Kuvera Personal Finance Datasets and LLMs

What it is

Kuvera is my line of open datasets and models for personal-finance advice. The latest dataset, Kuvera PersonalFinance V2.1, has 18,846 records. Each record pairs a real personal-finance question with a reasoning chain and a response.

I have fine-tuned five models on these datasets in two generations, from 4B to 14B parameters. I describe the method in my paper, Synthesizing Behaviorally-Grounded Reasoning Chains.

Why it was needed

Most financial AI benchmarks cover corporate finance, trading, and general financial knowledge. Personal-finance planning gets little attention. So do behavioral biases such as loss aversion, overconfidence, and anchoring, which shape real decisions about money.

Personal-finance questions also need reasoning, not retrieval. A useful answer weighs income, debt, risk tolerance, and time horizon against each other. It applies general principles to one person's situation instead of repeating generic advice.

What I built

• I took real questions from r/personalfinance, using only posts made before Reddit changed its API and terms in mid-2023. An LLM jury of Gemini 2.0 Flash, Phi-4 14B, and Gemma-3-27B filtered the posts. The jury kept only the posts that pose a problem and need a reasoned answer.
• The multi-stage generation pipeline starts with query analysis and psychological-intent extraction, which run in parallel. The intent step identifies sentiment, emotions, communicative intent, and certainty. Next, the pipeline retrieves context from a curated finance knowledge base of about 800k tokens. Last, it generates a response and validates it.
• LLM juries check the critical steps: the relevance of the retrieved context and the completeness of the query analysis. For the final response, a jury of GPT-4o-mini, DeepSeek-V3, and Gemini 2.0 Flash makes a 5-shot comparative selection.
• For v0.1, in June 2025, I fine-tuned a 14B LoRA on DeepSeek-R1-Distill-Qwen-14B and an 8B model on PersonalFinance_v2. For v0.2, from July to August 2025, I fine-tuned Gemma 3 4B, Gemma 3 12B, and Qwen3-8B on the larger V2.1 dataset.

What it achieved

• A fine-tuned Qwen3-8B matches 14B to 32B baselines on factuality, fluency, and personalization, at about 80% lower cost.
• The paper was published at the FinNLP Workshop, EMNLP 2025.
• I released Kuvera PersonalFinance V2.1 under Apache 2.0. It covers eight personal-finance domains, including debt, retirement, tax, and estate planning.

Status

I update the datasets and models when the generation pipeline improves, not on a fixed schedule.

Related

• Kuvera PersonalFinance V3 collection: models and dataset (opens in a new tab)
• PersonalFinance_v2: the earlier dataset (opens in a new tab)
• Kuvera 14B v0.1.0 (opens in a new tab)
• Essay: Creating a Reasoning Dataset with No Budget