Mobile payment risk early warning and response system

Abstract

The invention relates to the technical field of mobile payment risk early warning, and discloses a mobile payment risk early warning and response system. The system collects vehicle information, ETC card information, toll station information, traffic conditions, transaction time and place and user behavior data in real time through a data collection unit, and a data analysis unit calculates transaction frequency, total transaction amount, transaction amount fluctuation rate, user account activeness and card balance change rate according to the transmission data. The risk early warning unit receives the mobile payment risk score and judges whether the mobile payment risk score exceeds a payment risk threshold value, if yes, early warning is triggered, an early warning signal is generated, and after the response management unit receives the early warning signal, a response mechanism is automatically started, transaction compliance is manually audited, and the mobile payment risk score is comprehensively calculated. And a processing result is recorded in a risk database for future analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile payment risk early warning, and in particular to a mobile payment risk early warning and response system. Background Art

[0002] With the digital transformation of the transportation sector, the electronic toll collection system (ETC) has played an important role in improving traffic efficiency, reducing labor costs, and improving user experience. ETC payment uses automatic identification and wireless communication technology to quickly complete the payment of road tolls without stopping. However, with the widespread application of the ETC payment system, security risks and fraud have become increasingly prominent, including information leakage, counterfeit equipment, account theft, etc. There is an urgent need to establish an effective risk warning and response mechanism to ensure the safety of user funds and the normal operation of the transportation system. Summary of the invention

[0003] 1. Technical issues to be resolved In view of the shortcomings of the prior art, the present invention provides a mobile payment risk warning and response system, which has the function of collecting vehicle information, ETC card information, toll station information, traffic conditions, transaction time and location, and user behavior data in real time through a data collection unit, and calculating the transaction frequency based on the above transmission data by a data analysis unit. , Total Transaction Amount , Transaction Amount Volatility , User account activity and the card balance change rate The above values ​​are standardized and then the mobile payment risk score is calculated comprehensively. , the risk warning unit receives the mobile payment risk score , determine whether the payment risk threshold is exceeded. If exceeded, an early warning is triggered and an early warning signal is generated , the response management unit receives the warning signal After that, the response mechanism is automatically started, the transaction compliance is manually reviewed, and the processing results are recorded in the risk database for future analysis. Through the above system process, an effective mobile payment risk warning and response mechanism is established to ensure the safety of user funds, improve the operating efficiency of the transportation system, and solve the above problems.

[0004] (II) Technical solution To achieve the above-mentioned object, the present invention provides the following technical solutions: a mobile payment risk warning and response system, comprising a data collection unit, a data analysis unit, a risk warning unit and a response management unit; The data collection unit collects vehicle information, ETC card information, toll station information, traffic conditions, transaction time and location, and user behavior data in real time, and the vehicle information, ETC card information, toll station information, traffic conditions, transaction time and location, and user behavior data are transmitted to the data analysis unit via a wireless network; The data analysis unit calculates the transaction frequency according to the data transmitted by the data collection unit , Total Transaction Amount , Transaction Amount Volatility , User account activity and the card balance change rate , and the transaction frequency , Total Transaction Amount , Transaction Amount Volatility , User account activity and the card balance change rate Comprehensive calculation of mobile payment risk score after numerical standardization Send to risk warning unit; The risk warning unit receives a mobile payment risk score , determine whether the payment risk threshold is exceeded. If exceeded, an early warning is triggered and an early warning signal is generated transmitting to a response management unit; The response management unit receives the warning signal After that, the response mechanism is automatically activated, the transaction compliance is manually reviewed, and the processing results are recorded in the risk database for future analysis.

[0005] Preferably, the data analysis unit calculates the transaction frequency The formula is as follows: ; In the formula, represents the transaction frequency, Indicates the time period Total number of transactions within Represents the observed time period, and the formula reflects the trading activity by calculating the total number of transactions within a specific time period.

[0006] Preferably, the data analysis unit calculates the transaction amount The formula is as follows: ; In the formula, Indicates the total transaction amount. Indicates the total number of transactions in the time period. Indicates The amount of the transaction, Represents the number of recorded transactions. The formula gets the total transaction amount by summing up all transaction amounts within a specific time period.

[0007] Preferably, the data analysis unit calculates the transaction amount volatility The formula is as follows: ; In the formula, Indicates the volatility of transaction amount, Indicates the total number of transactions in the time period. Indicates The amount of the transaction, Indicates the number of recorded transactions. Represents the total transaction amount. This formula reflects the degree of dispersion of the transaction amount, that is, the deviation of each transaction amount from the average transaction amount. The greater the volatility, the greater the change in the transaction amount, and vice versa.

[0008] Preferably, the data analysis unit calculates the user account activity The formula is as follows: ; In the formula, Indicates the user account activity. represents the total number of transactions of the user during the observation period, represents the total transaction amount of the user during the observation period, It indicates the number of times the user logs in during the observation period. This formula takes into account the user's activity in multiple aspects. The more transactions, total transaction amounts and logins, the more active the user is. The higher the activity value, the more dependent the user is on the system and the lower the risk.

[0009] Preferably, the data analysis unit calculates the card balance change rate The formula is as follows: ; In the formula, Indicates the rate of change of card balance, represents the account balance at the end of the observation, It represents the account balance at the beginning of the observation. The formula calculates the percentage change of the account balance. When the rate of change of the balance is positive, it means that the account balance has increased, otherwise it means a decrease. When the balance decreases too much within a certain period of time, it means abnormal consumption behavior.

[0010] Preferably, the data analysis unit converts the transaction frequency , Total Transaction Amount , Transaction Amount Volatility , User account activity and the card balance change rate To perform numerical standardization, the formula is as follows: ; In the formula, Represents the standardized value. Within the range, represents the original value to be standardized, Indicates the minimum value of the original value to be standardized in all historical data. Indicates the maximum value of the original value to be standardized in all historical data.

[0011] Preferably, the data analysis unit calculates the mobile payment risk score The formula is as follows: ; In the formula, represents the mobile payment risk score, , , Represents the weights corresponding to the respective parameters. The above weight values ​​are reasonably allocated after regression statistical analysis of historical data to determine the relationship between each indicator and mobile payment risk.

[0012] Preferably, the risk warning unit receives a mobile payment risk score , to determine whether the payment risk threshold is exceeded. The judgment conditions are as follows: When mobile payment risk scores If it is greater than 0.5, it means that the mobile payment is in an abnormal risk state. At this time, the risk warning mechanism is triggered and the risk handling process begins.

[0013] Preferably, the risk database in the response management unit adopts Database to store data.

[0014] Compared with the prior art, the present invention provides a mobile payment risk warning and response system, which has the following beneficial effects: The present invention collects vehicle information, ETC card information, toll station information, traffic conditions, transaction time and location, and user behavior data in real time through the data collection unit, and the data analysis unit calculates the transaction frequency based on the above transmission data. , Total Transaction Amount , Transaction Amount Volatility , User account activity and the card balance change rate The above values ​​are standardized and then the mobile payment risk score is calculated comprehensively. , the risk warning unit receives the mobile payment risk score , determine whether the payment risk threshold is exceeded. If exceeded, an early warning is triggered and an early warning signal is generated , the response management unit receives the warning signal After that, the response mechanism is automatically started, the transaction compliance is manually reviewed, and the processing results are recorded in the risk database for future analysis. Through the above system process, an effective mobile payment risk warning and response mechanism is established to ensure the safety of user funds and improve the operating efficiency of the transportation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] In view of the widespread application of ETC payment system, security risks and fraud are becoming more and more prominent. Therefore, a mobile payment risk warning and response system is proposed. Figure 1 ,The system includes a data collection unit, a data analysis unit, a risk warning unit and a response management unit, wherein; The data collection unit uses advanced sensor technology, including high-precision GPS sensors, on-board cameras, RFID readers and writers, and environmental sensors to collect real-time vehicle information such as license plate number, vehicle model, speed, etc., ETC card information such as card number, balance, transaction records, etc., toll station information such as toll station location, toll standards, etc., traffic conditions such as real-time traffic flow, congestion, etc., transaction time and location such as the specific time and geographical location of the transaction, and user behavior data such as driving habits, payment frequency, etc. Among them, the high-precision GPS sensor is used to accurately obtain the vehicle location and speed, the on-board camera automatically reads the license plate information through image recognition technology, and the RFID reader is used to quickly identify and transmit ETC card information. In addition, the environmental sensor can monitor the surrounding traffic conditions, such as vehicle flow and speed, and these data are quickly transmitted to the data analysis unit through the efficient wireless transmission technology LoRa; The data analysis unit calculates the transaction frequency based on the above transmission data , Total Transaction Amount , Transaction Amount Volatility , User account activity and the card balance change rate The above values ​​are standardized and then the mobile payment risk score is calculated comprehensively. ,in: The formula for calculating transaction frequency is as follows: ; By analyzing the transaction frequency of users, the system can promptly identify abnormal behaviors of frequent transactions, such as a large number of small transactions in a short period of time, which may indicate that the user's account has been stolen or defrauded. In the formula, represents the transaction frequency, Indicates the time period Total number of transactions within represents the observed time period. The formula reflects the transaction activity by calculating the total number of transactions in a specific time period. Frequency analysis helps to understand users' daily payment habits and facilitates the formulation of personalized risk management strategies, such as providing additional security verification for users with high transaction frequency. The formula for calculating the total transaction amount is as follows: ; In the formula, Indicates the total transaction amount. Indicates the total number of transactions in the time period. Indicates The amount of the transaction, Indicates the number of recorded transactions. This formula obtains the total transaction amount by summing up all transaction amounts within a specific time period. Counting the total transaction amount can effectively monitor the user's consumption within a specific time period and identify abnormal large transactions so that risk control measures can be taken in a timely manner. The formula for calculating the volatility of transaction amount is as follows: ; In the formula, Indicates the volatility of transaction amount, Indicates the total number of transactions in the time period. Indicates The amount of the transaction, Indicates the number of recorded transactions. It represents the total transaction amount. This formula reflects the degree of dispersion of the transaction amount, that is, the deviation of each transaction amount from the average transaction amount. The greater the volatility, the greater the change in the transaction amount, and vice versa. Analyzing the volatility of the transaction amount can reveal abnormal fluctuations in user transaction behavior. Sudden and large fluctuations in the amount may indicate that the account has been attacked or the identity of the user is unknown. The formula for calculating user account activity is as follows: ; In the formula, Indicates the user account activity. represents the total number of transactions of the user during the observation period, represents the total transaction amount of the user during the observation period, Indicates the number of logins of the user during the observation period. This formula comprehensively considers the user's activity in multiple aspects. The more transactions, total transaction amount, and login times, the more active the user is. The higher the activity value, the higher the user's dependence on the system and the lower the risk. The formula for calculating the card balance change rate is as follows: ; Understanding the rate of change of user card balances helps to promptly identify account security issues, such as unknown consumption and abnormal balance changes, so as to quickly respond to potential fraud risks. In the formula, Indicates the rate of change of card balance, represents the account balance at the end of the observation, represents the account balance at the beginning of the observation. The formula calculates the change rate of the account balance. When the change rate of the balance is positive, it means that the account balance has increased, and vice versa. When the balance decreases too much within a certain period of time, it means abnormal consumption behavior; The formula for normalizing the values ​​is as follows: ; Standardization can convert data of different units and magnitudes into a unified standard so that they can be compared on the same scale, thereby eliminating the analysis errors caused by different dimensions. In the formula, Represents the standardized value. Within the range, represents the original value to be standardized, Indicates the minimum value of the original value to be standardized in all historical data. Indicates the maximum value of the original value to be standardized in all historical data. Through standardization, the impact of extreme values ​​on data analysis results can be reduced, making the analysis results more robust and reliable; The formula for calculating the mobile payment risk score is as follows: ; By comprehensively evaluating multiple factors such as user transaction behavior, account activity, device information, etc., risk scoring can identify potential risks in real time and improve the system's response speed. In the formula, represents the mobile payment risk score, , , Represents the weights corresponding to the respective parameters. The above weight values ​​are reasonably allocated after the relationship between each indicator and mobile payment risk is determined through regression statistical analysis of historical data. By calculating the risk score, abnormal transactions and potential fraudulent behaviors can be identified, account security can be improved, and financial losses can be reduced. For high-risk users or transactions, the system can enable stricter monitoring and verification measures to ensure the legitimacy of the transaction; The risk warning unit receives the mobile payment risk score , to determine whether the payment risk threshold is exceeded. The judgment conditions are as follows: When mobile payment risk scores If it is greater than 0.5, it means that the mobile payment is in an abnormal risk state, which triggers the risk warning mechanism and enters the risk handling process; When the response management unit receives an early warning signal After that, the response mechanism is automatically started, and an auxiliary decision-making model based on decision trees or deep learning is built to provide risk scoring and compliance recommendations, support manual auditors to quickly evaluate whether the transaction is legal, and improve audit efficiency. After the audit is completed, whether the transaction is approved or rejected, the audit results will be recorded in real time in the centralized risk database. Database snapshot technology is used to ensure data consistency and traceability, and metadata is attached to each transaction record, including audit time, auditor, decision basis and historical transaction information, to facilitate subsequent data analysis and audit needs. At the same time, chain data backup and high-availability database design are implemented to ensure the security and durability of risk data.

[0018] Through the above system process, an effective mobile payment risk warning and response mechanism has been established to ensure the safety of user funds and improve the operating efficiency of the transportation system.

[0019] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A mobile payment risk warning and response system, characterized by: It includes data collection unit, data analysis unit, risk warning unit and response management unit; The data collection unit collects vehicle information, ETC card information, toll station information, traffic conditions, transaction time and location, and user behavior data in real time, and the vehicle information, ETC card information, toll station information, traffic conditions, transaction time and location, and user behavior data are transmitted to the data analysis unit via a wireless network; The data analysis unit calculates the transaction frequency according to the data transmitted by the data collection unit , Total Transaction Amount , Transaction Amount Volatility , User account activity and the card balance change rate , and the transaction frequency , Total Transaction Amount , Transaction Amount Volatility , user account activity and card balance change rate Comprehensive calculation of mobile payment risk score after numerical standardization Send to risk warning unit; The risk warning unit receives a mobile payment risk score , determine whether the payment risk threshold is exceeded. If exceeded, an early warning is triggered and an early warning signal is generated transmitting to a response management unit; The response management unit receives the warning signal After that, the response mechanism is automatically activated, the transaction compliance is manually reviewed, and the processing results are recorded in the risk database for future analysis.

2. A mobile payment risk early warning and response system according to claim 1, characterized in that: The data analysis unit calculates the transaction frequency The formula is as follows: ； In the formula, represents the transaction frequency, Indicates the time period Total number of transactions within Represents the observed time period, and the formula reflects the trading activity by calculating the total number of transactions within a specific time period.

3. A mobile payment risk early warning and response system according to claim 2, characterized in that: The data analysis unit calculates the transaction amount The formula is as follows: ； In the formula, Indicates the total transaction amount. Indicates the total number of transactions in the time period. Indicates The amount of the transaction, Represents the number of recorded transactions. The formula calculates the total transaction amount by summing up all transaction amounts within a specific time period.

4. A mobile payment risk early warning and response system according to claim 3, characterized in that: The data analysis unit calculates the transaction amount volatility The formula is as follows: ； In the formula, Indicates the volatility of transaction amount, Indicates the total number of transactions in the time period. Indicates The amount of the transaction, Indicates the number of recorded transactions. Represents the total transaction amount. This formula reflects the degree of dispersion of the transaction amount, that is, the deviation of each transaction amount from the average transaction amount. The greater the volatility, the greater the change in the transaction amount, and vice versa.

5. A mobile payment risk early warning and response system according to claim 4, characterized in that: The data analysis unit calculates the user account activity The formula is as follows: ； In the formula, Indicates the user account activity. represents the total number of transactions of the user during the observation period, represents the total transaction amount of the user during the observation period, It indicates the number of times the user logs in during the observation period. This formula takes into account the user's activity in multiple aspects. The more transactions, total transaction amounts and logins, the more active the user is. The higher the activity value, the more dependent the user is on the system and the lower the risk.

6. A mobile payment risk early warning and response system according to claim 5, characterized in that: The data analysis unit calculates the card balance change rate The formula is as follows: ； In the formula, Indicates the rate of change of card balance, represents the account balance at the end of the observation, It represents the account balance at the beginning of the observation. The formula calculates the percentage change of the account balance. When the rate of change of the balance is positive, it means that the account balance has increased, otherwise it means a decrease. When the balance decreases too much within a certain period of time, it means abnormal consumption behavior.

7. A mobile payment risk early warning and response system according to claim 6, characterized in that: The data analysis unit calculates the transaction frequency , Total Transaction Amount , Transaction Amount Volatility , User account activity and the card balance change rate To perform numerical standardization, the formula is as follows: ； In the formula, Represents the standardized value. Within the range, represents the original value to be standardized, Indicates the minimum value of the original value to be standardized in all historical data. Indicates the maximum value of the original value to be standardized in all historical data.

8. A mobile payment risk early warning and response system according to claim 7, characterized in that: The data analysis unit calculates the mobile payment risk score The formula is as follows: ； In the formula, represents the mobile payment risk score, , , Represents the weights corresponding to the respective parameters. The above weight values ​​are reasonably allocated after regression statistical analysis of historical data to determine the relationship between each indicator and mobile payment risk.

9. A mobile payment risk early warning and response system according to claim 8, characterized in that: The risk warning unit receives a mobile payment risk score , to determine whether the payment risk threshold is exceeded. The judgment conditions are as follows: When mobile payment risk scores If it is greater than 0.5, it means that the mobile payment is in an abnormal risk state. At this time, the risk warning mechanism is triggered and the risk handling process begins.

10. A mobile payment risk early warning and response system according to claim 9, characterized in that: The risk database in the response management unit adopts Database to store data.

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