International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 06 | Jun 2024
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p-ISSN: 2395-0072
"AI-Powered Transaction Reconciliation: A Reinforcement Learning Approach" Sudharson Gunasekaran Senior Software Developer at a Leading Investment Management Firm ---------------------------------------------------------------------***--------------------------------------------------------------------Deep reinforcement learning addresses utility maximization Abstract - The banking sector is undergoing a significant
problems, further improving reconciliation methods. AIdriven reconciliation systems will eventually replace traditional methods, with neural networks automatically identifying and rectifying transaction errors (Perdana et al., 2023 [5]). The reinforcement learning models already being developed and used by other organizations can be further trained and customized to become more efficient in handling transactions (Charpentier et al., 2021 [6]).
transformation with the advent of digital technologies. Transaction reconciliation, a crucial aspect of ensuring the authenticity of payment-related activities, is also being revolutionized. This article delves into the transformative power of AI in enhancing reconciliation and improving transactional facilities. AI, through its automation of the reconciliation process, provides organizations with real-time reporting, identifies transaction failures and opportunities, and significantly increases productivity through effective time management. Recent developments in machine learning, particularly reinforcement learning, are enabling businesses to integrate these technologies seamlessly. The automation of tasks such as manual checking and comparing ledger transactions with balance sheets through AI is a game-changer, significantly improving efficiency.
1.1 Methodology Research methodology is essential for analyzing AI's role in transaction reconciliation. Primary and secondary research methods collect both numerical and qualitative data. Primary research gathers first-hand data, while secondary research uses existing data (Strijker et al. 2020 [7]). Both methods can be qualitative or quantitative. Qualitative methods collect non-numerical data like reports and case studies, while quantitative methods collect numerical data for predictions and pattern identification.
Key Words: Reinforcement learning, reconciliation, transaction, transaction management, Artificial intelligence
1. INTRODUCTION Transaction reconciliation is the process of verifying transaction data by comparing ledger entries to original entries to ensure document accuracy. AI has brought significant changes to the payment and settlement system (Izzo et al. 2022 [1]). In recent decades, information technology has optimized accounting methods and increased efficiency in continuous accounting. Digital transformation enables organizations to measure performance more effectively. AI's role in enhancing transaction reconciliation is crucial, impacting bank reconciliation, credit card reconciliation, intercompany reconciliation, and more (Petkov, 2020 [2]).
This research uses secondary qualitative methods to find insights and collect numerical data. The thematic analysis identifies patterns and categories from the data. The study explores various aspects of AI in transaction reconciliation using qualitative analysis. Secondary qualitative analysis is defined as collecting non-numeric data, such as categorical data from public opinions, surveys, interviews, and data from past studies. Extensive analysis of existing literature, various case studies, and interviews with the persons engaged with AI research have contributed to the depth of the research (Shah, 2023 [8]).
3. Research findings
The rise of online payment methods for regular transactions between banks and e-commerce businesses underscores the importance of accurate transaction reconciliation. AI plays a pivotal role in this, helping organizations maintain daily records and match them with original transactions. More importantly, machine learning algorithms enhance reconciliation techniques and serve as a robust defense against fraudulent transactions. With the development of technologies such as deep learning and artificial intelligence, businesses have automated processes, which are not only helping organizations with time management and optimization but also ensuring the security of financial transactions (Ravi, 2023 [4]).
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3.1 The Power of AI Technology in Business AI is crucial for technological revolutions, with its robust processing power allowing quick analysis of large data sets. The fourth industrial revolution relies on AI branches like deep learning, computer vision, neural networks, and pattern recognition to solve complex problems (Liu et al. 2020 [9]). Businesses use AI to automate repetitive tasks, enhance customer relationships, and manage transactions. AI's impact on the banking industry is significant, with predictions of generating 99 billion USD by 2030 through AI adoption (Chen et al. 2023 [10]).
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