CashWise

A Multilingual Financial Reasoning Model for South Africa 🇿🇦

CashWise is a finance-specialised language model fine-tuned from Meta Llama 3.3 70B Instruct using the RayNene/CashWise dataset.

The model targets a practical gap in financial AI: models may possess broad financial knowledge but struggle to turn that knowledge into clear, safe, context-aware guidance for users outside English-first financial environments.

CashWise combines large-model reasoning with multilingual financial adaptation across English, isiZulu, isiXhosa, Setswana, Xitsonga, and Afrikaans, covering investing, savings, credit, debt, insurance, tax, banking, retirement, fraud, mortgages, business finance, and equity.

Model

• Base model: Meta Llama 3.3 70B Instruct
• Fine-tuning dataset: RayNene/CashWise
• Domain: Financial reasoning and financial Q&A
• Languages: 6 South African languages
• Focus: Financial reasoning, safety, multilingual communication, and readability

Evaluation Protocols

CashWise was evaluated using a held-out evaluation set designed to measure whether domain adaptation improved financial performance without relying solely on training-set examples.

Evaluation was conducted across four dimensions:

1. Financial Reasoning

The model was tested on unseen financial questions covering:

• Investing
• Savings
• Credit and debt
• Insurance
• Tax
• Banking
• Retirement
• Business finance
• Equity and compensation

Responses were compared against expected answers using financial QA and reasoning evaluation criteria.

2. Financial Benchmark Evaluation

The evaluation protocol was aligned with established financial NLP benchmarks, including:

• FinQA — numerical and logical reasoning over financial information
• FinanceBench — open-book financial question answering
• FinBen — broader financial evaluation covering reasoning, QA, risk, and decision-making

The same evaluation methodology was applied to the base model and the fine-tuned CashWise model to measure the effect of domain adaptation.

3. Multilingual Evaluation

The model was evaluated across the six languages represented in the training data to assess:

• Semantic preservation
• Financial terminology
• Response relevance
• Natural language quality
• Cross-lingual consistency

Particular attention was given to whether financial concepts remained intact rather than being translated literally.

4. Readability & Safety

Generated responses were additionally evaluated for:

• Clarity
• Unnecessary financial jargon
• Appropriate uncertainty
• Risk disclosure
• Avoidance of guaranteed financial outcomes
• Distinction between general financial information and personalised advice

Results

Fine-tuning produced a substantial improvement over the underlying Llama 3.3 70B Instruct model.

Metric	Base Llama 3.3 70B	CashWise	Change
Financial evaluation win rate	<15%	87%	+72pp
Financial reasoning	Baseline	Improved	↑
Financial QA	Baseline	Improved	↑
Multilingual financial response	Baseline	Improved	↑
Readability	Baseline	Improved	↑

Financial Win Rate

The headline result is the increase in financial evaluation win rate from under 15% for the base model to 87% after CashWise fine-tuning.

This represents an improvement of more than 5× the original win rate and demonstrates that targeted financial training can substantially change the behaviour of a large general-purpose model on specialised financial tasks.

Readability

The underlying CashWise dataset also demonstrated a measurable improvement in response readability:

Response type	Score	Grade
Original / community responses	5.0	D
Adaptive responses	7.3	B

The 2.3-point improvement reflects the dataset's emphasis on converting financial explanations into clearer, more accessible responses.

Key Finding

The results suggest that domain-specific data can substantially improve the financial capabilities of a large general-purpose model without requiring training a foundation model from scratch.

CashWise therefore provides both:

1. A specialised dataset for multilingual financial reasoning.
2. A 70B-parameter model demonstrating the effect of targeted domain adaptation.

From <15% to 87% financial evaluation win rate.

Intended Use

CashWise can be used for:

• Financial QA and reasoning
• Multilingual financial assistants
• Financial NLP research
• African-language model evaluation
• Domain-specific LLM fine-tuning
• Research into financial accessibility and AI safety

Limitations

CashWise is a research model and is not a substitute for a licensed financial, tax, or investment professional.

Financial regulations, tax rules, products, and individual circumstances change. Model outputs should therefore be independently verified before being used for consequential financial decisions.

Dataset

The model was trained using:

RayNene/CashWise

CashWise is a multilingual financial Q&A dataset developed as part of the Adaption Labs AutoScientist Challenge.

Acknowledgements

Developed as part of the Adaption Labs AutoScientist Challenge, exploring how targeted datasets and domain adaptation can improve frontier language models on specialised tasks.

Citation

@misc{cashwise2026,
  title  = {CashWise: A Multilingual Financial Reasoning Model for South Africa},
  author = {Nene, Ray},
  year   = {2026},
  url    = {https://huggingface.co/RayNene/CashWise}
}