_ok bool, reasoning, label compliant/non_compliant/borderline

Direct classifier training for 6-layer logic — completeness, s961B, contradictions, s961G, language, fees. Current mix around 74% compliant / 18% non-compliant / 8% borderline gives balanced train and test.

All-Inclusive Bundle — rich_seed.jsonl

Interleaved 5 types linked by client_id — one bundle = 1 client → 1 conversation → 1 risk → 1 SOA → 1 judgement. A 25-bundle pack = 125 lines. Singles and bundle stay in sync and append to any size: Starter 500, Pro 2,000, Enterprise 10,000.

How We Generate s961B-Compliant Data

We use an AU prompt library covering s961B safe harbour, RG175 clear-concise-effective duties, super caps and contributions, SMSF, risk profiling, deeming, FHSS, Division 293/296 and more, with privacy-safe synthetic generation. No real client data is used at any stage, and the pipeline is isolated in a separate dataset folder so consumer trust and app data are untouched.

Sample & How to Load

Each line is JSONL — one record per line with payload and metadata. Truncated example:

{"record_type": "conversation", "payload": {"turns": [{"speaker": "adviser", "text": "Thanks for coming in..."}, {"speaker": "client", "text": "..."}]}, "metadata": {"synthetic": true}}

Load in Python or Hugging Face datasets — open('rich_seed.jsonl').readline() or load_dataset('json', data_files='rich_seed.jsonl'). Use client profiles to simulate onboarding, conversations to fine-tune chat, risk to test suitability, SOA text to train drafting, and judgements to evaluate your checker. Login to enquire for a sample extract.

Pricing — Singles and Bundle

One-off purchase. Synthetic only, for training and research — not personal advice. Login to enquire to lock pricing and receive a Stripe link.

Tier	Per-file size	Singles each	Bundle (5 files)
Starter	500 records	$299-$599	$999
Pro	2,000 records	$799-$1,499	$2,999
Enterprise	10,000 records	$2,499-$4,999	$9,999

Starter 500 is enough to fine-tune, Pro 2k for evaluation, Enterprise 10k for production. Bundle is around 40% cheaper per line than buying 5 singles. Enquire via Contact with Subject: Dataset Inquiry after login.

Who Buys — Use Cases by Segment

Open to any buyer on the same terms. Typical fits:

Segment	Use	Files to start	Tier fit
Fintech / platforms	Realistic conversations + risk fast for robos and advice apps	conversations, risk, soa	Pro / Bundle
Banks + super funds	In-house copilots and QA, CPS 234 / s912A / FAR without real SOAs	rich_seed full chain	Enterprise
Licensees / dealer groups	Internal QA, adviser productivity, file-review calibration	compliance, soa, conversations	Starter / Pro
Vertical AI / RegTech	s961B/G labels, fee disclosure, AU grounding	all 5 + bundle	Pro
Frontier labs	Preference + long-context regulated dialogues	conversations, compliance	Enterprise
Intl / secondary	Transferable AU-flavour sets, white-label pilots	sample to Bundle	Starter / Bundle

Who Is It For?

WealthTech and RegTech teams building AU-specific compliance checks, advice chat and risk tools. University researchers studying s961B best interests duty and RG175, and enterprise AI labs needing privacy-safe AU training data without collecting real SOAs. If your model must understand Australian super caps, Centrelink deeming, Division 296, FHSS and SMSF rules, synthetic gives you that grounding in days not months.

Banks and large financial planning firms with in-house models use the same datasets to fine-tune adviser copilots, paraplanner assistants and file-review QA, calibrate quality across advisers and licensees, train reviewers, and benchmark vendor tools — without exposing real client SOAs.

For In-House Model Teams at Banks and Licensees

For banks and AFSLs, real SOAs cannot be used for model training without creating privacy, confidentiality and accountability risk under APRA CPS 234 (information security), s912A general obligations (efficient, honest and fair services, compliance, risk management), and the Financial Accountability Regime (FAR) for accountable persons. Synthetic removes that blocker: 500–10k balanced compliant, borderline and non-compliant records, linked by client_id from fact-find through conversation and SOA to compliance judgement, ready for fine-tuning, regression testing and audit evidence.

Typical in-house uses: grounding internal copilots in s961B safe-harbour steps, RG175 clear-concise-effective duties and AFCA decision patterns; calibrating file reviewers across teams with shared edge cases (Division 296, deeming, LRBA ban, scaled vs full scope); onboarding paraplanners and advisers with realistic but fake files; and independently validating vendor AI before enterprise rollout. Available to any buyer — same singles and bundle, same synthetic-only, not-advice terms.

Why Synthetic Beats Real for AU Training

Real SOAs are protected by privacy and confidentiality obligations and cannot be shared; synthetic gives you 500–10k balanced compliant/non labels in days, not months, with no PII, no AFCA risk, and full control over edge cases (Division 296, deeming, LRBA ban). Your model learns AU law, not US 401k. You control the mix of scaled vs full advice, conservative vs high growth, and compliant vs non-compliant examples.

How to Use It

Load the JSONL into Python, Hugging Face datasets or your training pipeline — one line = one record. Use client profiles to simulate onboarding, conversations to fine-tune chat, and compliance labels to evaluate your checker. All records are synthetic, balanced and linked by client_id so you can trace a fact-find through to the SOA and the judgement.

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Frequently Asked Questions

Is this real client data?
No. 100% synthetic, artificially generated for AU scenarios. No real PII, not personal advice, for training and research only. Each record carries metadata.synthetic:true.

Can I buy one type or must I buy the bundle?
Both — 5 singles (for example just conversations) or all-inclusive bundle rich_seed.jsonl (5x size). Bundle is around 40% cheaper per line.

How many records do I need?
Starter 500 per file is enough to fine-tune, Pro 2k for evaluation, Enterprise 10k for production. Singles and bundle stay in sync and scale to any size.

Do I need to login to enquire?
Yes. Dataset enquiries go via Contact which requires Google login. Login first, then choose Subject: Dataset Inquiry (Synthetic Data). Without login you will be redirected to Login.

What are the exact prices?
Starter 500: singles $299-$599 each, bundle $999. Pro 2,000: singles $799-$1,499 each, bundle $2,999. Enterprise 10,000: singles $2,499-$4,999 each, bundle $9,999. Login to enquire to lock pricing.

How is it delivered and what licence?
JSONL delivery via secure link after Stripe payment. Licence is for model training and research only — not personal advice, no on-selling of raw data. Contact us for enterprise terms.

Will you add more variation?
Yes — generation uses varied AU scenarios covering super, SMSF, deeming, FHSS, Division 296, LRBA ban, scaled vs full scope and compliant vs borderline vs non-compliant mixes so each record is unique.

Can banks use this for in-house models without using real SOAs?
Yes — designed for that. Fine-tune copilots, QA checkers and training tools on synthetic s961B/RG175 cases with no client PII, supporting CPS 234, s912A and FAR accountability expectations. Same datasets, open to any buyer.

Does it cover s961B and RG175?
Yes. Conversations carry s961B safe-harbour notes, SOA content carries RG175 clear-concise-effective cues, and judgements carry s961B/s961G/fee booleans with reasoning.

Can I get a sample before buying?
Yes — login then send a Dataset Inquiry telling us your use-case and tier. We reply with a truncated sample extract.