Kuvera Personal Finance Datasets and LLMs
Open personal-finance reasoning datasets and the models fine-tuned on them, built to give advice that accounts for both the numbers and the person asking.
Status: Maintained
What it is
Kuvera is a line of open datasets and models for personal-finance advice. The latest dataset, Kuvera PersonalFinance V2.1, holds 18,846 records, each pairing a real personal-finance question with a reasoning chain and a response.
Five models have been fine-tuned on this data across two generations, from 4B to 14B parameters. The method is written up in Synthesizing Behaviorally-Grounded Reasoning Chains, published at the FinNLP Workshop, EMNLP 2025.
Why it was needed
Financial AI benchmarks concentrate on corporate finance, trading and general financial knowledge. Personal-finance planning gets little attention, and so do the behavioural biases, such as loss aversion, overconfidence and anchoring, that shape real decisions about money.
Personal-finance questions also need reasoning rather than retrieval. A useful answer weighs income, debt, risk tolerance and time horizon against each other and applies general principles to one person's situation, instead of repeating generic advice.
What I built
- Sourced real questions from r/personalfinance, limited to posts made before Reddit's mid-2023 API and terms changes, and filtered them with an LLM jury (Gemini 2.0 Flash, Phi-4 14B and Gemma-3-27B) to keep only posts that pose a problem needing a reasoned answer.
- A multi-stage generation pipeline: query analysis and psychological-intent extraction (sentiment, emotions, communicative intent and certainty) run in parallel, then retrieval from a curated finance knowledge base of about 800k tokens, then response generation and validation.
- LLM juries check each critical step: the relevance of retrieved context, the completeness of the query analysis, and a 5-shot comparative selection of the final response using GPT-4o-mini, DeepSeek-V3 and Gemini 2.0 Flash.
- Two model generations: v0.1 (June 2025) fine-tuned a 14B LoRA on DeepSeek-R1-Distill-Qwen-14B and an 8B model on PersonalFinance_v2; v0.2 (July to August 2025) fine-tuned Gemma 3 4B and 12B and Qwen3-8B on the scaled-up V2.1 dataset.
What it achieved
- A fine-tuned Qwen3-8B matches much larger 14B to 32B baselines on factuality, fluency and personalisation while cutting cost by about 80%.
- Published at the FinNLP Workshop, EMNLP 2025.
- Kuvera PersonalFinance V2.1 released openly under Apache 2.0: 18,846 records across eight personal-finance domains, from debt and retirement to tax and estate planning.
Status
Maintained. I update the datasets and models irregularly as the generation pipeline improves.