Singapore

This study investigated LLM–based automation for analyzing non-financial data in corporate credit evaluation. Two systems were developed and compared: a Single-Agent System (SAS), in which one LLM agent infers favorable and adverse repayment signals, and a Popperian Multi-Agent Debate System (PMADS), which structures the dual-perspective analysis as adversarial argumentation under the Karl Popper Debate protocol. Evaluation addressed three fronts: (i) work productivity (task completion time) compared with human experts; (ii) perceived report quality and usability, rated by credit risk professionals for system-generated reports; and (iii) reasoning characteristics quantified via reasoning-tree analysis (breadth, depth, and Reasoning Elaboration Index). Both systems drastically reduced task completion time relative to human experts (baseline ≈ 1,900 s/case; SAS ≈ 11.6 s; PMADS ≈ 92.0 s). Professionals rated SAS re-ports as adequate, while PMADS reports exceeded neutral benchmarks and scored significantly higher in explanatory adequacy, practical applicability, and usability. Reasoning-tree analysis showed PMADS produced deeper, more elaborated structures, whereas SAS yielded single-layered trees. These findings suggest that structured multi-agent debate enhances analytical rigor and perceived usefulness, though at the cost of longer computation time. Overall, the results demonstrate that reasoning-centered automation represents a promising approach for developing useful AI systems in decision-critical financial contexts.

AAAI 2026

Structured Debate Improves Corporate Credit Reasoning in Financial AI

mas: agent/ai theories and architectures

mas: applications

mas: coordination and collaboration

workshop paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Despite accounting for 96.1% of all businesses in Malaysia, access to financing remains one of the most persistent challenges faced by Micro, Small, and Medium Enterprises (MSMEs). Newly established or young businesses are often excluded from formal credit markets as traditional underwriting approaches rely heavily on credit bureau data. This study investigates the potential of bank statement data as an alternative data source for credit assessment to promote financial inclusion in emerging markets. Firstly, we propose a cash flow-based underwriting pipeline where we utilise bank statement data for end-to-end data extraction and machine learning credit scoring. Secondly, we introduce a novel dataset of 611 loan applicants from a Malaysian lending institution. Thirdly, we develop and evaluate credit scoring models based on application information and bank transaction-derived features. Empirical results show that the use of such data boosts the performance of all models on our dataset, which can improve credit scoring for new-to-lending MSMEs. Lastly, we intend to release the anonymised bank transaction dataset to facilitate further research on MSMEs financial inclusion within Malaysia's emerging economy.

Cash Flow Underwriting with Bank Transaction Data: Advancing MSME Financial Inclusion in Malaysia

Large Language Model (LLM)-based agent systems have shown strong potential for autonomous financial analysis. However, most multi-agent trading systems still fail to account for the diverse perspectives that drive real-market price movements, limiting their ability to capture the step-by-step reasoning and consensus-building seen in professional investment teams. We introduce AGORA-F: an AGentic Orchestrated Reasoning Architecture for Finance-that treats financial decision-making as a process in which agents gradually reach a shared conclusion through discussion based optimization. AGORA-F structures its workflow into specialized teams: Query Understanding Agents interpret user intent and derive data requirements; Analytical Agents collect and normalize real-time market data, financial statements, and sentiment signals to prepare detailed briefs; and Financial Analysis Agents evaluate the stock from multiple viewpoints, including return potential, risk exposure, macroeconomic context, and competitive positioning. A central Trading Agent coordinates iterative discussions among the financial analysts, refining information requests and aligning perspectives via an agentic, prompt-based gradient mechanism. When the discussion converges or a preset iteration budget is reached-a Manager Agent synthesizes the collective rationale and issues a final decision-buy, hold, or sell-with explicit evidence trails. Empirical results indicate that AGORA-F yields more consistent and explainable trading outcomes than comparable baselines, marking a step toward human-team-like collaborative reasoning in autonomous financial systems. Our code will be made publicly available.

AGORA-F: An Agentic, Gradient-Orchestrated Multi-Agent Architecture for Financial Decision-Making

FinOps (Finance + Operations) represents an operational framework and cultural practice which maximizes cloud business value through collaborative financial accountability across engineering, finance, and business teams. FinOps practitioners face a fundamental challenge: billing data arrives in heterogeneous formats, taxonomies, and metrics from multiple cloud providers and internal systems which eventually lead to synthesizing actionable insights, and making time-sensitive decisions. To address this challenge, we propose leveraging autonomous, goal-driven AI agents for FinOps automation. In this paper, we built a FinOps agent for a typical use-case for IT infrastructure and cost optimization. We built a system simulating a realistic end-to-end industry process starting with retrieving data from various sources to consolidating and analyzing the data to generate recommendations for optimization. We defined a set of metrics to evaluate our agent using several open-source and close-source language models and it shows that the agent was able to understand, plan, and execute tasks as well as an actual FinOps practitioner.

FinOps Agent - A Use-Case for IT Infrastructure and Cost Optimization

Financial AI systems suffer from a critical blind spot: while Retrieval-Augmented Generation (RAG) excels at finding relevant documents, language models still generate calculation errors and regulatory violations during reasoning, even with perfect retrieval. This paper introduces VERAFI (Verified Agentic Financial Intelligence), an agentic framework with neurosymbolic policy generation for verified financial intelligence. VERAFI combines state-of-the-art dense retrieval and cross-encoder reranking with financial tool-enabled agents and automated reasoning policies covering GAAP compliance, SEC requirements, and mathematical validation. Our comprehensive evaluation on FinanceBench demonstrates remarkable improvements: while traditional dense retrieval with reranking achieves only 52.4% factual correctness, VERAFI's integrated approach reaches 94.7%, an 81% relative improvement. The neurosymbolic policy layer alone contributes a 4.3 percentage point gain over pure agentic processing, specifically targeting persistent mathematical and logical errors. By embedding financial domain expertise directly into the reasoning process, VERAFI offers a practical pathway toward trustworthy financial AI that meets the stringent accuracy demands of regulatory compliance, investment decisions, and risk management.

VERAFI: Verified Agentic Financial Intelligence through Neurosymbolic Policy Generation

This paper presents a first empirical study of agentic AI as autonomous decision-makers in decentralized governance. Using more than 3K proposals from major protocols, we build an agentic AI voter that interprets proposal contexts, retrieves historical deliberation data, and independently determines its voting position. The agent operates within a realistic financial simulation environment grounded in verifiable blockchain data, implemented through a modular composable program (MCP) workflow that defines data flow and tool usage via Agentics framework. We evaluate how closely the agent’s decisions align with the human and token-weighted outcomes, uncovering strong alignments measured by carefully designed evaluation metrics. Our findings demonstrate that agentic AI can augment collective decision-making by producing interpretable, auditable, and empirically grounded signals in realistic DAO governance settings. The study contributes to the design of explainable and economically rigorous AI agents for decentralized financial systems.

DAO-AI: Evaluating Collective Decision-Making through Agentic AI in Decentralized Governance

Multi-agent systems have demonstrated the ability to improve performance on a variety of predictive tasks by leveraging collaborative decision making. However, the lack of effective evaluation methodologies has made it difficult to estimate the risk of bias, making deployment of such systems unsafe in high stakes domains such as consumer finance, where biased decisions can translate directly into regulatory breaches and financial loss. To address this challenge, we need to develop fairness evaluation methodologies for multi-agent predictive systems and measure the fairness characteristics of these systems in the financial tabular domain. Examining fairness metrics using large-scale simulations across diverse multi-agent configurations, with varying communication and collaboration mechanisms, we reveal patterns of emergent bias in financial decision-making that cannot be traced to individual agent components, indicating that multi-agent systems may exhibit genuinely collective behaviors. Our findings highlight that fairness risks in financial multi-agent systems represent a significant component of model risk, with tangible impacts on tasks such as credit scoring and income estimation. We advocate that multi-agent decision systems must be evaluated as holistic entities rather than through reductionist analyses of their constituent components.

Emergent Bias and Fairness in Multi-Agent Decision Systems

Retrieval-Augmented Generation (RAG) is becoming increasingly essential for Question Answering (QA) in the financial sector, where accurate and contextually grounded insights from complex public disclosures are crucial. However, existing financial RAG systems face two significant challenges: (1) they struggle to process heterogeneous data formats, such as text, tables, and figures; and (2) they encounter difficulties in balancing general-domain applicability with company-specific adaptation. To overcome these challenges, we present VeritasFi, an innovative hybrid RAG framework that incorporates a multi-modal preprocessing pipeline alongside a cutting-edge two-stage training strategy for its re-ranking component. VeritasFi enhances financial QA through three key innovations: (1) A multi-modal preprocessing pipeline that seamlessly transforms heterogeneous data into a coherent, machine-readable format. (2) A tripartite hybrid retrieval engine that operates in parallel, combining deep multi-path retrieval over a semantically indexed document corpus, real-time data acquisition through tool utilization, and an expert-curated memory bank for high-frequency questions, ensuring comprehensive scope, accuracy, and efficiency. (3) A two-stage training strategy for the document re-ranker, which initially constructs a general, domain-specific model using anonymized data, followed by rapid fine-tuning on company-specific data for targeted applications. By integrating our proposed designs, VeritasFi presents a groundbreaking framework that greatly enhances the adaptability and robustness of financial RAG systems, providing a scalable solution for both general-domain and company-specific QA tasks. In additional, VeritasFi has been deployed as an online meeting agent, for which it has served more than 2000 people.

VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering

Automated Question Generation (AQG) plays a crucial role in enabling scalable assessment and personalized learning, serving as a cornerstone of modern Intelligent Tutoring Systems (ITS). However, existing AQG systems, particularly those built upon Large Language Models (LLMs), face a significant cognitive ceiling in generating high-stakes, exam-ready content for specialized domains like finance (e.g., CFA Exams). They often generate linguistically fluent but cognitively shallow items, failing to ensure difficulty calibration, distractor plausibility, computational correctness, and multi-concept integration. To address these challenges, we propose Fin-Agent-QG, a cognitive-collaborative multi-agent framework that emulates the workflow of professional financial exam committees. Fin-Agent-QG coordinates specialized agents for different cognitive phases of question construction: a Teacher–Critic loop for conceptual and difficulty alignment, a Quantitative Analyst Agent utilizing Code-as-Chain-of-Thought for computational reasoning, and a Knowledge Graph Construction Agent with a Case Strategist for multi-concept integration via a Financial Knowledge Graph. Empirical evaluations by both LLM metrics and human experts demonstrate that Fin-Agent-QG produces exam-ready financial questions with greater cognitive depth, accuracy, and conceptual coherence than traditional AQG systems. This work bridges the gap between automated generation and expert-level financial assessment design.

Fin-Agent-QG: A Collaborative Multi-Agent Framework for Higher-Order Cognitive and Difficulty-Aware Financial Exam Question Generation

We present ArgBoost, a transparent multi-agent forecasting framework for directional cryptocurrency price prediction that integrates structured LLM-based reasoning with interpretable machine learning. ArgBoost classifies next-day VWAP (Volume Weighted Average Price) movements for Ethereum by aggregating outputs from specialized LLM agents operating across distinct data domains. The agent-generated claims are organized into an argumentation graph and analyzed using an Explainable Boosting Machine (EBM) trained on rolling windows. Using daily Ethereum data from January 2024 to April 2025, ArgBoost outperforms Monte Carlo baselines and traditional technical strategies in both predictive and trading performance. The model demonstrates particular strength in tail market conditions, accurately detecting sharp upswings and downturns. Feature importance analysis shows that interaction terms involving coordinated relationships among agents and contextual market conditions jointly enhance predictability, offering a transparent and modular approach to evidence-driven decision support in volatile financial markets.

ArgBoost: Interpretable Multi-Agent Reasoning for Directional Cryptocurrency Price Forecasting

Rice is one of the most widely cultivated crops globally and has been developed into numerous varieties. The quality of rice during cultivation is primarily determined by its cultivar and characteristics. Traditionally, rice classification and quality assessment rely on manual visual inspection, a process that is both time-consuming and prone to errors. However, with advancements in machine vision technology, automating rice classification and quality evaluation based on its cultivar and characteristics has become increasingly feasible, enhancing both accuracy and efficiency. This study proposes a real-time evaluation mechanism for comprehensive rice grain assessment, integrating a one-stage object detection approach, a deep convolutional neural network, and traditional machine learning techniques. The proposed framework enables rice variety identification, grain completeness grading, and grain chalkiness evaluation. The rice grain dataset used in this study comprises approximately 20,000 images from six widely cultivated rice varieties in China. Experimental results demonstrate that the proposed mechanism achieves a mean Average Precision (mAP) of 99.14\% in the object detection task and an accuracy of 97.89\% in the classification task. Furthermore, the framework attains an average accuracy of 97.56\% in grain completeness grading within the same rice variety, contributing to an effective quality evaluation system.

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Cash Flow Underwriting with Bank Transaction Data: Advancing MSME Financial Inclusion in Malaysia

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