Enhanced Audit Techniques Empowered by the Reinforcement Learning
Pertaining to IFRS 16 Lease
- URL: http://arxiv.org/abs/2101.05633v1
- Date: Tue, 5 Jan 2021 14:44:37 GMT
- Title: Enhanced Audit Techniques Empowered by the Reinforcement Learning
Pertaining to IFRS 16 Lease
- Abstract summary: The purpose of accounting audit is to have clear understanding on the financial activities of a company.
The reinforcement learning and its well-known code is to be made for the purpose of drawing the possibility and utilizability of interpreters.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The purpose of accounting audit is to have clear understanding on the
financial activities of a company, which can be enhanced by machine learning or
reinforcement learning as numeric analysis better than manual analysis can be
made. For the purpose of assessment on the relevance, completeness and accuracy
of the information produced by entity pertaining to the newly implemented
International Financial Reporting Standard 16 Lease (IFRS 16) is one of such
candidates as its characteristic of requiring the understanding on the nature
of contracts and its complete analysis from listing up without omission, which
can be enhanced by the digitalization of contracts for the purpose of creating
the lists, still leaving the need of auditing cash flows of companies for the
possible omission due to the potential error at the stage of data collection,
especially for entities with various short or middle term business sites and
related leases, such as construction entities.
The implementation of the reinforcement learning and its well-known code is
to be made for the purpose of drawing the possibility and utilizability of
interpreters from domain knowledge to numerical system, also can be called
'gamification interpreter' or 'numericalization interpreter' which can be
referred or compared to the extrapolation with nondimensional numbers, such as
Froude Number, in physics, which was a source of inspiration at this study.
Studies on the interpreters can be able to empower the utilizability of
artificial general intelligence in domain and commercial area.
Related papers
- Leveraging LLM Parametric Knowledge for Fact Checking without Retrieval [60.25608870901428]
Trustworthiness is a core research challenge for agentic AI systems built on Large Language Models (LLMs)<n>We propose the task of fact-checking without retrieval, focusing on the verification of arbitrary natural language claims, independent of their source robustness.
arXiv Detail & Related papers (2026-03-05T18:42:51Z) - Fine-Tuned Language Models for Domain-Specific Summarization and Tagging [3.543800643014229]
This paper presents a pipeline integrating fine-tuned large language models (LLMs) with named entity recognition (NER) for efficient domain-specific text summarization and tagging.<n>The authors address the challenge posed by rapidly evolving sub-cultural languages and slang, which complicate automated information extraction and law enforcement monitoring.
arXiv Detail & Related papers (2025-10-29T12:33:48Z) - Operationalising Extended Cognition: Formal Metrics for Corporate Knowledge and Legal Accountability [0.0]
We argue that corporate knowledge may be redefined as a dynamic capability, measurable by the efficiency of its information-access procedures.<n>We map these quantitative metrics onto the legal standards of actual knowledge, constructive knowledge, wilful blindness, and recklessness.
arXiv Detail & Related papers (2025-10-17T20:03:57Z) - Benchmarking Chinese Commonsense Reasoning with a Multi-hop Reasoning Perspective [53.594353527056775]
We propose Chinese Commonsense Multi-hop Reasoning ( CCMOR) to evaluate Large Language Models (LLMs)<n> CCMOR is designed to evaluate LLMs' ability to integrate Chinese-specific factual knowledge with multi-step logical reasoning.<n>We implement a human-in-the-loop verification system, where domain experts systematically validate and refine the generated questions.
arXiv Detail & Related papers (2025-10-09T20:29:00Z) - FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering [57.18367828883773]
FinAgentBench is a benchmark for evaluating agentic retrieval with multi-step reasoning in finance.<n>The benchmark consists of 26K expert-annotated examples on S&P-500 listed firms.<n>We evaluate a suite of state-of-the-art models and demonstrate how targeted fine-tuning can significantly improve agentic retrieval performance.
arXiv Detail & Related papers (2025-08-07T22:15:22Z) - Re:Form -- Reducing Human Priors in Scalable Formal Software Verification with RL in LLMs: A Preliminary Study on Dafny [68.00108157244952]
Large Language Models (LLMs) trained with Reinforcement Learning (RL) face a significant challenge: their verification processes are neither reliable nor scalable.<n>A promising yet largely uncharted alternative is formal language-based reasoning.<n>Grounding LLMs in rigorous formal systems where generative models operate in formal language spaces (e.g., Dafny) enables the automatic and mathematically provable verification of their reasoning processes and outcomes.
arXiv Detail & Related papers (2025-07-22T08:13:01Z) - MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model [54.14155564592936]
We propose a Mixture of Rule Experts guided by a Large Language Model (MoRE-LLM)
MoRE-LLM steers the discovery of local rule-based surrogates during training and their utilization for the classification task.
LLM is responsible for enhancing the domain knowledge alignment of the rules by correcting and contextualizing them.
arXiv Detail & Related papers (2025-03-26T11:09:21Z) - An Overview of Large Language Models for Statisticians [109.38601458831545]
Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI)
This paper explores potential areas where statisticians can make important contributions to the development of LLMs.
We focus on issues such as uncertainty quantification, interpretability, fairness, privacy, watermarking and model adaptation.
arXiv Detail & Related papers (2025-02-25T03:40:36Z) - Financial Knowledge Large Language Model [4.599537455808687]
We introduce IDEA-FinBench, an evaluation benchmark for assessing financial knowledge in large language models (LLMs)
We propose IDEA-FinKER, a framework designed to facilitate the rapid adaptation of general LLMs to the financial domain.
Finally, we present IDEA-FinQA, a financial question-answering system powered by LLMs.
arXiv Detail & Related papers (2024-06-29T08:26:49Z) - A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges [60.546677053091685]
Large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain.
We explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation.
We highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications.
arXiv Detail & Related papers (2024-06-15T16:11:35Z) - Large Language Models are Limited in Out-of-Context Knowledge Reasoning [65.72847298578071]
Large Language Models (LLMs) possess extensive knowledge and strong capabilities in performing in-context reasoning.
This paper focuses on a significant aspect of out-of-context reasoning: Out-of-Context Knowledge Reasoning (OCKR), which is to combine multiple knowledge to infer new knowledge.
arXiv Detail & Related papers (2024-06-11T15:58:59Z) - Explainable Authorship Identification in Cultural Heritage Applications:
Analysis of a New Perspective [48.031678295495574]
We explore the applicability of existing general-purpose eXplainable Artificial Intelligence (XAI) techniques to AId.
In particular, we assess the relative merits of three different types of XAI techniques on three different AId tasks.
Our analysis shows that, while these techniques make important first steps towards explainable Authorship Identification, more work remains to be done.
arXiv Detail & Related papers (2023-11-03T20:51:15Z) - Retrieval-Augmented Chain-of-Thought in Semi-structured Domains [10.417698947670564]
Large language models (LLMs) have shown impressive language comprehension and in-context learning capabilities.
This study explores leveraging the semi-structured nature of legal and financial data to efficiently retrieve relevant context.
The resulting system outperforms contemporary models and also provides useful explanations for the answers.
arXiv Detail & Related papers (2023-10-22T22:45:14Z) - Automating construction contract review using knowledge graph-enhanced large language models [1.50580995941543]
This paper investigates whether integrating Large Language Models (LLMs) and Knowledge Graphs (KGs) can enhance the accuracy and interpretability of automated contract risk identification.<n>A tuning-free approach is proposed that integrates LLMs with a Nested Contract Knowledge Graph (NCKG) using a Graph Retrieval-Augmented Generation (GraphRAG) framework for contract knowledge retrieval and reasoning.<n>Tested on international EPC contracts, the method achieves more accurate risk evaluation and interpretable risk summaries than baseline models.
arXiv Detail & Related papers (2023-09-21T14:53:36Z) - KoLA: Carefully Benchmarking World Knowledge of Large Language Models [87.96683299084788]
We construct a Knowledge-oriented LLM Assessment benchmark (KoLA)
We mimic human cognition to form a four-level taxonomy of knowledge-related abilities, covering $19$ tasks.
We use both Wikipedia, a corpus prevalently pre-trained by LLMs, along with continuously collected emerging corpora, to evaluate the capacity to handle unseen data and evolving knowledge.
arXiv Detail & Related papers (2023-06-15T17:20:46Z) - Beyond Classification: Financial Reasoning in State-of-the-Art Language
Models [0.0]
Large Language Models (LLMs) have demonstrated remarkable ability in complex multi-step reasoning tasks.
This research presents a comprehensive investigation into the potential application of LLMs in the financial domain.
The ability to generate coherent financial reasoning first emerges at 6B parameters, and continues to improve with better instruction-tuning or larger datasets.
arXiv Detail & Related papers (2023-04-30T04:36:05Z) - Understanding metric-related pitfalls in image analysis validation [59.15220116166561]
This work provides the first comprehensive common point of access to information on pitfalls related to validation metrics in image analysis.
Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy.
arXiv Detail & Related papers (2023-02-03T14:57:40Z) - Federated Continual Learning to Detect Accounting Anomalies in Financial
Auditing [1.2205797997133396]
We propose a Federated Continual Learning framework enabling auditors to learn audit models from decentral clients continuously.
We evaluate the framework's ability to detect accounting anomalies in common scenarios of organizational activity.
arXiv Detail & Related papers (2022-10-26T21:33:08Z) - Credal Valuation Networks for Machine Reasoning Under Uncertainty [0.6307485015636124]
The paper develops a valuation network as a graphical system for higher-level fusion and reasoning under uncertainty.
A practical implementation of the credal valuation network is discussed and its utility demonstrated on a small scale example.
arXiv Detail & Related papers (2022-08-04T04:33:16Z) - Counterfactual Detection meets Transfer Learning [48.82717416666232]
We show that detecting Counterfactuals is a straightforward Binary Classification Task that can be implemented with minimal adaptation on already existing model Architectures.
We introduce a new end to end pipeline to process antecedents and consequents as an entity recognition task, thus adapting them into Token Classification.
arXiv Detail & Related papers (2020-05-27T02:02:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.