Practical exploration of the integration of RPA and AI intelligent agents in the financial field

Vertical integration of technology stack

Front end interaction layer RPA+Back end decision layer AI intelligent agent

RPA is responsible for data collection and execution (such as logging into the system to export reports), while AI agents perform semantic parsing and strategy generation (such as identifying risk indicators from reports and generating response plans). For example, banks can use RPA to capture customer transaction data, and then have AI agents analyze abnormal patterns and trigger anti fraud processes.

AI intelligent agents empower RPA to quickly respond to changes in demand

For example, in the month end closing process, employees can input "add a provision for expense carryover starting next month" in natural language according to work needs, and the AI agent will automatically update the RPA rules after understanding.

Financial data processing and input process

In the process of financial data processing and input, the accuracy and timeliness of data are crucial. Many enterprises have adopted AI based automated data collection and input systems. These systems can automatically extract financial data from various data sources, such as enterprise business systems, electronic invoices, bank statements, etc., and organize and input it according to preset rules and formats.

Taking revenue recognition and measurement as an example, according to the new revenue standards, when financial personnel need to create revenue documents, they often need to check contracts, approval forms, etc. Whether the contract meets the corresponding internal control requirements (such as whether the double seal is complete) currently requires personnel to complete it, because the contract formats are diverse. At this point, AI contract agents can be used to extract contract texts with different formats into structured data through analysis of large language models, such as checking whether both parties have stamped, obtaining installment payment amounts, tax information, counterparty names, payment bank account information, etc. Transfer structured information to RPA for further processing, and drive the flow of revenue documents to the next stage (such as invoicing) in the ERP system. When RPA is combined with AI agents, it expands the application boundaries of RPA and breaks through people's fixed understanding of RPA rules.

Tax management process

In the tax management process, especially in cross-border taxation, complex international tax laws, exchange rate fluctuations, and multi country financial data need to be handled. Intelligent agents can use NLP technology and machine learning algorithms to understand complex tax rules, timely pay attention to changes in financial and tax policies of various countries, obtain tariff related information, and then use RPA to complete the filling and inspection of tax information.