connectailab-rl-CHOCO-BRAIN_GUARD-v1

connectailab-rl-CHOCO-BRAIN_GUARD-v1 — FinTech Fraud Guardian Edition

“connectailab-rl-CHOCO-BRAIN_GUARD-v1: Prefrontal Cortex Fraud Defense Engine for Autonomous E-Commerce & FinTech
English Documentation|한국어 매뉴얼”
Developed by Hwihwa Lab· AI × Automation × Aesthetics

Why a Fraud Defense Decision Engine?

Standard conversational LLMs are prone to hallucinations, verbosity, and latency—rendering them unsuitable for mission-critical financial safety audits.
connectailab-rl-CHOCO-BRAIN_GUARD-v1 is an External Prefrontal Cortex Decision Engine for Solopreneurs & E-Commerce — powered by the JEV (Judgment-Evaluation-Verification) protocol and calibrated via Deep Reinforcement Learning (PPO), this on-device executive cortex neutralizes malicious fraud attacks, high-risk refund exploitation, and compliance crises in 0.05 seconds with zero latency.

Model Persona: "The FinTech Risk Guardian"

While standard generic models maintain balanced weights (1.0 : 1.0 : 1.0), CHOCO-BRAIN_GUARD-v1 is mathematically tuned with an aggressive risk penalty:

$$\text{Reward} = 0.8 \cdot \frac{\text{Profit}}{10} + 1.0 \cdot (\Delta\text{Satisfaction} \times 100) - \mathbf{3.0} \cdot (\text{Incidents} \times 5)$$

Core Guard Protocols:

1. Zero-Tolerance Security Filtering: Hard blocks triggered on transaction fraud score $\ge 0.70$ (Industry standard is 0.95).
2. Suspicious Refund Isolation: Automatic human review routing for high-risk refund claims (score $\ge 0.60$).
3. Legal & Compliance Sentinel: Immediate escalation on critical CS signals (litigation, compliance, defamation threats).

Benchmark & Real-World Validation

Tested against 713 unseen real-world credit card transactions (177 confirmed fraud attempts) and 40 months of stochastic enterprise operations:

_{Autonomous Risk Adjudication in CONNECT TYCOON · Top-tier prefrontal cortex instantly gating fraud & refund abuse across departments to safeguard enterprise capital reserves.}

Dual-Cortex Decision Architecture

This repository forms the natural language reasoning cortex of Hwihwa Lab's two-tier autonomous decision pipeline:

┌────────────────────────────────────────────────────────┐
│  CONNECT TYCOON Enterprise Simulation Environment      │
└───────────────────────────┬────────────────────────────┘
                            │ Multi-objective PPO
                            ▼
┌────────────────────────────────────────────────────────┐
│  [Policy Brain] CHOCO-BRAIN_GUARD-v1-teacher           │
│  - File: brain.json (142 KB Policy Matrix)             │
│  - 11.11M KRW/mo | 96% Satisfaction | 2.0 Incidents    │
└───────────────────────────┬────────────────────────────┘
                            │ JEV Trajectory Ground-Truth
                            ▼
┌────────────────────────────────────────────────────────┐
│  [Reasoning LLM] CHOCO-BRAIN_GUARD-v1 (Current Model)  │
│  - Qwen3.5-0.8B LoRA Adapter (Natural Language JEV)   │
│  - 0.05s Real-time Fraud Adjudication & Action Routing │
└───────────────────────────┬────────────────────────────┘
                            │ Quantization (Q8_0)
                            ▼
┌────────────────────────────────────────────────────────┐
│  [Edge Package] CHOCO-BRAIN_GUARD-v1-GGUF              │
│  - 811 MB Standalone GGUF Binary + Modelfile Bundle   │
│  - Zero-latency local execution in Ollama & LM Studio  │
└────────────────────────────────────────────────────────┘

Quick Start & JEV Protocol

1. High-Level Decision Engine (connect-tycoon)

import connect_tycoon as ct

# Load directly from Hugging Face Hub
brain = ct.CompanyBrain.from_hub("hwihwalab/connectailab-rl-CHOCO-BRAIN_GUARD-v1")

# Make real-time decision
response = brain({
    "model": "typesafe/jev-1.13",
    "state": {
        "department": "refund",
        "reason": "defect",
        "amount": 45.0,
        "fraud_score": 0.82
    },
    "questions": {"decision": {"type": "choice"}}
})
print("Guardian Decision:", response["answers"]["decision"]["choice"])

2. Standard Hugging Face Transformers & PEFT

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "Qwen/Qwen3.5-0.8B"
adapter_id = "hwihwalab/connectailab-rl-CHOCO-BRAIN_GUARD-v1"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, adapter_id)

# Run simulator with CHOCO-BRAIN_GUARD as CEO
connect-tycoon play --brain hf:hwihwalab/connectailab-rl-CHOCO-BRAIN_GUARD-v1

About Hwihwa Lab

“AI × Automation × Aesthetics — Crafting tools you'll love ”

Hwihwa Lab is an independent AI research & engineering studio dedicated to blending practical on-device intelligence, autonomous operations, and refined aesthetics. We engineer resilient systems that free solopreneurs and indie creators from operational overhead—giving people their time and creative joy back.

Core Research & Engineering Pillars

• Zero-Cost Edge Operations: Private, on-device AI pipelines for autonomous business workflows without ongoing cloud API overhead.
• Autonomous Decision OS & Micro-ERP: Deterministic, prefrontal-cortex-style reasoning architectures designed to orchestrate daily business decisions and operations.
• Embodied AI & Domain-Specific Robotics: Simulation-driven reinforcement learning, physical control brains, and specialized domain data engineering for real-world environments.
• Interactive Creative Aesthetics: Fusing functional automation with refined visual craft and modern design philosophy to build delightful tools.

Connect & Research Ecosystem

• Research Studio: Hugging Face (@hwihwalab) · GitHub (@Hwihwa-Lab)
• CHOCO-BRAIN GUARD Defense Suite:
• Real-Time Fraud Adjudicator: connectailab-rl-CHOCO-BRAIN_GUARD-v1 (Current Model) (FP16 reasoning LLM executing 0.05s fraud judgment via JEV protocol)
• Zero-Leakage Edge Fraud Guardian: connectailab-rl-CHOCO-BRAIN_GUARD-v1-GGUF (811MB Q8_0 binary for private, on-device fraud defense without cloud leakage)
• Fraud Defense Policy Core: connectailab-rl-CHOCO-BRAIN_GUARD-v1-teacher (142KB PPO reinforcement learning policy trained with 3x risk penalty)

_{Part of the CONNECT AI LAB Collaborative Research Network · CONNECT TYCOON Decision Foundation}

License

This project and fine-tuned weights are distributed under the Apache 2.0 License — see the repository LICENSE file for full details.

Base model weights adhere to the original Qwen License.

Built and maintained by HWIHWA LAB in collaboration with CONNECT AI LAB.