System and method for bank data identity authentication and matching

Abstract

The invention discloses a system and method for bank data identity authentication and matching, relates to the technical field of data processing, improves the accuracy and efficiency of identity authentication and data matching and reduces financial risks through integration and comparison of multi-source data, and comprises a data collection module for collecting basic information of customers; the data preprocessing module is used for cleaning and formatting the collected data; the preliminary identity authentication module is used for performing preliminary comparison according to the customer name and the identity card number; the multi-factor identity authentication module is used for further comparing the mobile phone number and the address information of the client; the data matching module is used for comparing and matching customer data in the business system; and the result feedback and storage module is used for feeding back authentication and matching results and storing related data, and through integration and comparison of multi-source data, the accuracy and efficiency of identity authentication and data matching are improved, and financial risks are remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a system and method for bank data identity authentication and matching. Background Art

[0002] Existing technologies present numerous challenges for banks in credit approval and preventing financial fraud. Traditional methods rely primarily on manual comparisons and isolated data verification across systems, which is inefficient and prone to high error rates. With the increasing volume of data and the diversification of fraud methods, efficient and accurate processing of customer information, identity authentication, and data matching have become critical challenges that banks urgently need to address. Summary of the Invention

[0003] To solve the above technical problems, the present invention provides a system for bank data identity authentication and matching, which improves the accuracy and efficiency of identity authentication and data matching and reduces financial risks through the integration and comparison of multi-source data. The system includes a data collection module for collecting basic information of customers; a data preprocessing module for cleaning and formatting the collected data; a preliminary identity authentication module for performing a preliminary comparison based on the customer's name and ID number; a multi-factor identity authentication module for further comparing the customer's mobile phone number and address information; a data matching module for comparing and matching customer data in the business system; and a result feedback and storage module for feeding back authentication and matching results and storing related data.

[0004] Preferably, the data collection module is used to collect basic information of customers from various data sources.

[0005] Preferably, the data preprocessing module is used to clean and format the collected data.

[0006] Preferably: the preliminary identity authentication module is used to perform a preliminary comparison based on the customer's name and ID number, and if there is a mismatch, mark the record as mismatched and output it.

[0007] Preferably, the multi-factor identity authentication module is used to further compare the customer's mobile phone number and address information on the basis of passing the preliminary identity authentication, and pass the identity authentication when all factors match, and mark it as doubtful if there are mismatching factors and further manual review.

[0008] Preferably, the data matching module is used to compare and match customer data in the business system, and merge customer data when there is a matching record, and store the customer data as a new record in the system if there is no match.

[0009] Preferably, the result feedback and storage module is used to feedback authentication and matching results and store relevant data.

[0010] Preferably, a risk analysis module is also included to perform risk analysis on authentication and matching results and identify potential risk points and fraudulent behaviors.

[0011] A method for bank data identity authentication and matching, comprising the following steps: collecting basic customer information; cleaning and formatting the collected data; performing a preliminary comparison based on the customer's name and ID number; further comparing the customer's mobile phone number and address information; comparing and matching the customer data in the business system; feeding back the authentication and matching results, and storing the relevant data.

[0012] Preferably, after comparing and matching the customer data, the method further comprises the step of performing risk analysis on the authentication and matching results to identify potential risk points and fraudulent behaviors.

[0013] The technical effects and advantages of the present invention are as follows:

[0014] 1. Improve the accuracy and efficiency of identity authentication and data matching through the integration and comparison of multi-source data, and significantly reduce financial risks.

[0015] 2. Clean and format the collected data to remove duplicate, invalid or erroneous data to ensure data quality and consistency, remove duplicate, invalid or erroneous data such as null values ​​and outliers, etc. Data formatting: convert the data into a unified format to facilitate subsequent processing and analysis.

[0016] 3. Use the name and ID number for preliminary comparison to quickly filter out records that are obviously inconsistent and reduce the burden of subsequent processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Schematic diagram of a system for bank data identity authentication and matching provided in an embodiment of the present application;

[0018] Figure 2 This is a flowchart of a system for bank data identity authentication and matching provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for purposes of illustration and description and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described to better illustrate the principles of the invention and its practical application, and to enable those skilled in the art to understand the invention and design various embodiments with various modifications suitable for specific applications.

[0020] See also Figure 1As shown, in this embodiment, a system for bank data identity authentication and matching is provided, including a data collection module for collecting basic information of customers; a data preprocessing module for cleaning and formatting the collected data; a preliminary identity authentication module for performing a preliminary comparison based on the customer's name and ID number; a multi-factor identity authentication module for further comparing the customer's mobile phone number and address information; a data matching module for comparing and matching customer data in the business system; and a result feedback and storage module for feeding back authentication and matching results and storing related data.

[0021] Furthermore, the data collection module is used to collect basic information of customers from various data sources.

[0022] Specifically, basic customer information is collected from various data sources (such as the bank's internal system, third-party credit reporting agencies, etc.), including but not limited to name, ID number, mobile phone number, address, etc.

[0023] In addition, data sources are diverse, including but not limited to internal bank systems, third-party credit reporting agencies, etc. During the data collection process, the legality and compliance of the data must be ensured to avoid infringing on customer privacy.

[0024] Furthermore, the data preprocessing module is used to clean and format the collected data.

[0025] Specifically, the collected data is cleaned and formatted to remove duplicate, invalid or erroneous data to ensure data quality and consistency.

[0026] Data cleaning: remove duplicate, invalid or erroneous data, such as null values ​​and outliers, etc. Data formatting: convert data into a unified format for subsequent processing and analysis.

[0027] Furthermore, the preliminary identity authentication module is used to perform a preliminary comparison based on the customer's name and ID number, and if there is a mismatch, the record is marked as mismatched and output.

[0028] Specifically, a preliminary comparison is performed using the name and ID number. If a match is found, the process continues to the next step. If not, the record is marked as mismatched and output. This step aims to quickly filter out records with obvious discrepancies and reduce the burden of subsequent processing.

[0029] Utilize efficient matching algorithms to quickly compare names and ID numbers. The matching results must be accurate and reliable to avoid misjudgments and missed detections.

[0030] Furthermore, the multi-factor identity authentication module is used to further compare the customer's mobile phone number and address information based on the initial identity authentication, and pass the identity authentication when all factors match. If there are mismatching factors, it is marked as suspicious and further manually reviewed.

[0031] Specifically, after initial identity verification is successful, the mobile phone number and address information are further compared. If all factors match, the identity verification is successful. If any mismatches occur, the record is marked as questionable and requires further manual review. This step enhances the accuracy and security of identity verification. The comparison process must consider the timeliness and accuracy of the data to avoid misjudgments due to untimely or erroneous data updates.

[0032] Furthermore, the data matching module is used to compare and match customer data in the business system, and merge the customer data when there is a matching record, and store it as a new record in the system if there is no match.

[0033] Specifically, customer data that has passed identity authentication is further matched across various business systems. If a matching record exists in the business system, the customer data is merged with the existing record; if not, the customer data is stored in the system as a new record. This step helps consolidate customer data and improves data consistency and availability. Data integrity and consistency must be considered during the matching process to avoid matching failures due to missing or inconsistent data.

[0034] Furthermore, the result feedback and storage module is used to feed back authentication and matching results and store relevant data.

[0035] Specifically, the authentication and matching results will be promptly fed back to the client and relevant departments so that appropriate measures can be taken in a timely manner. At the same time, the authentication results and data matching information will be stored in the system for subsequent analysis and risk control, providing strong support for subsequent analysis and risk control.

[0036] Furthermore, it also includes a risk analysis module for performing risk analysis on authentication and matching results to identify potential risk points and fraudulent behaviors.

[0037] Specifically, risk analysis is conducted on the authentication and matching results to identify potential risk points and fraudulent activities. This step helps banks promptly detect and respond to potential risks, ensure fund security, identify potential risk points and fraudulent activities, and provide early warning and decision-making support for banks.

[0038] A method for bank data identity authentication and matching, comprising the following steps: collecting basic customer information; cleaning and formatting the collected data; performing a preliminary comparison based on the customer's name and ID number; further comparing the customer's mobile phone number and address information; comparing and matching the customer data in the business system; feeding back the authentication and matching results, and storing the relevant data.

[0039] Furthermore, after comparing and matching the customer data, the steps are further included: performing risk analysis on the authentication and matching results to identify potential risk points and fraudulent behaviors.

[0040] A method for bank data identity authentication and matching, comprising the following steps: collecting basic customer information; cleaning and formatting the collected data; performing a preliminary comparison based on the customer's name and ID number; further comparing the customer's mobile phone number and address information; comparing and matching the customer data in the business system; feeding back the authentication and matching results, and storing the relevant data.

[0041] Furthermore, after comparing and matching customer data, risk analysis is performed on the authentication and matching results to identify potential risk points and fraudulent behaviors.

[0042] The specific steps are as follows:

[0043] Start: trigger the process;

[0044] The bank feeds back the reconciliation data, which the reconciler compiles into a standard template;

[0045] Upload the reconciliation template data (hereinafter referred to as bank data) and select relevant elements (bank, branch, and reconciliation month). Once all selections are complete, import the data. The system filters the data in the CRM (hereinafter referred to as system data). The system analyzes whether the name field (bank data) contains * or x. If not, a direct match is performed based on the name field. Check whether the name field can match the system data. If so, but there are duplicates with the same name, match all system IDs and notes in the remarks column (full name matches, but there are duplicates, please manually identify them and assign priority 1). If so, but there is a unique match, match the system ID and notes in the remarks column (full name unique match successful, priority 1). If not, note the remarks column (full name no match, priority 1).

[0046] If the system analyzes (bank data) that the name field contains * or x, it will determine whether the bank data (ID number) is empty. If so, it will determine whether the bank data (mobile phone number) is empty. If so, the structure will be output according to the priority order (high priority results will cover low priority results). If there is no match, it will prompt "Insufficient elements, unable to match, please review manually."

[0047] If the system analysis (bank data) shows that the name field contains * or x, and the bank data (ID number) is not empty, then concatenate the name and ID number fields in the bank data and calculate the validity score of the concatenated field (score = 2 * number of Chinese characters ∧ 2 + number * 1); determine whether the validity score is greater than or equal to 11. If not, then determine whether the bank data (mobile phone number) is empty; continue with the above steps;

[0048] If the validity score is greater than 11, analyze the system data (name, ID number) fields, convert the non-empty ID number fields to the same format as the bank data and connect them; match the (name or ID number) field to determine whether the system data can be matched; if not, make a note in the remarks column (no matching data for name or ID number, priority 6); if and there is duplicate data in the field, match all system IDs and remarks in the remarks column (name or ID number matches, but there are duplicate data, please manually identify, priority 4); if and there is a unique match, match the system ID and remarks in the remarks column (name or ID number is the only match, priority 2), and then determine whether the bank data (mobile phone number) is empty; if not, connect the (name and mobile phone number) fields in the bank data, calculate the validity score of the connected field (score = 2*number of Chinese characters∧2+number*1), and then determine whether the validity score is greater than or equal to 7. If not, output the structure according to the priority order (high priority results cover low priority results). If there is no match, the prompt "Insufficient elements, unable to match, please manually review" will be displayed;

[0049] If the validity score is greater than 7, analyze the system data (name, ID number) fields, convert the non-empty ID number fields to the same format as the bank data and connect them; then match according to the (name or ID number) field to determine whether the system data can be matched; if not, make a note in the remarks column (no matching data for the name or ID number, priority 7); if yes and there is duplicate data in the field, match all system IDs and remarks in the remarks column (the name or ID number matches, but there is duplicate data, please manually identify, priority 5); if yes and it is a unique match, match the system ID and remarks in the remarks column (the name or ID number is the only match, priority 3), and then output the results in order of priority (high priority results cover low priority results). If there is no match, the prompt "Insufficient elements, unable to match, please manually review" will be displayed.

[0050] Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without making creative efforts should fall within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described and explained in the present invention shall be implemented in accordance with conventional means in the field unless otherwise specified or limited.

Claims

1. A system for bank data identity authentication and matching, characterized in that: It includes data collection module, data preprocessing module, preliminary identity authentication module, multi-factor identity authentication module, data matching module, and result feedback and storage module.

2. A system for bank data identity authentication and matching according to claim 1, characterized in that: The data collection module is used to collect basic information of customers from various data sources.

3. A system for bank data identity authentication and matching according to claim 1, characterized in that: The data preprocessing module is used to clean and format the collected data.

4. A system for bank data identity authentication and matching according to claim 1, characterized in that: The preliminary identity authentication module is used to perform a preliminary comparison based on the customer's name and ID number, and if there is a mismatch, mark the record as mismatched and output it.

5. A system for bank data identity authentication and matching according to claim 1, characterized in that: The multi-factor identity authentication module is used to further compare the customer's mobile phone number and address information on the basis of passing the initial identity authentication, and pass the identity authentication when all factors match. If there are mismatching factors, it is marked as suspicious and further manually reviewed.

6. A system for bank data identity authentication and matching according to claim 1, characterized in that: The data matching module is used to compare and match customer data in the business system and merge customer data when there is a matching record. If there is no match, the customer data is stored in the system as a new record.

7. A system for bank data identity authentication and matching according to claim 1, characterized in that: The result feedback and storage module is used to feed back authentication and matching results and store relevant data.

8. A system for bank data identity authentication and matching according to claim 1, characterized in that: It also includes a risk analysis module for performing risk analysis on authentication and matching results to identify potential risk points and fraudulent behaviors.

9. The method for bank data identity authentication and matching according to any one of claims 1 to 8, characterized in that: The following steps are involved: Collect basic customer information; clean and format the collected data; perform a preliminary comparison based on the customer's name and ID number; further compare the customer's mobile phone number and address information; compare and match customer data in the business system; feedback authentication and matching results, and store relevant data.

10. A method for bank data identity authentication and matching according to claim 9, characterized in that: After comparing and matching customer data, further steps are included: risk analysis of authentication and matching results to identify potential risk points and fraudulent behaviors.

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