AI-driven multi-agent system for financial reconciliation

Abstract

AI-driven multi-agent system (100) for financial reconciliation, comprising: a data ingestion agent configured to collect financial data from various sources, including bank statements, enterprise resource planning (ERP) systems, and general ledgers; a transaction matching agent that uses artificial intelligence (AI) to identify and match relevant transactions based on predefined rules and machine learning models; an anomaly detection agent that uses machine learning algorithms to detect discrepancies, fraudulent activity, and missing transactions; an exception handling agent configured to categorize discrepancies, suggest corrective actions, and escalate unresolved exceptions for manual review; an agent to verify compliance with financial regulations, tax policies and accounting standards; an audit module that produces detailed, audit-ready reconciliation reports; a learning module that continuously improves system accuracy through adaptive AI-based learning models; a real-time processing engine that enables continuous transaction monitoring and reconciliation.

Description

[0001] The present invention relates to financial reconciliation systems and, in particular, to an AI-driven multi-agent system for automating and optimizing financial reconciliation processes.

[0002] Financial reconciliation is a critical process in corporate accounting, banking, and financial management, ensuring that transactions recorded across various accounts, ledgers, and financial systems are accurate and consistent. Traditional reconciliation methods rely heavily on manual processes, rule-based automation, and periodic audits, which are time-consuming, error-prone, and inefficient when handling large volumes of financial data. Given the increasing complexity of financial transactions, particularly in areas such as banking, fintech, corporate finance, and regulatory compliance, organizations face the challenge of identifying discrepancies, detecting fraud, and ensuring compliance with accounting standards.Existing solutions often involve rigid reconciliation frameworks that are difficult to adapt to dynamic transaction patterns, evolving regulatory requirements, and real-time processing needs.

[0003] Artificial intelligence (AI) and multi-agent systems offer promising advances in addressing these challenges. AI-driven reconciliation systems can intelligently analyze transaction records, detect anomalies, automate exception handling, and optimize reconciliation workflows without requiring extensive human intervention. Multi-agent architectures further improve system efficiency by distributing tasks among autonomous agents, each specialized in specific reconciliation functions such as data entry, error detection, pattern recognition, and audit reporting.

[0004] To solve this problem, the present invention provides an AI-driven multi-agent system for financial reconciliation.

[0005] The system aims to provide an advanced AI-driven multi-agent financial reconciliation framework that improves the accuracy, efficiency, and automation of financial data reconciliation processes.

[0006] The system uses advanced AI algorithms to identify discrepancies, detect fraudulent activity, and reduce false positives when reconciling financial data.

[0007] The system facilitates real-time reconciliation by continuously analyzing financial transactions to ensure up-to-date and accurate financial records.

[0008] The system can be scaled for enterprises, financial institutions and fintech applications and can be adapted to changing regulatory and business requirements.

[0009] The system has integrated monitoring of compliance with financial regulations, accounting standards and audit requirements.

[0010] The system automates the resolution of reconciliation exceptions by proposing corrective actions based on historical data and predefined business rules.

[0011] The system is designed to integrate with existing Enterprise Resource Planning (ERP) systems, banking platforms, and financial management software.

[0012] In one embodiment, the present invention provides an AI-driven multi-agent system for financial reconciliation. The present invention introduces an AI-driven multi-agent system for financial reconciliation designed to improve the accuracy, efficiency, and automation of financial data reconciliation across multiple accounts, ledgers, and transaction sources. Traditional reconciliation processes are often labor-intensive, error-prone, and inefficient, especially when processing large amounts of financial data. The proposed system overcomes these limitations by employing a multi-agent architecture in which autonomous AI-driven agents collaborate to validate transactions, identify discrepancies, and ensure financial compliance.

[0013] The system consists of multiple intelligent agents, each specialized in specific reconciliation tasks such as data entry, transaction matching, anomaly detection, exception handling, and compliance monitoring. These agents leverage machine learning (ML), natural language processing (NLP), and rule-based reasoning to analyze financial data in real time, identify inconsistencies, and suggest corrective actions. By distributing tasks across multiple agents, the system optimizes reconciliation workflows, significantly reducing manual intervention and processing time. One of the system's key features is its real-time reconciliation capability, which enables continuous monitoring of financial transactions and immediate detection of discrepancies or anomalies.This proactive approach minimizes financial risks, improves fraud detection, and ensures that financial records remain accurate and up-to-date. Furthermore, the system is designed to be scalable and adaptable, allowing it to handle high transaction volumes across corporations, banks, fintech platforms, and other financial institutions.

[0014] The invention is explained again below with reference to the figure. It shows: Fig. : an AI-driven multi-agent system (100) for financial reconciliation.

[0015] Fig.shows an AI-driven multi-agent system (100) for financial reconciliation. The system comprises an AI-driven multi-agent architecture designed to automate, optimize, and improve financial reconciliation processes. It consists of multiple intelligent agents, each responsible for different reconciliation functions such as data entry, transaction matching, anomaly detection, exception handling, compliance checking, and audit reporting. These agents work together and leverage machine learning (ML), natural language processing (NLP), and rule-based reasoning to analyze financial data, identify discrepancies, and ensure financial data accuracy. The system works by first ingesting financial data from various sources, including bank statements, enterprise resource planning (ERP) systems, accounting ledgers, and third-party financial platforms.A data preprocessing agent standardizes and cleanses this data, ensuring consistency and compatibility across different formats. Once the data is processed, a transaction matching agent uses AI algorithms to compare corresponding records and identify mismatched entries. The system supports multiple matching techniques, such as one-to-one, one-to-many, and many-to-many transaction matching, enabling it to handle complex financial workflows.

[0016] The system supports real-time reconciliation, allowing companies to continuously monitor financial transactions and detect errors as they occur. Its scalable and modular design enables integration with existing financial management tools, including banking systems, ERP platforms, and cloud-based accounting software. Furthermore, the system can be customized to meet the unique reconciliation needs of industries such as banking, fintech, insurance, and corporate finance. By leveraging AI-driven automation, multi-agent collaboration, and real-time data processing, the proposed system significantly reduces manual effort, improves reconciliation accuracy, and ensures compliance with financial standards.This innovative approach addresses the growing complexity of financial reconciliation and provides companies with a highly efficient, intelligent and adaptable solution to streamline their financial operations. List of reference symbols 100 systems

Claims

[1] AI-driven multi-agent system (100) for financial reconciliation, comprising: a data ingestion agent configured to collect financial data from various sources, including bank statements, enterprise resource planning (ERP) systems, and general ledgers; a transaction matching agent that uses artificial intelligence (AI) to identify and match relevant transactions based on predefined rules and machine learning models; an anomaly detection agent that uses machine learning algorithms to detect discrepancies, fraudulent activity, and missing transactions; an exception handling agent configured to categorize discrepancies, suggest corrective actions, and escalate unresolved exceptions for manual review; an agent to verify compliance with financial regulations, tax policies and accounting standards; an audit module that produces detailed, audit-ready reconciliation reports; a learning module that continuously improves system accuracy through adaptive AI-based learning models; a real-time processing engine that enables continuous transaction monitoring and reconciliation. [2] The system of claim 1, wherein the data entry agent preprocesses raw financial data to eliminate inconsistencies, standardize formats, and eliminate duplicate records. [3] The system of claim 1, wherein the transaction reconciliation agent supports one-to-one, one-to-many, and many-to-many reconciliation patterns to support various financial workflows. [4] The system of claim 1, wherein the anomaly detection agent uses deep learning techniques to detect patterns indicative of fraudulent activity. [5] The system of claim 1, wherein the exception handling agent uses natural language processing (NLP) to interpret financial data and generate contextual explanations for discrepancies. [6] The system of claim 1, wherein the compliance checking agent is dynamically updated to reflect changes in financial regulations and industry standards. [7] The system of claim 1, wherein the audit reporting module generates visual dashboards and interactive reports for real-time financial insights. [8] The system of claim 1, wherein the learning module uses reinforcement learning to optimize the accuracy of the matching over time.

Images