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:
json
1{2"financial_risk_level":"LOW | MEDIUM | HIGH | CRITICAL",3"investor_risk":"LOW | MEDIUM | HIGH",4"product_risk":"LOW | MEDIUM | HIGH",5"fraud_indicators":[],6"suitability_concerns":[],7"regulatory_authority":["BCB","CVM","COAF"],8"regulatory_basis":[],9"finding":"",10"corrective_action":"",11"consumer_explanation":"",12"confidence":"LOW | MEDIUM | HIGH"13}
Task categories
Category
Task
Examples
A
Financial risk assessment (crypto products)
40
B
Suitability analysis (investor profile vs product)
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.
