Fin-Ally: Pioneering the Development of an Advanced, Commonsense-Embedded Conversational AI for Money Matters

2025-09-29

AI Summary

Financial dialogue systems often generate factually correct yet contextually inappropriate responses due to insufficient commonsense reasoning and politeness regulation, undermining professional credibility; moreover, the scarcity of high-quality multi-turn annotated data restricts existing work to isolated module development. To address these challenges, we propose Fin-Ally: (1) we introduce Fin-Vault, the first financial consulting-oriented multi-turn dialogue dataset comprising 1,417 dialogues; (2) we design an end-to-end model integrating commonsense reasoning (via COMET-BART injection), politeness modeling, and multi-turn contextual understanding; and (3) we employ Direct Preference Optimization (DPO) to align outputs with human preferences. Experiments on budget planning, expense tracking, and investment advice tasks demonstrate significant improvements in response accuracy and pragmatic appropriateness over state-of-the-art baselines. Fin-Ally advances financial dialogue systems toward greater professionalism, affective alignment, and trustworthiness.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Humans and AI: Learning Human Values and Preferences

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search

Abstract

The exponential technological breakthrough of the FinTech industry has significantly enhanced user engagement through sophisticated advisory chatbots. However, large-scale fine-tuning of LLMs can occasionally yield unprofessional or flippant remarks, such as ``With that money, you're going to change the world,'' which, though factually correct, can be contextually inappropriate and erode user trust. The scarcity of domain-specific datasets has led previous studies to focus on isolated components, such as reasoning-aware frameworks or the enhancement of human-like response generation. To address this research gap, we present Fin-Solution 2.O, an advanced solution that 1) introduces the multi-turn financial conversational dataset, Fin-Vault, and 2) incorporates a unified model, Fin-Ally, which integrates commonsense reasoning, politeness, and human-like conversational dynamics. Fin-Ally is powered by COMET-BART-embedded commonsense context and optimized with a Direct Preference Optimization (DPO) mechanism to generate human-aligned responses. The novel Fin-Vault dataset, consisting of 1,417 annotated multi-turn dialogues, enables Fin-Ally to extend beyond basic account management to provide personalized budgeting, real-time expense tracking, and automated financial planning. Our comprehensive results demonstrate that incorporating commonsense context enables language models to generate more refined, textually precise, and professionally grounded financial guidance, positioning this approach as a next-generation AI solution for the FinTech sector. Dataset and codes are available at: https://github.com/sarmistha-D/Fin-Ally

Problem

• Addresses unprofessional AI responses in financial advisory chatbots
• Solves domain-specific dataset scarcity for financial conversations
• Integrates commonsense reasoning with human-like financial guidance

Innovation

• Introduces multi-turn financial dataset Fin-Vault
• Integrates commonsense reasoning with politeness dynamics
• Optimizes responses using Direct Preference Optimization mechanism