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Framework for Automating Bank Transactions and Invoice Mapping Using Multi-criteria Optimization and Neural Networks 43rd

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Framework for Automating Bank Transactions and Invoice Mappi...

43rd IBIMA Conference on Artificial intelligence and Machine Learning, IBIMA-AI 2024

作者： Protasowicki, Tomasz Institute of Computer and Information Systems Faculty of Cybernetics Military University of Technology Warsaw Poland

ISBN: (纸本)9783031774928

This study aims to address the labor-intensive process of bank transaction and invoice reconciliation by presenting a comprehensive framework that leverages multi-criteria optimization and neural networks. The literature reveals a significant gap in automating this process with high accuracy and efficiency. The method presented in this paper integrates optimization techniques and neural networks to minimize manual intervention and enhance the precision of the reconciliation process. Utilizing utility functions for multi-criteria decision-making can effectively combine optimization techniques to balance accuracy, processing time, and error rates. Findings indicate that the proposed framework significantly improves processing time and accuracy, providing a scalable and efficient solution for financial operations. The presented framework demonstrates its potential by achieving a high level of F1-score, reducing processing times, and minimizing error rates, thereby advancing the automation of financial reconciliations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： financial data processing

Statistical inference for highly correlated stationary point processes and noisy bivariate Neyman-Scott processes

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arXiv 2024年

作者： Shiotani, Takaaki Yoshida, Nakahiro Japan Science and Technology Agency CREST Japan The Institute of Statistical Mathematics Graduate School of Mathematical Sciences University of Tokyo 3-8-1 Komaba Meguro-ku Tokyo153-8914 Japan

Motivated by estimating the lead-lag relationships in high-frequency financial data, we propose noisy bivariate Neyman-Scott point processes with gamma kernels (NBNSP-G). NBNSP-G tolerates noises that are not necessarily Poissonian and has an intuitive interpretation. Our experiments suggest that NBNSP-G can explain the correlation of orders of two stocks well. A composite-type quasi-likelihood is employed to estimate the parameters of the model. However, when one tries to prove consistency and asymptotic normality, NBNSP-G breaks the boundedness assumption on the moment density functions commonly assumed in the literature. Therefore, under more relaxed conditions, we show consistency and asymptotic normality for bivariate point process models, which include NBNSP-G. Our numerical simulations also show that the estimator is indeed likely to converge. Copyright © 2024, The Authors. All rights reserved.

关键词： financial data processing

Extracting Structured Insights from financial News: An Augmented LLM Driven Approach

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arXiv 2024年

作者： Dolphin, Rian Dursun, Joe Chow, Jonathan Blankenship, Jarrett Adams, Katie Pike, Quinton Polygon.io AtlantaGA United States

financial news plays a crucial role in decision-making processes across the financial sector, yet the efficient processing of this information into a structured format remains challenging. This paper presents a novel approach to financial news processing that leverages Large Language Models (LLMs) to overcome limitations that previously prevented the extraction of structured data from unstructured financial news. We introduce a system that extracts relevant company tickers from raw news article content, performs sentiment analysis at the company level, and generates summaries, all without relying on pre-structured data feeds. Our methodology combines the generative capabilities of LLMs, and recent prompting techniques, with a robust validation framework that uses a tailored string similarity approach. Evaluation on a dataset of 5530 financial news articles demonstrates the effectiveness of our approach, with 90% of articles not missing any tickers compared with current data providers, and 22% of articles having additional relevant tickers. In addition to this paper, the methodology has been implemented at scale with the resulting processed data made available through a live API endpoint, which is updated in real-time with the latest news1. To the best of our knowledge, we are the first data provider to offer granular, per-company sentiment analysis from news articles, enhancing the depth of information available to market participants. We also release the evaluation dataset of 5530 processed articles as a static file2, which we hope will facilitate further research leveraging financial news. © 2024, CC BY.

关键词： financial data processing

AI Adoption to Combat financial Crime: Study on Natural Language processing in Adverse Media Screening of financial Services in English and Bangla multilingual interpretation

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arXiv 2024年

作者： Roy, Soumita Institute of Business Administration - JU Bangladesh

This document explores the potential of employing Artificial Intelligence (AI), specifically Natural Language processing (NLP), to strengthen the detection and prevention of financial crimes within the Mobile financial Services(MFS) of Bangladesh with multilingual scenario. The analysis focuses on the utilization of NLP for adverse media screening, a vital aspect of compliance with anti-money laundering (AML) and combating financial terrorism (CFT) regulations. Additionally, it investigates the overall reception and obstacles related to the integration of AI in Bangladeshi banks. This report measures the effectiveness of NLP is promising with an accuracy around 94%. NLP algorithms display substantial promise in accurately identifying adverse media content linked to financial crimes. The lack of progress in this aspect is visible in Bangladesh, whereas globally the technology is already being used to increase effectiveness and efficiency. Hence, it is clear there is an issue with the acceptance of AI in Bangladesh. Some AML & CFT concerns are already being addressed by AI technology. For example, Image Recognition OCR technology are being used in KYC procedures. Primary hindrances to AI integration involve a lack of technical expertise, high expenses, and uncertainties surrounding regulations. This investigation underscores the potential of AI-driven NLP solutions in fortifying efforts to prevent financial crimes in Bangladesh. © 2024, CC BY-NC-ND.

关键词： financial data processing

SIMULATION-BASED APPROACH FOR MULTIPROJECT SCHEDULING BASED ON COMPOSITE PRIORITY RULES

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arXiv 2024年

作者： Alvarez-Campana, Pablo Villafañez, Felix Acebes, Fernando Poza, David GIR INSISOC Department of Business Organization and CIM School of Industrial Engineering University of Valladolid Valladolid47011 Spain

This paper presents a simulation approach to enhance the performance of heuristics for multiproject scheduling. Unlike other heuristics available in the literature that use only one priority criterion for resource allocation, this paper proposes a structured way to sequentially apply more than one priority criterion for this purpose. By means of simulation, different feasible schedules are obtained to, therefore, increase the probability of finding the schedule with the shortest duration. The performance of this simulation approach was validated with the MPSPLib library, one of the most prominent libraries for resource-constrained multiproject scheduling. These results highlight the proposed method as a useful option for addressing limited time and resources in portfolio management. © 2024, CC BY-NC-ND.

关键词： financial data processing

Retracted: Prediction of stock market movement via technical analysis of stock data stored on blockchain using novel History Bits based machine learning algorithm

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IET Software 2023年第4期17卷 705-716页

作者： Nitin Nandkumar Sakhare Imambi S. Shaik Suman Saha Computer Science and Engineering Department Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Suman Saha Department of ICT Bangabandhu Sheikh Mujibur Rahman Digital University Bangladesh Gazipur Bangladesh. Department of ICT Bangabandhu Sheikh Mujibur Rahman Digital University Bangladesh Gazipur Bangladesh Nitin Nandkumar Sakhare Computer Science and Engineering Department Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh 522302 India.

Retraction: [Nitin Nandkumar Sakhare, Imambi S. Shaik, Suman Saha, Prediction of stock market movement via technical analysis of stock data stored on blockchain using novel History Bits based machine learning algorithm, IET Software 2023 ( https://***/10.1049/sfw2.12092 )]. The above article from IET Software , published online on 12 January 2023 in Wiley Online Library (***), has been retracted by agreement between the Editor-in-Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

关键词： blockchains decision trees open prices private blockchain market activity learning (artificial intelligence) random forests trading call stock market movement high prices low prices securities movement pricing stock market predictions multilayer perceptrons stock data financial data processing NIFTY 50 dataset close prices stock trading data stock markets pattern classification support vector machine ensemble-based rank search strategy History Bits model data manipulation support vector machines History Bits algorithm

A Cloud Resources Portfolio Optimization Business Model - From Theory to Practice

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arXiv 2024年

作者： Haag, Valentin Kiessler, Maximilian Pittl, Benedikt Schikuta, Erich Faculty of Computer Science University of Vienna Vienna Austria

Cloud resources have become increasingly important, with many businesses using cloud solutions to supplement or outright replace their existing IT infrastructure. However, as there is a plethora of providers with varying products, services, and markets, it has become increasingly more challenging to keep track of the best solutions for each application. Cloud service intermediaries aim to alleviate this problem by offering services that help users meet their requirements. This paper aims to lay the groundwork for developing a cloud portfolio management platform and its business model, defined via a business model canvas. Furthermore, a prototype of a platform is developed offering a cloud portfolio optimization service, using two algorithms developed in previous research to create suitable and well-utilized allocations for a customer’s *** Codes 91-08 Copyright © 2024, The Authors. All rights reserved.

关键词： financial data processing

ViBERTgrid BiLSTM-CRF: Multimodal Key Information Extraction from Unstructured financial Documents

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arXiv 2024年

作者： Pala, Furkan Akpınar, Mehmet Yasin Deniz, Onur Eryigit, Gülşen Natural Language Processing Yapı Kredi Technology Istanbul34467 Turkey Department of Artificial Intelligence and Data Engineering Istanbul Technical University Istanbul34469 Turkey

Multimodal key information extraction (KIE) models have been studied extensively on semi-structured documents. However, their investigation on unstructured documents is an emerging research topic. The paper presents an approach to adapt a multimodal transformer (i.e., ViBERTgrid previously explored on semi-structured documents) for unstructured financial documents, by incorporating a BiLSTM-CRF layer. The proposed ViBERTgrid BiLSTM-CRF model demonstrates a significant improvement in performance (up to 2 percentage points) on named entity recognition from unstructured documents in financial domain, while maintaining its KIE performance on semi-structured documents. As an additional contribution, we publicly released token-level annotations for the SROIE dataset in order to pave the way for its use in multimodal sequence labeling models. © 2024, CC BY.

关键词： financial data processing

A Dutch financial Large Language Model

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arXiv 2024年

作者： Noels, Sander De Blaere, Jorne De Bie, Tijl Ghent University Ghent Belgium Silverfin Ghent Belgium

This paper presents FinGEITje, the first Dutch financial Large Language Model (LLM) specifically designed and optimized for various financial tasks. Together with the model, we release a specialized Dutch financial instruction tuning dataset with over 140,000 samples, constructed employing an automated translation and data processing method. The open-source data construction method is provided, facilitating the creation of financial instruction datasets in different languages. To evaluate model performance, the study introduces the first Dutch financial evaluation benchmark, along with an automated evaluation method that utilizes an LLM as an independent evaluator, reducing manual intervention in performance evaluation. The experimental results highlight the superior performance of FinGEITje across five critical Dutch and English financial tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： financial data processing