Grounded Financial Assistant Evaluation

Connect employment, housing and household finance to prototype assistants that explain recorded circumstances.

The record concepts include income / financial context, dated financial changes, affordability signals, employment spells, employer / work transitions, earnings context and household composition. Review dated household income and employment records. Inspect financial changes alongside tenure and housing context. Keep unavailable financial information outside factual claims.

This dataset is prepared to order. Choose the city, date range and required scope; the accepted order defines the population, final fields, formats and delivery.

Onboarded city options

Nairobi (Kenya) · Lagos (Nigeria)

Custom city requests

African Major Cities · Additional cities require availability, scope and quote confirmation.

Data type

Synthetic

Connected domains

Finance, Employment, Households, Housing

Catalogue structure

Connected longitudinal

Key features

Record concepts for your order-specific extract.

Who it is for

• Fintech AI teams
• model-risk teams
• evaluators
• research labs

Entities represented

Resident + linked events

This identifies the entities described by the catalogue. The analytical row grain, eligibility, keys and dates are agreed for your extract.

Explore evidence-grounded explanations of changing income, work and housing costs.

Record concepts for your extract

• income / financial context
• dated financial changes
• affordability signals
• employment spells
• employer / work transitions
• earnings context
• household composition

The agreed specification defines the final columns, keys, relationships and observation period for your order.

Score monetary and date accuracy, relevant evidence coverage and unsupported inferences against reference facts; keep explanations within the supplied financial record.

Proposed questions for your custom order

1. Can an AI explain changes in a household's recorded income, employment and housing costs using dated evidence while clearly identifying missing financial information?
2. Can an assistant summarize income and the available financial context from the supplied records?
3. Can an AI assistant connect an employment change to the correct financial timeline?
4. Does an AI assistant avoid inventing balances, eligibility or lending decisions?
5. Can an AI assistant clearly say when a requested calculation lacks the necessary inputs?

Proposed research and analytical applications

• Task prototyping
• Evidence tracing
• Error analysis
• Controlled evaluation

Study and modeling guidance

Information available at prediction or evaluation time

Use only evidence valid and available at the agreed anchor; do not use later outcomes as features.

Comparison and evaluation

Specify comparable cohorts or held-out evaluation before reporting results.

Incomplete observation

Agree follow-up and retain incomplete observation as censored or unknown.

Data generation

FCTE synthetic city engine

Data classification

Synthetic

FCTE generation methods and published city/domain checks provide source context. The order defines the final extract and the release-specific checks, documentation and limitations supplied with it.

Known limitations

• Synthetic records describe generated scenarios; they do not establish observed population prevalence or real-world causal effects.

Catalogue base price

$695 USD

Price basis

Base price per selected city

Availability

Prepared after order

The displayed catalogue amount is a base price per selected city. Your requested dates, population, fields, formats and usage rights determine the agreed scope and final quote. Applicable published discounts and taxes are confirmed with that quote.

Your prepared package

Your order confirms the data files, documentation, evidence, examples and release controls included in the prepared package.

• Agreed data extract
• Schema and field documentation
• Release-specific evidence and limitations
• Agreed loading or analysis examples
• Licence, manifest, checksums and delivery note

SD_RESEARCH_DATASET/
├── data/
│   ├── finance_records.parquet
│   ├── employment_records.parquet
│   ├── household_history.parquet
│   └── housing_records.parquet
├── tasks/
│   ├── task_definition.yaml
│   ├── questions.jsonl
│   ├── reference_answers.jsonl
│   └── evidence_paths.jsonl
├── evaluation/
│   ├── baseline_model.py
│   ├── metrics.json
│   └── example_notebook.ipynb
├── documentation/
│   ├── README.md
│   ├── DATA_DICTIONARY.csv
│   ├── SCHEMA.md
│   └── METHODOLOGY.md
├── evidence/
│   ├── CERTIFICATION.json
│   ├── VALIDATION_REPORT.pdf
│   └── KNOWN_LIMITATIONS.md
├── MANIFEST.json
├── CHECKSUMS.sha256
└── LICENSE.pdf

Delivery process

Confirm the cities, date range, records, formats, rights and price in writing. The order-specific extract is then prepared and supplied under the agreed payment and delivery conditions.

One secure download package for the agreed release and licensed entity.

Delivery timing, access expiry, formats and prerequisites are confirmed in the accepted order.

Support, refreshes, future releases, hosting and APIs are included only when expressly ordered.

Data Licensing Agreement · 2026-10

A non-exclusive, non-transferable licence for the named customer within the accepted scope and duration.

Permitted use

Internal research, development, testing and analytics within the accepted order.

Model training

Model training, external applications, commercial distribution and access by other legal entities must be expressly addressed in the order.

Redistribution

Raw or substantially reconstructable data cannot be resold, published or redistributed without separate written authorization.

Attribution

Preserve release provenance and applicable third-party notices. Published outputs must identify the data as synthetic.