Paper: arXiv 2510.15993

Authors: Fernando Spadea, Oshani Seneviratne

Abstract

Most financial recommendation systems often fail to account for key behavioral and regulatory factors, leading to advice that is misaligned with user preferences, difficult to interpret, or unlikely to be followed. We present FLARKO (Financial Language-model for Asset Recommendation with Knowledge-graph Optimization), a novel framework that integrates Large Language Models (LLMs), Knowledge Graphs (KGs), and Kahneman-Tversky Optimization (KTO) to generate asset recommendations that are both profitable and behaviorally aligned. FLARKO encodes users’ transaction histories and asset trends as structured KGs, providing interpretable and controllable context for the LLM. To demonstrate the adaptability of our approach, we develop and evaluate both a centralized architecture (CenFLARKO) and a federated variant (FedFLARKO). To our knowledge, this is the first demonstration of combining KTO for fine-tuning of LLMs for financial asset recommendation. We also present the first use of structured KGs to ground LLM reasoning over behavioral financial data in a federated learning (FL) setting. Evaluated on the FAR-Trans dataset, FLARKO consistently outperforms state-of-the-art recommendation baselines on behavioral alignment and joint profitability, while remaining interpretable and resource-efficient.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper combines advanced concepts (KTO, knowledge graphs, federated learning, LLM fine-tuning) with substantial empirical evaluation on a specific dataset, clear performance metrics, and multi-model comparisons.

Research Flowchart

  flowchart TD
  A["Research Goal"] --> B["Develop FLARKO Framework"]
  B --> C["Input Data"]
  C --> D["KG & LLM Integration"]
  D --> E["Apply KTO for Alignment"]
  E --> F["Centralized vs. Federated"]
  F --> G["Key Outcomes & Findings"]
  
  subgraph C ["Input Data"]
      C1["FAR-Trans Dataset"]
      C2["User Transaction History"]
      C3["Asset Trends"]
  end
  
  subgraph G ["Outcomes"]
      G1["Superior Behavioral Alignment"]
      G2["Joint Profitability"]
      G3["Interpretable Recommendations"]
      G4["Resource Efficient"]
  end