WaliyaKhan882/agentic_fintech

Agentic FinTech — Security & Reliability Gaps in RL-based Systems

Dataset Summary

A domain-knowledge instruction-tuning dataset built from 60 peer-reviewed academic papers on security and reliability challenges in reinforcement-learning-based agentic financial systems. Designed for fine-tuning instruction-following LLMs (e.g. meta-llama/Llama-3.2-1B-Instruct) to answer expert-level questions about RL agents, LLM-based trading systems, adversarial threats, and regulatory gaps in FinTech.

All user questions are domain-level — they never reference paper titles — so a fine-tuned model can knowledgeably answer questions from anyone unfamiliar with the underlying literature.

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Dataset Details

Property	Value
Format	ChatML messages (system / user / assistant)
Total entries	3,280
Source papers	60 peer-reviewed papers
Coverage years	2020–2025
Primary domains	RL in FinTech, LLM Agents, Agentic AI, Cybersecurity, Financial Regulation

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Data Fields

Each record contains a single messages field — a list of three dicts:

{
  "messages": [
    {"role": "system",    "content": "You are a knowledgeable research assistant ..."},
    {"role": "user",      "content": "<domain-level question>"},
    {"role": "assistant", "content": "<detailed answer drawn from academic literature>"}
  ]
}

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Dataset Structure

The dataset is organised into three sections:

Section 1 — Per-Paper Domain Q&A (2,700 entries)

Nine question types × 5 rephrasings per paper (60 papers). Questions use domain concepts (mechanism, dimension, sub-domain) — never the paper title.

Question type	Example user question
concept	How does layered memory improve decision-making in autonomous trading agents?
threat	What adversarial risks arise when deploying multi-agent RL systems in live markets?
solution	What techniques are used to defend RL-based portfolio managers against reward hacking?
empirical	What empirical evidence exists for goal misgeneralization in agentic FinTech systems?
dimension	How is the reliability dimension addressed in LLM-based agentic trading frameworks?
future	What open research challenges remain for federated learning in financial AI?
datasets	What datasets are commonly used to benchmark agentic trading systems?
taxonomy	How are security threats in RL-based financial agents typically classified?
which_papers	Which research addresses adversarial robustness in deep RL trading agents?

Section 2 — Grouped Paper Lists (~500 entries)

Questions asking to list papers grouped by pillar, dimension, or mechanism (5 rephrasings each). Paper titles appear only in answers.

Section 3 — General Synthesis Q&A (80 entries)

16 high-level synthesis topics × 5 rephrasings: security gaps · reliability gaps · adversarial attacks · goal misgeneralization · RL in FinTech · LLM agents · hallucinations · regulatory gaps · federated learning · memory architectures · MARL · taxonomy · evaluation metrics · agentic AI definition · systemic risk · meta paper list

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Source Data

Constructed from two source files covering 60 papers:

• Excel spreadsheet — 15 structured fields per paper: title, venue, year, domain, sub-domain, pillar, dimension, mechanism, problem, solution, results, evaluation measures, dataset, future work, summary
• DOCX document — 5-step structured summaries per paper: abstract, introduction, method, results, discussion

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System Prompt

All entries share the same system message:

"You are a knowledgeable research assistant specialising in artificial intelligence, reinforcement learning, and financial technology. Answer questions accurately and concisely based on published academic research on security and reliability gaps in RL-based agentic financial systems."

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Intended Use

Fine-tuning small instruction-following LLMs (1B–7B parameters) to act as domain experts on:

• Security vulnerabilities in RL-based financial agents
• Reliability and robustness challenges in agentic FinTech
• Adversarial attacks, reward hacking, and goal misgeneralization
• Regulatory and ethical gaps in autonomous financial AI
• State-of-the-art methods and open research problems

Not intended for general-purpose financial advice or real-time trading decisions.

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Citation

If you use this dataset, please cite the original 60 academic papers it was derived from. The dataset itself was compiled and structured for research and educational purposes under a CC-BY 4.0 license.