FinRisk-BR — Brazilian Crypto Investor Risk Adapter

LoRA adapter for Brazilian crypto financial risk reasoning, fine-tuned via Adaption's AutoScientist platform.

Covers investor protection, fraud detection, suitability analysis and consumer risk explanation under BCB · CVM · COAF and federal legislation (Lei 14.478/2022, Decreto 11.563/2023).

The problem this adapter addresses

Generic LLMs reason about Brazilian financial regulations in the abstract — citing rules and authorities — but struggle to reason about financial harm: whether a product destroys investor capital, masks fraud behind legal language, mismatches a customer's risk profile, or exposes a retail investor to losses they cannot absorb.

This adapter teaches the model to go beyond compliance and reason about investor protection, fraud detection, suitability and consumer risk in the Brazilian crypto and investment market.

Model details

Parameter	Value
Base model	`meta-llama/Llama-3.3-70B-Instruct` (70B)
Trained model name	`adaption_brazil_crypto_regulatory_qa`
Training method	SFT + LoRA
LoRA rank (r)	16
LoRA alpha	32
LoRA dropout	0.05
Trainable modules	all-linear
Epochs	3
Training steps	75
Learning rate	5e-5 (cosine scheduler)
Warmup ratio	0.1
Weight decay	0.01

Evaluation results

Training Winrates

Model	Win Rate
Base model	78%
Adapted (`brazil_crypto_regulatory_qa`)	22%

The base model wins on general preference — consistent with the pattern observed when fine-tuning strong multilingual models on narrow domain tasks with structured JSON output. The adapter changes the model's behavior in the intended direction: producing structured financial risk reports with financial_risk_level, fraud_indicators, suitability_concerns, and consumer_explanation fields that base models do not consistently generate.

Train/Eval Metrics

Metric	Value
Initial train loss	1.548
Final validation loss	~0.739
Loss reduction	−52%
Training steps	75
Eval checkpoints	5
LR scheduler	cosine (warmup)

Dataset quality

Metric	Value
Dataset grade	A
Quality improvement	Adaption Adaptive Data remastering
Total examples	140 instruction/response pairs

Output schema

This adapter produces structured JSON financial risk assessments:

{
  "financial_risk_level": "LOW | MEDIUM | HIGH | CRITICAL",
  "investor_risk": "LOW | MEDIUM | HIGH",
  "product_risk": "LOW | MEDIUM | HIGH",
  "fraud_indicators": [],
  "suitability_concerns": [],
  "regulatory_authority": ["BCB", "CVM", "COAF"],
  "regulatory_basis": [],
  "finding": "",
  "corrective_action": "",
  "consumer_explanation": "",
  "confidence": "LOW | MEDIUM | HIGH"
}

Task categories

Category	Task	Examples
A	Financial risk assessment (crypto products)	40
B	Suitability analysis (investor profile vs product)	30
C	Fraud pattern detection	30
D	Regulator routing (BCB / CVM / COAF)	20
E	Consumer explanation (plain language)	20

Training dataset

Platform	Link
HuggingFace Dataset	Fernandosr85/adaption-brazil-crypto-regulatory-qa
Kaggle Dataset	finrisk-br-brazilian-crypto-investor-risk-dataset
Kaggle Notebook	FinRisk-BR

Financial risk scenarios covered

Scenario	Authority	Risk
Token com promessa de rendimento garantido	CVM · BCB	CRITICAL
Esquema de pirâmide com criptoativos	CVM · BCB · COAF	CRITICAL
Golpe via Pix para compra de criptoativos	BCB	CRITICAL
Coerção para transferência via Pix	BCB	CRITICAL
PSAV operando sem autorização BCB	BCB	HIGH
KYC insuficiente com risco de lavagem	BCB · COAF	HIGH
Tokenização de RWA sem registro CVM	CVM	HIGH
Produto de alta volatilidade para perfil conservador	CVM	HIGH
Stablecoin para câmbio não declarado	BCB	HIGH
Token de utilidade sem características de valor mobiliário	CVM	MEDIUM

Regulatory coverage

Authority	Role
BCB (Banco Central do Brasil)	PSAV authorization, AML/CFT, Pix fraud, foreign exchange
CVM (Comissão de Valores Mobiliários)	Suitability, securities tokens, public offerings
COAF	AML/CFT, PEP due diligence, suspicious activity
Federal	Lei 14.478/2022, Decreto 11.563/2023, Resolução Conjunta nº 6/2023

Credits

Fine-tuning platform: Adaption — AutoScientist & Adaptive Data
Challenge: AutoScientist Challenge 2026
Training infrastructure: Adaption compute credits
Author: Fernando Rodrigues · Kaggle: fernandosr85 · HuggingFace: Fernandosr85

Disclaimer

Experimental research artifact submitted to the AutoScientist Challenge 2026 (Finance category). Outputs do not constitute financial or legal advice and require review by qualified professionals.

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Model tree for Fernandosr85/finrisk-br-adapter

Base model

meta-llama/Llama-3.1-70B

Finetuned

meta-llama/Llama-3.3-70B-Instruct

Adapter

(276)

this model

Fernandosr85
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finrisk-br-adapter