Financial incoming money automatic matching and warehousing management system
By automatically parsing bank payment SMS messages and combining multilingual and fuzzy matching technologies, the problem of human error in financial payment management has been solved, enabling fast and accurate matching and secure storage of payment information, thereby improving the efficiency and security of corporate financial management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TIANJINCHUNPENGYUZHUANGLIGANGJIAOXIAN CO LTD
- Filing Date
- 2025-12-19
- Publication Date
- 2026-04-28
AI Technical Summary
In the traditional financial payment management process, finance personnel need to manually verify payment information, which leads to frequent human errors. In multinational business or multi-subsidiary scenarios, the complex formats and languages result in high manpower consumption and are prone to errors, affecting fund accounting and normal business operations.
The system employs an information collection module to automatically parse bank payment notification SMS messages, combines multilingual and fuzzy matching technologies with intelligent amount verification technology, utilizes a zero-trust architecture for encrypted storage, and introduces a self-learning module to optimize matching rules. Through parallel processing and artificial intelligence prediction models, it improves matching efficiency and accuracy.
It enables rapid and accurate matching of incoming payment information, reduces manual operations, lowers the error rate, ensures the accuracy of fund accounting and data security, and improves the system's adaptability and processing capabilities.
Smart Images
Figure CN121937236A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of financial management technology, and more specifically, to an automatic matching and warehousing management system for incoming financial payments. Background Technology In traditional financial payment management processes, companies often face numerous challenges. When receiving payments from customers, finance personnel need to manually verify the payment information, including the payer's name and the amount, and then match this information with the customer ledger in the sales system. However, corporate finance involves a large number of different types of customers and complex transaction situations. Especially when dealing with cross-border business or payments involving multiple subsidiaries, complex scenarios may arise, such as diverse payer name formats and different languages. This process not only consumes a lot of manpower and time but is also prone to human error, such as incorrect data entry or mismatch. Once an error occurs, it may lead to accounting chaos, affecting the company's accurate accounting of funds and normal business operations. In view of this, we propose an automatic financial payment matching and warehousing management system. Summary of the Invention
[0002] The purpose of this invention is to provide an automatic matching and warehousing management system for incoming financial payments, which aims to solve the problem that in the traditional financial payment management process, financial personnel need to manually verify incoming payment information, which is prone to human error.
[0003] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an automatic matching and warehousing management system for incoming financial payments, the system comprising an information collection module, a matching module, a warehousing management module, and a monitoring and early warning module; The information collection module collects bank payment notification SMS messages through an SMS forwarding APP installed on an Android phone. The mobile phone number registered in the APP corresponds to the payment notification number of the company's bank account. The information collection module is used to parse the SMS content, extract the payer's name and amount information, and determine whether the payer's name and amount information are complete and accurate. If the information is inconsistent, it is sent to the designated finance personnel for manual verification and adjustment. If the information is correct, it is transmitted to the matching module. The matching module is used to receive payment information from the information collection module. It uses multilingual and fuzzy matching technology and natural language processing technology to convert the payer's name in different languages into a standard format. It combines fuzzy matching algorithm with transaction history, related information factors and sales system account data for matching. If the matching is successful, the matching result is sent to the inventory management module. If the matching fails, the payment information is marked and pushed to the terminal device of the designated financial personnel for manual intervention. The database management module is used to receive matching success information from the matching module. It uses a zero-trust architecture combined with an encrypted database to store the payment information. Under the zero-trust architecture, the accessor's identity is confirmed through multi-factor authentication. Fine-grained authorization management is carried out according to user roles and operation permissions. User behavior and network traffic are continuously monitored. Once an anomaly is detected, access is immediately blocked and an alarm is issued. The monitoring and early warning module is used to monitor the operation status of each module in real time, count the number of matching failures and processing time indicators, and issue an early warning to relevant personnel when an abnormal situation occurs, such as the number of matching failures exceeding a preset threshold or the processing time exceeding a preset duration.
[0004] Preferably, in the multilingual and fuzzy matching technology, the fuzzy matching algorithm is based on the edit distance algorithm and combines the frequency of the payer's name in the transaction history with the relevance of the associated information to calculate the similarity.
[0005] Preferably, the matching module also employs intelligent amount verification technology, which determines the amount tolerance range based on historical transaction data and business scenarios, automatically verifies the incoming amount, and determines the amount tolerance range by analyzing the data of the last 5 historical transactions. This tolerance range is dynamically adjusted according to the business type.
[0006] Preferably, the matching module employs parallel processing technology, setting up multiple threads to process different payment information from the information collection module, while simultaneously reading individual account data from the sales system for matching, thereby improving matching efficiency. Furthermore, the matching module introduces an artificial intelligence prediction model to predict possible matching results in advance based on historical matching data and current business trends, further optimizing the matching process.
[0007] Preferably, in the zero-trust architecture of the inbound management module, multi-factor authentication includes a combination of password, fingerprint recognition and dynamic verification code verification methods, and fine-grained authorization management is set based on user roles and operation permissions, with different user roles having different access and operation permissions for incoming payment information.
[0008] Preferably, it also includes a self-learning module, which is based on machine learning algorithms and automatically adjusts the parameters of multilingual and fuzzy matching technologies and the tolerance range of intelligent amount verification technologies based on the results of each matching and data entry operation, thereby optimizing matching rules and amount verification strategies.
[0009] Preferably, the monitoring and early warning module provides early warning methods including but not limited to SMS notifications, in-system pop-up reminders, and email notifications, and sets different notification priorities for abnormal situations of different urgency levels.
[0010] Preferably, the encrypted database uses the national standard SM4 advanced encryption algorithm to encrypt and store incoming payment information, while updating the sales system's individual account data and generating inbound vouchers to record detailed information on incoming payments.
[0011] Compared with the prior art, the beneficial effects of the present invention are: 1. The information collection module in this invention automatically parses bank payment notification SMS messages, and the matching module uses multilingual and fuzzy matching technology, intelligent amount verification technology, combined with parallel processing and artificial intelligence prediction models to achieve fast and accurate matching of payment information with the sales system's individual accounts. This significantly reduces manual operations, avoids human error, improves matching accuracy, shortens processing time, ensures accurate corporate fund accounting, and guarantees normal business operations.
[0012] 2. The inbound management module in this invention adopts a zero-trust architecture combined with an encrypted database to store incoming payment information. The zero-trust architecture strictly controls data access and reduces the risk of data leakage through multi-factor authentication, fine-grained authorization management and real-time user behavior monitoring. The national cryptographic SM4 encryption algorithm is used to encrypt the incoming payment information, making it difficult to crack even if the data is obtained. This effectively protects the security and integrity of the company's financial data and safeguards the security of the company's financial information.
[0013] 3. By adding a self-learning module, this invention uses machine learning algorithms to automatically adjust the parameters of multilingual and fuzzy matching technologies and the tolerance range of intelligent amount verification technology based on the results of each matching and data entry operation. It optimizes matching rules and amount verification strategies. As business data accumulates, the system continuously learns and evolves, enabling it to better cope with diverse customer types and complex transaction scenarios in corporate financial work, improve system adaptability and processing capabilities, and continuously provide enterprises with efficient and accurate financial payment management services. Attached Figure Description
[0014] Figure 1 This is a schematic diagram of the system architecture in this invention; Figure 2 This is a schematic diagram of the system flow in this invention. Detailed Implementation
[0015] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Example
[0016] like Figure 1 As shown: An automatic matching and warehousing management system for incoming financial payments, the system includes an information collection module, a matching module, a warehousing management module, and a monitoring and early warning module; The information collection module collects bank payment notification SMS messages through an SMS forwarding APP installed on an Android phone. The mobile phone number registered in the APP corresponds to the payment notification number of the company's bank account. The information collection module is used to parse the SMS content, extract the payer's name and amount information, and determine whether the payer's name and amount information are complete and accurate. If the information is inconsistent, it is sent to the designated finance personnel for manual verification and adjustment. If the information is correct, it is transmitted to the matching module. The matching module is used to receive payment information from the information collection module. It uses multilingual and fuzzy matching technology and natural language processing technology to convert the payer's name in different languages into a standard format. It combines fuzzy matching algorithm with transaction history, related information factors and sales system account data for matching. If the matching is successful, the matching result is sent to the inventory management module. If the matching fails, the payment information is marked and pushed to the terminal device of the designated financial personnel for manual intervention. The database management module is used to receive matching success information from the matching module. It uses a zero-trust architecture combined with an encrypted database to store the payment information. Under the zero-trust architecture, the accessor's identity is confirmed through multi-factor authentication. Fine-grained authorization management is carried out according to user roles and operation permissions. User behavior and network traffic are continuously monitored. Once an anomaly is detected, access is immediately blocked and an alarm is issued. The monitoring and early warning module is used to monitor the operation status of each module in real time, count the number of matching failures and processing time indicators, and issue an early warning to relevant personnel when an abnormal situation occurs, such as the number of matching failures exceeding a preset threshold or the processing time exceeding a preset duration.
[0017] In the multilingual and fuzzy matching technology, the fuzzy matching algorithm is based on the edit distance algorithm and combines the frequency of the payer's name in the transaction history with the relevance of related information to calculate the similarity.
[0018] The matching module also employs intelligent amount verification technology, which determines the amount tolerance range based on historical transaction data and business scenarios, automatically verifies the incoming amount, and determines the amount tolerance range by analyzing the data of the last 5 historical transactions. This tolerance range is dynamically adjusted according to the business type.
[0019] The matching module employs parallel processing technology, setting up multiple threads to process different payment information from the information collection module, while simultaneously reading individual account data from the sales system for matching, thereby improving matching efficiency. Furthermore, the matching module introduces an artificial intelligence prediction model to predict possible matching results in advance based on historical matching data and current business trends, further optimizing the matching process.
[0020] In the zero-trust architecture of the inbound management module, multi-factor authentication includes a combination of password, fingerprint recognition and dynamic verification code verification methods. Fine-grained authorization management is set based on user roles and operation permissions, and users with different roles have different access and operation permissions for incoming payment information.
[0021] It also includes a self-learning module, which is based on machine learning algorithms and automatically adjusts the parameters of multilingual and fuzzy matching technologies and the tolerance range of intelligent amount verification technology based on the results of each matching and data entry operation, thereby optimizing matching rules and amount verification strategies.
[0022] The monitoring and early warning module provides early warning methods including but not limited to SMS notifications, in-system pop-up reminders, and email notifications, and sets different notification priorities for abnormal situations of different urgency levels.
[0023] The encrypted database uses the advanced national cryptographic algorithm SM4 to encrypt and store incoming payment information. At the same time, it updates the individual account data in the sales system and generates inbound vouchers to record detailed information about incoming payments.
[0024] Specifically, the SMS forwarding app installed on the Android phone receives bank payment notification SMS messages using the corresponding company bank account number. The information collection module parses the SMS messages, extracts the payer's name and amount information, and then judges the completeness and accuracy of this information. If there are any discrepancies, the information is sent to designated finance personnel for manual verification and adjustment. If the information is correct, it is transmitted to the matching module. The matching module receives incoming payment information from the information collection module. It employs multilingual and fuzzy matching technologies, using natural language processing to convert payer names from different languages into a standard format. Then, it combines this with a fuzzy matching algorithm based on edit distance and considering the frequency of payer names and the relevance of related information in the transaction history. This fuzzy matching is then matched against the sales system's ledger data. Simultaneously, intelligent amount verification technology is used to analyze the last five historical transaction data and business scenarios to determine the amount tolerance range and automatically verify the incoming payment amount. This tolerance range is dynamically adjusted according to the business type. During the matching process, parallel processing technology is used, with multiple threads processing different incoming payment information while simultaneously reading the sales system's ledger data, improving matching efficiency. Furthermore, the matching module utilizes an artificial intelligence prediction model to predict matching results in advance based on historical matching data and current business trends, optimizing the matching process. If the match is successful, the result is sent to the inventory management module; if the match fails, the incoming payment information is marked and pushed to the terminal device of a designated finance personnel for manual intervention. The inbound management module receives the matching success information from the matching module and stores the incoming payment information using a zero-trust architecture combined with an encrypted database. Under the zero-trust architecture, the accessor's identity is confirmed through a multi-factor authentication method that combines password, fingerprint recognition, and dynamic verification code. Fine-grained authorization management is carried out based on user roles and operation permissions. User behavior and network traffic are continuously monitored. Once an anomaly is detected, access is immediately blocked and an alarm is issued. The encrypted database uses the advanced national cryptographic SM4 encryption algorithm to encrypt and store the incoming payment information. At the same time, the sales system's account ledger data is updated, and an inbound voucher is generated to record the incoming payment information in detail. The monitoring and early warning module monitors the operation status of each module in the system in real time, and counts the number of matching failures and processing time. When the number of matching failures exceeds the preset threshold, such as when a certain number of consecutive matching failures are reached, or when the processing time exceeds the preset duration, or when the processing time for a single payment is too long, an abnormal situation is judged to have occurred. At this time, the monitoring and early warning module issues early warnings to relevant personnel through SMS notifications, in-system pop-up reminders, and email notifications, setting different priorities according to the urgency of the abnormality, so as to discover and solve problems in a timely manner. The self-learning module is based on machine learning algorithms. During system operation, it automatically adjusts the parameters of multilingual and fuzzy matching technologies based on the results of each matching and data entry operation, continuously optimizing matching rules and amount verification strategies. This enables the system to adapt to the complex and ever-changing business scenarios in corporate finance, thereby improving the system's performance and processing capabilities.
[0025] The embodiments disclosed in this invention are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of this invention based on the above embodiments and make different extensions and variations, but as long as they do not depart from the spirit of this invention, they are all within the protection scope of this invention.
Claims
1. An automatic financial payment matching and warehousing management system, characterized in that, The system includes an information collection module, a matching module, an inventory management module, and a monitoring and early warning module; The information collection module collects bank payment notification SMS messages through an SMS forwarding APP installed on an Android phone. The mobile phone number registered in the APP corresponds to the payment notification number of the company's bank account. The information collection module is used to parse the SMS content, extract the payer's name and amount information, and determine whether the payer's name and amount information are complete and accurate. If the information is inconsistent, it is sent to the designated finance personnel for manual verification and adjustment. If the information is correct, it is transmitted to the matching module. The matching module is used to receive payment information from the information collection module. It uses multilingual and fuzzy matching technology and natural language processing technology to convert the payer's name in different languages into a standard format. It combines fuzzy matching algorithm with transaction history, related information factors and sales system account data for matching. If the matching is successful, the matching result is sent to the inventory management module. If the matching fails, the payment information is marked and pushed to the terminal device of the designated financial personnel for manual intervention. The database management module is used to receive matching success information from the matching module. It uses a zero-trust architecture combined with an encrypted database to store the payment information. Under the zero-trust architecture, the accessor's identity is confirmed through multi-factor authentication. Fine-grained authorization management is carried out according to user roles and operation permissions. User behavior and network traffic are continuously monitored. Once an anomaly is detected, access is immediately blocked and an alarm is issued. The monitoring and early warning module is used to monitor the operation status of each module in real time, count the number of matching failures and processing time indicators, and issue an early warning to relevant personnel when an abnormal situation occurs, such as the number of matching failures exceeding a preset threshold or the processing time exceeding a preset duration.
2. The financial receipts automatic matching and warehousing management system according to claim 1, characterized in that, In the multilingual and fuzzy matching technology, the fuzzy matching algorithm is based on the edit distance algorithm and combines the frequency of the payer's name in the transaction history with the relevance of related information to calculate the similarity.
3. The financial receipts automatic matching and warehousing management system according to claim 1, characterized in that, The matching module also employs intelligent amount verification technology, which determines the amount tolerance range based on historical transaction data and business scenarios, automatically verifies the incoming amount, and determines the amount tolerance range by analyzing the data of the last 5 historical transactions. This tolerance range is dynamically adjusted according to the business type.
4. The financial receipts automatic matching and warehousing management system according to claim 1, characterized in that, The matching module employs parallel processing technology, setting up multiple threads to process different payment information from the information collection module, while simultaneously reading individual account data from the sales system for matching, thereby improving matching efficiency. Furthermore, the matching module introduces an artificial intelligence prediction model to predict possible matching results in advance based on historical matching data and current business trends, further optimizing the matching process.
5. The financial receipts automatic matching and warehousing management system according to claim 1, characterized in that, In the zero-trust architecture of the inbound management module, multi-factor authentication includes a combination of password, fingerprint recognition and dynamic verification code verification methods. Fine-grained authorization management is set based on user roles and operation permissions, and users with different roles have different access and operation permissions for incoming payment information.
6. The financial receipts automatic matching and warehousing management system according to claim 1, characterized in that, It also includes a self-learning module, which is based on machine learning algorithms and automatically adjusts the parameters of multilingual and fuzzy matching technologies and the tolerance range of intelligent amount verification technology based on the results of each matching and data entry operation, thereby optimizing matching rules and amount verification strategies.
7. The financial receipts automatic matching and warehousing management system according to claim 1, characterized in that, The monitoring and early warning module provides early warning methods including but not limited to SMS notifications, in-system pop-up reminders, and email notifications, and sets different notification priorities for abnormal situations of different urgency levels.
8. The financial receipts automatic matching and warehousing management system according to claim 1, characterized in that, The encrypted database uses the advanced national cryptographic algorithm SM4 to encrypt and store incoming payment information. At the same time, it updates the individual account data in the sales system and generates inbound vouchers to record detailed information about incoming payments.