Are the Financial Services records on this page real?

No. Every record on this page is synthetic, generated from a fixed seed by the pack's own generator; masked values come from the real masking engine. The masking example uses a fixed public sample key; your own data is masked with a key from your secret store.

How long does a first certified Financial Services dataset take?

Estimates, not guarantees — try the synthetic sandbox: minutes; install (or reuse) the agent: under an hour; connect a source: under an hour; review and approve policies: under an hour; first certified dataset: minutes.

Which test scenarios does the Financial Services pack include?

11 runnable scenarios (everyday banking, failed payments, duplicate transactions, unusual sequences and more), plus 1 negative-test scenario kept apart from valid data.

Do Financial Services rows leave my network?

No. The DataNivra agent runs inside your environment: it reads the source, masks, subsets, generates and certifies there, and sends only metadata, aggregate counts and evidence to the DataNivra control plane (customer-resident processing, zero raw-production-data egress).

Industry packs

Financial Services industry pack

Account, card and statement test data that still reconciles.

Synthetic banking, cards, lending, payments and fraud test data with linked masking

Fully self-service (journey proven end to end) Version 2.1.0 · Available in paid plans

The full Financial Services 2.1.0 journey — from the public pack page through the synthetic sandbox, signup, activation, agent and source setup, scenario, masking and subsetting, certification and provisioning to evidence export — was verified end to end on a local real-component stack (the real control plane, agent, engine, sandbox runner, website and console built from source, with synthetic data) on 2026-10-09. Published agents 0.4.4, 0.4.3, 0.4.2, 0.4.1 and 0.4.0 run this pack version.

Try with synthetic data Activate this pack — start free

No call and no credit card needed. Prefer a conversation? Request a demo (optional). Missing something? Request a feature.

Included in Free Trial, Developer, Team, Business, Enterprise and Private Cloud / Self-Hosted. You can activate it for your organization yourself in the console; no call is needed.

Problems this pack solves

Masked data that still joins across 5 systems

9 cross-system relationships link cards, core_banking, fraud, lending and payments; the pack's masking keeps one pseudonym per identity on every side. For example, core_banking.customers.customer_id and cards.cards.customer_ref get the same pseudonym.

The cases production samples rarely contain

9 ready-made scenarios generate them on demand, for example: outgoing payments that fail or are returned, each with a failure reason; bursts of sub-unit probe transactions seconds apart followed by a large spend; accounts moving to frozen, dormant or closed with a recorded change time.

Sensitive fields found and masked before anyone sees them

34 columns across 10 entities are classified (credential, direct identifier, financial, payment, PII, quasi identifier and sensitive) and covered by 3 masking templates you review and approve.

Evidence that each dataset is fit to use

2 certification presets check masking coverage, referential integrity, orphans and row counts before a dataset can be provisioned; a failed dataset is never provisioned.

Entities and relationships

10 entities across 5 source systems, generated from the pack’s own entity model.

Arrows point from the referencing entity to the one it references. Solid: a foreign key inside one system. Dashed: a cross-system relationship — the pack keeps the same pseudonym on both sides, so masked data still joins.

All 13 relationships as a table

Relationships of the Financial Services pack
Entity	References	Columns	Kind
Address	Customer	`customer_id` → `customer_id`	Within a system
Account	Customer	`customer_id` → `customer_id`	Within a system
Beneficiary	Account	`account_id` → `account_id`	Within a system
Transaction	Merchant	`merchant_id` → `merchant_id`	Within a system
Card	Account	`account_ref` → `account_id`	Across systems (pack relationship template)
Card	Customer	`customer_ref` → `customer_id`	Across systems (pack relationship template)
Loan	Customer	`customer_ref` → `customer_id`	Across systems (pack relationship template)
Transaction	Account	`account_ref` → `account_id`	Across systems (pack relationship template)
Transaction	Card	`card_ref` → `card_id`	Across systems (pack relationship template)
Payment	Account	`account_ref` → `account_id`	Across systems (pack relationship template)
Payment	Beneficiary	`beneficiary_ref` → `beneficiary_id`	Across systems (pack relationship template)
FraudCase	Transaction	`transaction_ref` → `transaction_id`	Across systems (pack relationship template)
FraudCase	Customer	`customer_ref` → `customer_id`	Across systems (pack relationship template)

Realistic synthetic records

Synthetic data. Every record on this page is synthetic, generated from a fixed seed by the pack's own generator; masked values come from the real masking engine.

Entity

Customer — `core_banking.customers` (synthetic)
`customer_id` sensitive	`first_name` sensitive	`last_name` sensitive	`email` sensitive	`phone` sensitive	`birth_date` sensitive	`tax_id` sensitive	`segment`	`kyc_status`	`customer_since`
CUS00000001	Celeth	Ivermere	celeth.pemridge6056@example.com	555-056-1060	1962-12-15	TIN-ZZ-0145011	RETAIL	PENDING_REVIEW	2023-10-26
CUS00000002	Hesel	Zephholt	hesel.fairworth9409@example.org	555-009-8894	1951-07-30	TIN-ZZ-3915991	RETAIL	VERIFIED	2019-12-27

Masking: before and after

Template Linked banking test data (FS_LINKED_TEST_DATA) applied to a synthetic Customer record from core_banking.customers.

Synthetic record

Synthetic Customer record before and after masking
Column	Classified as	Before (synthetic)	After masking
`customer_id`	Direct identifier	`CUS00000001`	`CUS39407502`
`first_name`	PII, Direct identifier	`Celeth`	`Hesira`
`last_name`	PII, Direct identifier	`Ivermere`	`Eldbrook`
`email`	PII, Direct identifier	`celeth.pemridge6056@example.com`	`brael.oakbrook4399@example.com`
`phone`	PII, Direct identifier	`555-056-1060`	`555-020-6362`
`birth_date`	PII, Quasi identifier	`1962-12-15`	`1962-11-18`
`tax_id`	Direct identifier, PII	`TIN-ZZ-0145011`	`CUT-KL-9747159`

Same pseudonym in two systems. The identifier CUS00000001 appears in core_banking.customers.customer_id and in cards.cards.customer_ref. Both become CUS39407502, so the masked systems still join (relationship FS_CARD_CUSTOMER).

Masked with a fixed public sample key so this example is reproducible; your data is masked with your own key, referenced from your secret store.

Synthetic scenarios you can run

11 runnable scenarios. Run any of them in the hosted synthetic sandbox, or preview them in your browser without an account.

Everyday banking

Normal FS_EVERYDAY_BANKING

Ordinary customers, accounts, cards and activity

Everyday, valid records: the baseline most tests expect. Children per parent record: 1–3.

Run in sandbox: Everyday banking Preview in the browser demo: Everyday banking

Failed payments

Rare FS_FAILED_PAYMENTS

Outgoing payments that fail or are returned, each with a failure reason

Valid but uncommon business situations that production samples often miss. Children per parent record: 1–3.

Run in sandbox: Failed payments Preview in the browser demo: Failed payments

Duplicate transactions

Duplicate FS_DUPLICATE_TRANSACTIONS

Transactions re-submitted with identical content under new ids (double posting)

Records that repeat others under new keys (clean-up and matching tests). Children per parent record: 2–4.

Run in sandbox: Duplicate transactions Preview in the browser demo: Duplicate transactions

Unusual sequences

Rare FS_UNUSUAL_SEQUENCES

Bursts of sub-unit probe transactions seconds apart followed by a large spend

Valid but uncommon business situations that production samples often miss. Children per parent record: 5–10.

Run in sandbox: Unusual sequences Preview in the browser demo: Unusual sequences

Account status changes

Rare FS_ACCOUNT_STATUS_CHANGES

Accounts moving to frozen, dormant or closed with a recorded change time

Valid but uncommon business situations that production samples often miss. Children per parent record: 1–3.

Run in sandbox: Account status changes Preview in the browser demo: Account status changes

Chargebacks

Rare FS_CHARGEBACKS

Charged-back card transactions with chargeback dispute cases

Valid but uncommon business situations that production samples often miss. Children per parent record: 1–3.

Run in sandbox: Chargebacks Preview in the browser demo: Chargebacks

High value transactions

Boundary FS_HIGH_VALUE_TRANSACTIONS

Transactions at or above the high-value threshold (exactly at it for every tenth row)

Values at the edges of valid ranges (limits, thresholds, extremes). Children per parent record: 1–3.

Run in sandbox: High value transactions Preview in the browser demo: High value transactions

Loan delinquency

Rare FS_LOAN_DELINQUENCY

Loans in arrears: delinquent or defaulted, 30-180 days past due, balance outstanding

Valid but uncommon business situations that production samples often miss. Children per parent record: 1–2.

Run in sandbox: Loan delinquency Preview in the browser demo: Loan delinquency

Reconciliation breaks

Rare FS_RECONCILIATION_BREAKS

Outgoing payments and their ledger postings that do not agree: amount mismatches, postings a few days late, completed payments never posted and returns never reversed

Valid but uncommon business situations that production samples often miss. Children per parent record: 1.

Run in sandbox: Reconciliation breaks Preview in the browser demo: Reconciliation breaks

Balance boundaries

Boundary FS_BALANCE_BOUNDARIES

Checking balances exactly at zero, one cent either side of it, and at or one cent either side of the arranged overdraft limit

Values at the edges of valid ranges (limits, thresholds, extremes). Children per parent record: 1–3.

Run in sandbox: Balance boundaries Preview in the browser demo: Balance boundaries

Customer histories

Historical FS_CUSTOMER_HISTORIES

Multi-year, referentially intact customer/account histories without gaps

Old records for archive, migration and retention tests. Children per parent record: 2–4.

Run in sandbox: Customer histories Preview in the browser demo: Customer histories

Negative tests, kept apart. This scenario produces deliberately broken data for error handling and never passes certification as valid data:

FS_BROKEN_REFERENCES — Dangling cross-system references and invalid values for error-handling tests

Sample schemas and representative outputs

The schema the pack expects in each system (also as CREATE TABLE DDL in the downloads). A run produces a masked or synthetic dataset with the same tables, a certification report with the gates of the chosen preset, and an evidence manifest with checksums — the rows stay in your environment.

Customer — core_banking.customers · 10 columns

A retail or small-business banking customer (the natural subset root).

Column	Type	Required	Sensitive classes
`customer_id` (key)	`VARCHAR(12)`	Yes	Direct identifier
`first_name`	`VARCHAR(64)`	Yes	PII, Direct identifier
`last_name`	`VARCHAR(64)`	Yes	PII, Direct identifier
`email`	`VARCHAR(128)`	No	PII, Direct identifier
`phone`	`VARCHAR(32)`	No	PII, Direct identifier
`birth_date`	`DATE`	No	PII, Quasi identifier
`tax_id`	`VARCHAR(16)`	No	Direct identifier, PII
`segment`	`VARCHAR(16)`	Yes	—
`kyc_status`	`VARCHAR(16)`	Yes	—
`customer_since`	`DATE`	Yes	—

Address — core_banking.addresses · 8 columns

Postal addresses of a customer (residential, mailing).

Column	Type	Required	Sensitive classes
`location_id` (key)	`BIGINT`	Yes	—
`customer_id`	`VARCHAR(12)`	Yes	Direct identifier
`usage_type`	`VARCHAR(12)`	Yes	—
`street_address`	`VARCHAR(128)`	Yes	PII, Quasi identifier
`city`	`VARCHAR(64)`	Yes	Quasi identifier
`postal_code`	`VARCHAR(10)`	No	PII, Quasi identifier
`country_code`	`VARCHAR(2)`	Yes	—
`is_primary`	`BOOLEAN`	Yes	—

Account — core_banking.accounts · 11 columns

A deposit or credit account held by a customer.

Column	Type	Required	Sensitive classes
`account_id` (key)	`BIGINT`	Yes	—
`customer_id`	`VARCHAR(12)`	Yes	Direct identifier
`account_number`	`VARCHAR(12)`	Yes	Financial, Direct identifier
`routing_code`	`VARCHAR(12)`	Yes	Financial
`product_type`	`VARCHAR(16)`	Yes	—
`status`	`VARCHAR(12)`	Yes	—
`currency_code`	`VARCHAR(3)`	Yes	—
`available_balance`	`DECIMAL(14,2)`	Yes	Financial, Sensitive
`overdraft_limit`	`DECIMAL(14,2)`	Yes	Financial
`opened_on`	`DATE`	Yes	—
`status_changed_at`	`TIMESTAMP`	No	—

Beneficiary — core_banking.beneficiaries · 6 columns

A saved payee of an account (outgoing payments go to beneficiaries).

Column	Type	Required	Sensitive classes
`beneficiary_id` (key)	`BIGINT`	Yes	—
`account_id`	`BIGINT`	Yes	—
`beneficiary_name`	`VARCHAR(96)`	Yes	PII, Direct identifier
`beneficiary_iban`	`VARCHAR(34)`	Yes	Financial, Direct identifier
`beneficiary_bank_code`	`VARCHAR(11)`	Yes	Financial
`created_at`	`TIMESTAMP`	Yes	—

Card — cards.cards · 10 columns

A debit or credit card issued against an account.

Column	Type	Required	Sensitive classes
`card_id` (key)	`BIGINT`	Yes	—
`account_ref`	`BIGINT`	Yes	—
`customer_ref`	`VARCHAR(12)`	Yes	Direct identifier
`card_number`	`VARCHAR(19)`	Yes	Payment, Direct identifier
`cardholder_name`	`VARCHAR(96)`	Yes	PII, Direct identifier
`card_expiry`	`VARCHAR(5)`	Yes	Payment
`cvv`	`VARCHAR(4)`	No	Payment, Credential
`card_type`	`VARCHAR(8)`	Yes	—
`status`	`VARCHAR(12)`	Yes	—
`issued_on`	`DATE`	Yes	—

Loan — lending.loans · 11 columns

A consumer or small-business loan.

Column	Type	Required	Sensitive classes
`loan_id` (key)	`BIGINT`	Yes	—
`customer_ref`	`VARCHAR(12)`	Yes	Direct identifier
`loan_number`	`VARCHAR(14)`	Yes	Financial, Direct identifier
`product_type`	`VARCHAR(16)`	Yes	—
`principal`	`DECIMAL(14,2)`	Yes	Financial
`outstanding_balance`	`DECIMAL(14,2)`	Yes	Financial, Sensitive
`interest_rate`	`DOUBLE`	Yes	—
`status`	`VARCHAR(12)`	Yes	—
`days_past_due`	`INT`	Yes	—
`originated_on`	`DATE`	Yes	—
`maturity_on`	`DATE`	Yes	—

Merchant — payments.merchants · 5 columns

A card-accepting business (reference data; invented names and category codes).

Column	Type	Required	Sensitive classes
`merchant_id` (key)	`VARCHAR(12)`	Yes	Quasi identifier
`merchant_name`	`VARCHAR(64)`	Yes	—
`merchant_category_code`	`VARCHAR(4)`	Yes	—
`merchant_city`	`VARCHAR(64)`	Yes	Quasi identifier
`country_code`	`VARCHAR(2)`	Yes	—

Transaction — payments.transactions · 10 columns

A card or account transaction.

Column	Type	Required	Sensitive classes
`transaction_id` (key)	`BIGINT`	Yes	—
`account_ref`	`BIGINT`	Yes	—
`card_ref`	`BIGINT`	No	—
`merchant_id`	`VARCHAR(12)`	No	Quasi identifier
`amount`	`DECIMAL(14,2)`	Yes	—
`currency_code`	`VARCHAR(3)`	Yes	—
`transaction_type`	`VARCHAR(12)`	Yes	—
`status`	`VARCHAR(14)`	Yes	—
`channel`	`VARCHAR(12)`	Yes	—
`occurred_at`	`TIMESTAMP`	Yes	—

Payment — payments.payments · 9 columns

An outgoing credit transfer from an account to a beneficiary.

Column	Type	Required	Sensitive classes
`payment_id` (key)	`BIGINT`	Yes	—
`account_ref`	`BIGINT`	Yes	—
`beneficiary_ref`	`BIGINT`	No	—
`amount`	`DECIMAL(14,2)`	Yes	—
`currency_code`	`VARCHAR(3)`	Yes	—
`payment_rail`	`VARCHAR(12)`	Yes	—
`status`	`VARCHAR(12)`	Yes	—
`failure_reason`	`VARCHAR(32)`	No	—
`initiated_at`	`TIMESTAMP`	Yes	—

FraudCase — fraud.fraud_cases · 10 columns

A fraud investigation or chargeback dispute on a transaction.

Column	Type	Required	Sensitive classes
`case_id` (key)	`BIGINT`	Yes	—
`transaction_ref`	`BIGINT`	Yes	—
`customer_ref`	`VARCHAR(12)`	Yes	Direct identifier
`case_type`	`VARCHAR(20)`	Yes	—
`status`	`VARCHAR(12)`	Yes	—
`reason_code`	`VARCHAR(8)`	Yes	—
`disputed_amount`	`DECIMAL(14,2)`	Yes	Financial
`opened_at`	`TIMESTAMP`	Yes	—
`resolved_at`	`TIMESTAMP`	No	—
`analyst_notes`	`VARCHAR(400)`	No	Sensitive

Compatible connectors

Verified with this pack version: PostgreSQL and Local files (Parquet / CSV). 25 more connectors are compatible by capability: they support what the pack needs, but have not been verified with this pack yet.

Connector	Status with Financial Services
Local files (Parquet / CSV)	Verified with this pack
PostgreSQL	Verified with this pack
Amazon DynamoDB	Compatible by capability (not yet verified with this pack)
Amazon Redshift	Compatible by capability (not yet verified with this pack)
Amazon S3 / S3-compatible storage	Compatible by capability (not yet verified with this pack)
Apache Kafka	Compatible by capability (not yet verified with this pack)
Azure Blob Storage / Data Lake Storage	Compatible by capability (not yet verified with this pack)
Azure Cosmos DB	Compatible by capability (not yet verified with this pack)
Azure Synapse Analytics	Compatible by capability (not yet verified with this pack)
CockroachDB	Compatible by capability (not yet verified with this pack)
Databricks	Compatible by capability (not yet verified with this pack)
Generic SQL (SQLAlchemy)	Compatible by capability (not yet verified with this pack)
Google BigQuery	Compatible by capability (not yet verified with this pack)
HTTP APIs	Compatible by capability (not yet verified with this pack)
IBM Db2	Compatible by capability (not yet verified with this pack)
IBM Informix	Compatible by capability (not yet verified with this pack)
Mainframe files (EBCDIC / copybook)	Compatible by capability (not yet verified with this pack)
MariaDB	Compatible by capability (not yet verified with this pack)
Microsoft SQL Server	Compatible by capability (not yet verified with this pack)
MongoDB	Compatible by capability (not yet verified with this pack)
MySQL	Compatible by capability (not yet verified with this pack)
Oracle Database	Compatible by capability (not yet verified with this pack)
Parquet files	Compatible by capability (not yet verified with this pack)
Salesforce	Compatible by capability (not yet verified with this pack)
ServiceNow	Compatible by capability (not yet verified with this pack)
Snowflake	Compatible by capability (not yet verified with this pack)
SQLite (developer evaluation)	Compatible by capability (not yet verified with this pack)

Prerequisites and expected setup effort

You need

A DataNivra account; the Free Trial includes this pack.
One DataNivra agent inside your network (outbound HTTPS only) that ships Financial Services 2.1.0.
Read-only access to a compatible source (verified with this pack: PostgreSQL, Local files (Parquet / CSV)).
A non-production target environment the agent may write test data to.
A masking key in your own secret store, referenced as vault://…, azure-kv://… or env://… (DataNivra only ever sees the reference).
For production sources, a second person who approves policies (separation of duties).

A DataNivra agent that reports its installed industry packs (releases after agent 0.3.0); the control plane runs a pack job only on an agent holding the exact active pack version with the catalogued integrity digest.

Expected setup effort (estimates)

Step	Estimate
Try the synthetic sandbox No install: sign up and open the sandbox.	Minutes (estimate)
Install (or reuse) the agent One Docker command or a Helm chart; outbound HTTPS only.	Under an hour (estimate)
Connect a source Register a read-only source through the existing connector workflow.	Under an hour (estimate)
Review and approve policies Create drafts from the Financial Services templates, review and approve them.	Under an hour (estimate)
First certified dataset Run the first job; certification and evidence are produced automatically.	Minutes (estimate)

Certification presets in plain language

`FS_STRICT`

Every gate; full masking coverage; zero orphaned accounts, cards, payments or cases; exact row counts. Default for masked banking test data.

Masking coverage of at least 100% of sensitive columns
No orphaned child records
Empty-value ratio may rise by at most 5%
Row counts must match exactly

16 gates it requires

POLICY_COVERAGE: every sensitive column is covered by an approved policy
MASKING_COMPLETION: masking finished on every covered column
REFERENTIAL_INTEGRITY: every reference still points at an existing record
SCHEMA_VALIDATION: the output schema matches the source schema
DATA_QUALITY: empty-value ratios stay within the preset’s drift limit
ROW_COUNT_RECONCILIATION: row counts match the plan within the tolerance
ORPHAN_DETECTION: no child record lost its parent
PROVENANCE: every row is tagged masked or synthetic
MANIFEST: a manifest lists every output table with checksums
POLICY_VERSION: the exact approved policy versions are recorded
ENGINE_VERSION: the engine version is recorded
CHECKSUMS: output checksums are recorded for later verification
IDENTITY_CONSISTENCY: linked identifiers got the same pseudonym in every system
SOURCE_READ_ONLY: the source was only read, never written
EGRESS_GUARD: no row-level data left the agent
CONNECTOR_HEALTH: the connectors stayed healthy during the run

`FS_SCENARIO_TESTING`

Every gate, with slightly relaxed NULL-ratio drift for scenario datasets whose business states (failed payments, closed accounts) leave optional fields empty.

Masking coverage of at least 100% of sensitive columns
No orphaned child records
Empty-value ratio may rise by at most 15%
Row counts must match exactly

16 gates it requires

POLICY_COVERAGE: every sensitive column is covered by an approved policy
MASKING_COMPLETION: masking finished on every covered column
REFERENTIAL_INTEGRITY: every reference still points at an existing record
SCHEMA_VALIDATION: the output schema matches the source schema
DATA_QUALITY: empty-value ratios stay within the preset’s drift limit
ROW_COUNT_RECONCILIATION: row counts match the plan within the tolerance
ORPHAN_DETECTION: no child record lost its parent
PROVENANCE: every row is tagged masked or synthetic
MANIFEST: a manifest lists every output table with checksums
POLICY_VERSION: the exact approved policy versions are recorded
ENGINE_VERSION: the engine version is recorded
CHECKSUMS: output checksums are recorded for later verification
IDENTITY_CONSISTENCY: linked identifiers got the same pseudonym in every system
SOURCE_READ_ONLY: the source was only read, never written
EGRESS_GUARD: no row-level data left the agent
CONNECTOR_HEALTH: the connectors stayed healthy during the run

Activate it and create your first certified dataset

Seven steps in the console, each linked below. Nothing needs DataNivra staff.

Activate the pack for your organization
Activation checks your plan, the pack's integrity digest and its dependencies, then enables Financial Services 2.1.0.
Open pack activation in the console
Install or reuse your agent
One agent runs every pack you activate; it must report this pack version. Upgrade it if it does not.
Install the agent in the console
Connect a compatible source
Start with a connector verified with this pack: PostgreSQL, Local files (Parquet / CSV).
Connect a source in the console
Create policies from the pack templates
Create drafts from Linked banking test data, Unlinked analytics extract, Linked test data without card data; a second person approves them (separation of duties).
Create policies from templates in the console
Request your first dataset
Choose a scenario such as Everyday banking (FS_EVERYDAY_BANKING), the subset size and relationship closure.
Request a dataset in the console
Certify
The run is certified against a preset such as FS_STRICT; a failed dataset is never provisioned.
See certification evidence in the console
Provision and keep the evidence
Provision the certified dataset to a non-production target, download the evidence and schedule refreshes.
Provision the dataset in the console

Downloads

Version 2.1.0, 62 files (200.6 KB), all synthetic and generated from the pack itself. Every file’s SHA-256 is listed in MANIFEST.json.

Start here (2)

QUICKSTART.md 2.9 KB · SHA-256 fab081f86f2f…
TROUBLESHOOTING.md 4.7 KB · SHA-256 b23c4467f0bc…

Entity–relationship diagram (2)

er-diagram.mmd 4.4 KB · SHA-256 dfd9c55ce496…
er-diagram.svg 7.6 KB · SHA-256 085d8c79b0ac…

Sample schemas (6)

schema/cards.sql 921 B · SHA-256 aa53a9b7cbf6…
schema/core_banking.sql 3.0 KB · SHA-256 1b45303bd931…
schema/entities.json 28.4 KB · SHA-256 ced45ee5919a…
schema/fraud.sql 869 B · SHA-256 09723df22e5e…
schema/lending.sql 937 B · SHA-256 3508e6de2340…
schema/payments.sql 1.7 KB · SHA-256 2f3b83e8dbc8…

Policy templates (9)

policies/FS_ANALYTICS_UNLINKED.json 14.1 KB · SHA-256 c77806ded9bb…
policies/FS_CARD_DATA_MINIMISED.json 15.6 KB · SHA-256 5ed09e22969b…
policies/FS_CLASSIFICATION.json 202 B · SHA-256 7c879eabe4f4…
policies/FS_LINKED_TEST_DATA.json 15.7 KB · SHA-256 3deb3f2575ee…
policies/FS_REFRESH_WEEKLY.json 190 B · SHA-256 e54930bed540…
policies/FS_SCENARIO_TESTING.json 549 B · SHA-256 a0728ad1a443…
policies/FS_STRICT.json 549 B · SHA-256 df479db4f4bb…
policies/FS_SUBSET_DEFAULT.json 112 B · SHA-256 d46c49c10d7c…
policies/README.md 1.2 KB · SHA-256 2dfce1845cc4…

API, CLI, SDK and CI/CD examples (10)

api/curl.sh 2.6 KB · SHA-256 462355632b0c…
api/dataset-request.json 1.6 KB · SHA-256 dc8371e08399…
api/policy-approval.json 155 B · SHA-256 d55e632a5036…
api/policy-drafts.json 162 B · SHA-256 1e25229c4597…
api/provision.json 63 B · SHA-256 7f02a6a87792…
api/sandbox.json 46 B · SHA-256 0007572db27d…
ci/README.md 497 B · SHA-256 6d29da7a00f5…
ci/github-actions.yml 1.9 KB · SHA-256 159083ba7771…
cli.md 1.7 KB · SHA-256 d0d324a59409…
sdk_example.py 1.6 KB · SHA-256 8a5072318a64…

Synthetic sample data (CSV, JSON, Parquet) (31)

synthetic/SYNTHETIC_MANIFEST.json 2.3 KB · SHA-256 12554c489991…
synthetic/cards.cards.csv 1.0 KB · SHA-256 37e37b410070…
synthetic/cards.cards.json 3.2 KB · SHA-256 9cc6a884503d…
synthetic/cards.cards.parquet 3.5 KB · SHA-256 64cb038897e9…
synthetic/core_banking.accounts.csv 1.2 KB · SHA-256 18132413f644…
synthetic/core_banking.accounts.json 4.5 KB · SHA-256 5539155db284…
synthetic/core_banking.accounts.parquet 4.0 KB · SHA-256 7104979a2a9f…
synthetic/core_banking.addresses.csv 724 B · SHA-256 68e1db3396fa…
synthetic/core_banking.addresses.json 2.2 KB · SHA-256 957b838f4d68…
synthetic/core_banking.addresses.parquet 2.9 KB · SHA-256 c41f94661e66…
synthetic/core_banking.beneficiaries.csv 1.2 KB · SHA-256 16c7518202de…
synthetic/core_banking.beneficiaries.json 3.4 KB · SHA-256 f9aacfeaa47c…
synthetic/core_banking.beneficiaries.parquet 3.0 KB · SHA-256 c00dcd3e05e7…
synthetic/core_banking.customers.csv 783 B · SHA-256 9e61e01eebf7…
synthetic/core_banking.customers.json 1.8 KB · SHA-256 ab2cb69c8947…
synthetic/core_banking.customers.parquet 3.5 KB · SHA-256 51bdfefe637b…
synthetic/fraud.fraud_cases.csv 718 B · SHA-256 3fad20ae2d8e…
synthetic/fraud.fraud_cases.json 1.8 KB · SHA-256 1634d563f476…
synthetic/fraud.fraud_cases.parquet 3.8 KB · SHA-256 55a5fed64107…
synthetic/lending.loans.csv 253 B · SHA-256 af0c491d06d6…
synthetic/lending.loans.json 378 B · SHA-256 99f54cf9d83d…
synthetic/lending.loans.parquet 3.4 KB · SHA-256 c6c98dd7d35b…
synthetic/payments.merchants.csv 547 B · SHA-256 e3e1271faade…
synthetic/payments.merchants.json 1.7 KB · SHA-256 ca65477b4824…
synthetic/payments.merchants.parquet 2.1 KB · SHA-256 32eee0ccc1c7…
synthetic/payments.payments.csv 943 B · SHA-256 d1ae4ff6c3a3…
synthetic/payments.payments.json 3.2 KB · SHA-256 0742b0074862…
synthetic/payments.payments.parquet 3.3 KB · SHA-256 b20c47e9d670…
synthetic/payments.transactions.csv 3.9 KB · SHA-256 253f6678142f…
synthetic/payments.transactions.json 16.7 KB · SHA-256 0b09d62662c0…
synthetic/payments.transactions.parquet 4.5 KB · SHA-256 9c733c3cf8f8…

Troubleshooting

The reason codes you can meet on the way, with the recovery step. Every code is also in the error-code catalog.

SANDBOX_PACK_NOT_OFFERED — Synthetic estate not offered: A requested synthetic estate (industry pack) is not available in the hosted sandbox. What to do: Start the sandbox with the default estates.
ENTITLEMENT_REQUIRED — Plan does not include this: Your plan does not include this feature or industry pack. What to do: Upgrade in Billing & Plan.
PACK_INTEGRITY_UNVERIFIED — Pack integrity not verified: This pack version was registered without an integrity digest, so it cannot be activated (fail closed). What to do: Ask your operator to re-register the pack catalogue with the current control-plane image (register-pack), then activate again.
PACK_DEPENDENCY_INACTIVE — Required pack not active: This pack depends on another industry pack that is not active for your organization. What to do: Activate the packs this pack depends on first, then activate it again.
AGENT_PACK_MISSING — No agent can run this industry pack: The job's agent does not report the pack (older agents report no packs at all), so the job was not sent. What to do: Upgrade the agent to a release that reports its installed packs and ships this pack, then run the request again.
AGENT_PACK_VERSION_INCOMPATIBLE — Industry pack version differs on the agent: The agent holds a different version of the pack than the one the job was approved for. What to do: Deploy an agent with the pack's active version, or roll the pack back in Industry packs.
AGENT_PACK_INTEGRITY_MISMATCH — Agent pack differs from the catalogue: The agent reports the pack's version with a different integrity digest than the catalogued one, so the job was not sent. What to do: Redeploy the agent from the official signed image for this release, then run the request again.
PACK_TEMPLATES_UNAVAILABLE — Pack templates not registered: This pack version was registered without its policy templates. What to do: Ask your operator to re-register the pack catalogue (register-pack); create policies manually meanwhile.
PACK_TEMPLATE_KEY_REF_REQUIRED — Masking key reference required: A selected template keeps identities linked across systems and needs your masking key reference. What to do: Provide key_ref, e.g. vault://your-vault/tdm-masking-key, then create the drafts again.
PACK_NOT_ENABLED — Industry pack not enabled: The industry pack is not enabled for this tenant. What to do: Enable the pack (if your plan includes it).

Frequently asked questions

Are the Financial Services records on this page real?: No. Every record on this page is synthetic, generated from a fixed seed by the pack's own generator; masked values come from the real masking engine. The masking example uses a fixed public sample key; your own data is masked with a key from your secret store.
Can I activate the Financial Services pack myself?: Included in Free Trial, Developer, Team, Business, Enterprise and Private Cloud / Self-Hosted. You can activate it for your organization yourself in the console; no call is needed.
Which databases and files does the Financial Services pack work with?: Verified with this pack version: PostgreSQL and Local files (Parquet / CSV). 25 more connectors are compatible by capability: they support what the pack needs, but have not been verified with this pack yet.
How long does a first certified Financial Services dataset take?: Estimates, not guarantees — try the synthetic sandbox: minutes; install (or reuse) the agent: under an hour; connect a source: under an hour; review and approve policies: under an hour; first certified dataset: minutes.
Which test scenarios does the Financial Services pack include?: 11 runnable scenarios (everyday banking, failed payments, duplicate transactions, unusual sequences and more), plus 1 negative-test scenario kept apart from valid data.
Do Financial Services rows leave my network?: No. The DataNivra agent runs inside your environment: it reads the source, masks, subsets, generates and certifies there, and sends only metadata, aggregate counts and evidence to the DataNivra control plane (customer-resident processing, zero raw-production-data egress).

Learn the concepts, then come back to activate

Regulatory context

Supports PCI DSS–oriented and privacy programmes by keeping cardholder and customer data inside your environment. It does not certify any system for PCI DSS.

Try with synthetic data Activate this pack — start free

No call and no credit card needed. Prefer a conversation? Request a demo (optional). Missing something? Request a feature.

Problems this pack solves

Masked data that still joins across 5 systems

The cases production samples rarely contain

Sensitive fields found and masked before anyone sees them

Evidence that each dataset is fit to use

Entities and relationships

Realistic synthetic records

Masking: before and after

Synthetic scenarios you can run

Everyday banking

Failed payments

Duplicate transactions

Unusual sequences

Account status changes

Chargebacks

High value transactions

Loan delinquency

Reconciliation breaks

Balance boundaries

Customer histories

Sample schemas and representative outputs

Compatible connectors

Prerequisites and expected setup effort

You need

Expected setup effort (estimates)

Certification presets in plain language

FS_STRICT

FS_SCENARIO_TESTING

Activate it and create your first certified dataset

Activate the pack for your organization

Install or reuse your agent

Connect a compatible source

Create policies from the pack templates

Request your first dataset

Certify

Provision and keep the evidence

Downloads

Troubleshooting

Frequently asked questions

Learn the concepts, then come back to activate

Regulatory context

`FS_STRICT`

`FS_SCENARIO_TESTING`