CN116522102A - Method and device for dynamically adjusting accident risk model database based on privacy calculation - Google Patents
Method and device for dynamically adjusting accident risk model database based on privacy calculation Download PDFInfo
- Publication number
- CN116522102A CN116522102A CN202310560865.2A CN202310560865A CN116522102A CN 116522102 A CN116522102 A CN 116522102A CN 202310560865 A CN202310560865 A CN 202310560865A CN 116522102 A CN116522102 A CN 116522102A
- Authority
- CN
- China
- Prior art keywords
- accident risk
- variable information
- privacy
- dynamically adjusting
- tag library
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004364 calculation method Methods 0.000 title claims abstract description 36
- 238000000034 method Methods 0.000 title claims abstract description 27
- 230000001419 dependent effect Effects 0.000 claims abstract description 24
- 238000012217 deletion Methods 0.000 claims description 16
- 230000037430 deletion Effects 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 3
- 230000000875 corresponding effect Effects 0.000 description 5
- 208000014674 injury Diseases 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 208000027418 Wounds and injury Diseases 0.000 description 2
- 230000006378 damage Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 208000012260 Accidental injury Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 210000003462 vein Anatomy 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/211—Selection of the most significant subset of features
- G06F18/2113—Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- General Engineering & Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Health & Medical Sciences (AREA)
- Bioethics (AREA)
- General Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Economics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Game Theory and Decision Science (AREA)
- Computer Security & Cryptography (AREA)
- Computer Hardware Design (AREA)
- Medical Informatics (AREA)
- Technology Law (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The application discloses a method and a device for dynamically adjusting an accident risk model database based on privacy calculation, which relate to the technical field of databases and are used for acquiring accident risk modeling samples of insurance company clients; carrying out privacy intersection on the accident risk modeling sample to obtain an intersection sample; according to the label library of the intersection sample associated data source side, obtaining independent variable information and dependent variable information; calculating the average IV value of the tag library according to the independent variable information and the dependent variable information; and dynamically adjusting the ordering and pricing mechanism of the tag library according to the average IV value. According to the method and the device for dynamically adjusting the accident risk model database based on privacy calculation, under the condition that supervision is guaranteed, the tag library is dynamically optimized through the average IV value, so that the value of a data source is fully mined, safety and reliability are achieved, and the cost is low and the gain is high.
Description
Technical Field
The application relates to the technical field of databases, in particular to a method and a device for dynamically adjusting an accident risk model database based on privacy calculation.
Background
Accident insurance (accident insurance for short) is one of personal insurance business. The life insurance for paying insurance conditions is that the insured life is dead and disabled due to accidental injury. The basic content is as follows: the insurance applicant pays a certain premium to the insured, and if the insured suffers an unexpected injury during the insurance period and is directly or closely responsible for the death, disability, medical fee expenditure or temporary disability caused during a certain period from the date of the unexpected injury, the insured pays a certain amount of insurance policy to the insured or its beneficiary. The guarantee project has two items, namely death pay and disability pay.
With the continued development of society, the trend of insurance companies to construct pre-application wind control models based on external data sources has been developed. When the traditional insurance company seeks external data to gain own wind control model, a part of three-element encrypted labeled samples are provided for the data source, so that modeling engineers of the data source provide modeling services, a model which meets expectations is finally obtained, and finally, the data source is deployed to score to provide services for the insurance company in the form of api.
However, with the advent of data security laws and related personal privacy information protection related regulations, directly acquiring personal encryption information does not meet the requirements of future regulations, and considering product architecture, if data sources are not fully embodied by maintaining fixed data assets and providing sample modeling services, and the data sources lack insight into the dimensions of target customers' financial histories, shopping preferences, identity characteristics, and vein relationships.
Disclosure of Invention
Therefore, the application provides a method and a device for dynamically adjusting an accident risk model database based on privacy calculation, so as to solve the problems that a data source cannot fully embody the value of data when a wind control model is developed, the multi-dimensional insight capability is lacked and the safety is poor in the prior art.
In order to achieve the above object, the present application provides the following technical solutions:
in a first aspect, a method for dynamically adjusting an accident risk model database based on privacy calculations includes:
acquiring an accident risk modeling sample of an insurance company client;
carrying out privacy intersection on the accident risk modeling sample to obtain an intersection sample;
obtaining independent variable information and dependent variable information according to the label library of the intersection sample associated data source side;
calculating the average IV value of the tag library according to the independent variable information and the dependent variable information;
and dynamically adjusting the ordering and pricing mechanism of the tag library according to the average IV value.
Preferably, the accident risk modeling sample at least comprises a name, an identity card number, a mobile phone number and a backtracking time.
Preferably, a left association query is adopted when the tag library on the data source side is associated according to the intersection sample.
Preferably, the tag library comprises a warranty library, a shopping preference library, a reverse selection crowd library and a overdue library.
Preferably, when the sorting and pricing mechanism of the tag library is dynamically adjusted according to the average IV value, the tag library weight or exposure value corresponding to the larger average IV value is larger.
In a second aspect, a method for dynamically adjusting an accident risk model database based on privacy calculations includes:
acquiring an accident risk modeling sample of an insurance company client;
carrying out privacy intersection on the accident risk modeling sample to obtain an intersection sample;
obtaining independent variable information and dependent variable information according to the label library of the intersection sample associated data source side;
calculating the deletion rate of the tag library according to the independent variable information and the dependent variable information;
and dynamically adjusting the ordering and pricing mechanism of the tag library according to the deletion rate.
Preferably, when the ordering and pricing mechanism of the tag library is dynamically adjusted according to the deletion rate, the higher the deletion rate is, the smaller the tag library weight or exposure value corresponding to the deletion rate is.
In a third aspect, an apparatus for dynamically adjusting an accident risk model database based on privacy calculations, comprises:
the data uploading module is used for acquiring an accident risk modeling sample of an insurance company client;
the privacy intersection module is used for performing privacy intersection on the accident risk modeling sample to obtain an intersection sample;
the tag library processing module is used for obtaining independent variable information and dependent variable information according to the tag library of the intersection sample associated data source side;
the privacy statistics module is used for calculating the average IV value of the tag library according to the independent variable information and the dependent variable information;
and dynamically adjusting the ordering and pricing mechanism of the tag library according to the average IV value.
In a fourth aspect, a computer device comprises a memory storing a computer program and a processor implementing the steps of a method of dynamically adjusting an accident risk model database based on privacy calculations when the computer program is executed.
In a fifth aspect, a computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of a method of dynamically adjusting an accident risk model database based on privacy calculations.
Compared with the prior art, the application has the following beneficial effects:
the application provides a method and a device for dynamically adjusting an accident risk model database based on privacy calculation, which are implemented by acquiring an accident risk modeling sample of an insurance company client; carrying out privacy intersection on the accident risk modeling sample to obtain an intersection sample; according to the label library of the intersection sample associated data source side, obtaining independent variable information and dependent variable information; calculating the average IV value of the tag library according to the independent variable information and the dependent variable information; and dynamically adjusting the ordering and pricing mechanism of the tag library according to the average IV value. According to the method and the device for dynamically adjusting the accident risk model database based on privacy calculation, under the condition that supervision is guaranteed, the tag library is dynamically optimized through the average IV value, so that the value of a data source is fully mined, safety and reliability are achieved, and the cost is low and the gain is high.
Drawings
For a more visual illustration of the prior art and the present application, several exemplary drawings are presented below. It should be understood that the specific shape and configuration shown in the drawings should not be considered in general as limiting upon the practice of the present application; for example, based on the technical concepts and exemplary drawings disclosed herein, those skilled in the art have the ability to easily make conventional adjustments or further optimizations for the add/subtract/assign division, specific shapes, positional relationships, connection modes, dimensional scaling relationships, etc. of certain units (components).
FIG. 1 is a flowchart of a method for dynamically adjusting an accident risk model database based on privacy calculation according to an embodiment of the present application;
fig. 2 is a block diagram of a device for dynamically adjusting an accident risk model database based on privacy calculation according to a second embodiment of the present application.
Detailed Description
The present application is further described in detail below with reference to the attached drawings.
In the description of the present application: unless otherwise indicated, the meaning of "a plurality" is two or more. The terms "first," "second," "third," and the like in this application are intended to distinguish between the referenced objects without a special meaning in terms of technical connotation (e.g., should not be construed as emphasis on degree or order of importance, etc.). The expressions "comprising", "including", "having", etc. also mean "not limited to" (certain units, components, materials, steps, etc.).
The terms such as "upper", "lower", "left", "right", "middle", and the like, as referred to in this application, are generally used for convenience in visual understanding with reference to the drawings, and are not intended to be an absolute limitation of the positional relationship in actual products. Such changes in relative positional relationship are considered to be within the scope of the present description without departing from the technical concepts disclosed herein.
Example 1
Referring to fig. 1, the present embodiment provides a method for dynamically adjusting an accident risk model database based on privacy calculation, including:
s1: acquiring an accident risk modeling sample of an insurance company client;
specifically, the insurance company provides a batch of accident risk modeling samples M, where the accident risk modeling samples M are required to include encrypted three-element information of name, identification card number and mobile phone number, and include the application time as the starting point of the backtracking variable.
S2: carrying out privacy intersection on the accident risk modeling sample to obtain an intersection sample;
specifically, the privacy calculation refers to a technical set for realizing data analysis and calculation on the premise of protecting the data from external leakage, so as to achieve the purpose of being available and invisible to the data. The precondition of privacy exchange of the accident risk modeling sample M given by the insurance company is as follows: the nodes of privacy calculation are deployed locally by both parties, and an intersection sample M' is obtained after privacy intersection is carried out.
S3: according to the label library of the intersection sample associated data source side, obtaining independent variable information and dependent variable information;
specifically, the tag library is an initial library of accident risk modeling samples provided by customers, and variables in each library table can be assumed to have a certain correlation with the accident risk modeling samples, and particularly for accident risk scenes, the protection attribute, overdue attribute, whether the customer is an inverse selection crowd or not and the like in the accident risk modeling samples can be correlated with the dependent variable (y value) of the samples.
In this embodiment, for the obtained accident risk modeling sample, the data source side is initially configured with a tag library, where the tag library includes An A1 protection library, an A2 shopping preference library, an A3 reverse selection crowd library, ai. An, and the like, and each tag library includes a corresponding variable list (list). The step prepares for the subsequent calculation of the corresponding statistics by the y value of the left associated data source side, such as indexes of claims, adventure, etc., and the independent variable (x value).
S4: calculating the average IV value of the tag library according to the independent variable information and the dependent variable information;
specifically, the step S2 can accurately calculate the average IV value of each library by the information of the library variable (independent variable x) and the information of the risk and pay (dependent variable y) associated with the step.
IV (information value), i.e. information value, is an index for measuring the prediction capability of the features to the model, and is commonly used as a reference basis for feature screening before model training.
For example: for the A1 generation-keeping library, the average IV value of 5 variables (the user takes up 3/6/9/12/24 months as the number of the generation-keeping sales insurance sheets) in the generation-keeping library is assumed to be 0.2; for the A4 overdue library, assume that the average IV of 5 variables (the number of orders overdue by the user in the next 3/6/9/12/24 months) in the overdue library is 0.3; stock 10 variables for an#, and average IV value of 0.01; preferably, the weight or exposure value of the A4 overdue library can be maximized later, that is, the model is most likely to be selected by the client, and the weight can be reduced to be lower for the an# -library, so that the storage resource can be effectively saved.
S5: and dynamically adjusting the ordering and pricing mechanism of the tag library according to the average IV value.
Through the steps, correction can be continuously carried out after a plurality of batches of samples are provided by different insurance companies, so that database ordering can be continuously optimized according to the fact that IV values are deposited as ordering indexes. When the client models, the database variable with the front IV value can be selected as the preferred layer, part of variables with poor performance can be dynamically adjusted (after the variables are offset or directly removed) by the variables with good performance, resources are saved as a whole, and meanwhile, the number of the variables of the preferred layer can be used as the reference of the subsequent business bargaining, namely, the more the variables of the preferred layer are in the client model, the higher the charging possibility is.
According to the method for dynamically adjusting the accident risk model database based on the privacy calculation, the data is safer and more compliant through the privacy calculation, the sample provided by the insurance company client can just become the impetus for optimizing the iterative data system, the efficient dynamic operation of the data asset is realized, the data value is fully mined, the benefits and the cost are balanced, the disadvantages are converted into the iterative upgrade opportunities, and the security is high, the cost is low, and the gain is high.
Example two
The embodiment provides another method for dynamically adjusting an accident risk model database based on privacy calculation, which comprises the following steps:
s1: acquiring an accident risk modeling sample of an insurance company client;
s2: carrying out privacy intersection on the accident risk modeling sample to obtain an intersection sample;
s3: according to the label library of the intersection sample associated data source side, obtaining independent variable information and dependent variable information;
s4: calculating the deletion rate of the tag library according to the independent variable information and the dependent variable information;
s5: and dynamically adjusting the ordering and pricing mechanism of the tag library according to the deletion rate.
In this embodiment, a sorting and pricing mechanism of the tag library is dynamically adjusted by using a deletion rate, and when the sorting and pricing mechanism of the tag library is dynamically adjusted according to the deletion rate, the higher the deletion rate is, the smaller the tag library weight or exposure value corresponding to the higher the deletion rate is, that is, the higher the deletion rate is, the lower the probability of selection in customer modeling is.
Example III
Referring to fig. 2, an apparatus for dynamically adjusting an accident risk model database based on privacy calculation according to the present embodiment includes:
the data uploading module is used for acquiring an accident risk modeling sample of an insurance company client;
the privacy intersection module is used for performing privacy intersection on the accident risk modeling sample to obtain an intersection sample;
the tag library processing module is used for obtaining independent variable information and dependent variable information according to the tag library of the intersection sample associated data source side;
the privacy statistics module is used for calculating the average IV value of the tag library according to the independent variable information and the dependent variable information;
and dynamically adjusting the ordering and pricing mechanism of the tag library according to the average IV value.
Specific limitations regarding a means for dynamically adjusting the risk of accident model database based on privacy calculations can be found in the above description of a method for dynamically adjusting the risk of accident model database based on privacy calculations, and will not be described in detail herein.
Example IV
The embodiment provides a computer device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of a method for dynamically adjusting an accident risk model database based on privacy calculation when executing the computer program.
Example five
The present embodiment provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the step of dynamically adjusting an accident risk model database based on privacy calculations.
Any combination of the technical features of the above embodiments may be performed (as long as there is no contradiction between the combination of the technical features), and for brevity of description, all of the possible combinations of the technical features of the above embodiments are not described; these examples, which are not explicitly written, should also be considered as being within the scope of the present description.
The foregoing has outlined and detailed description of the present application in terms of the general description and embodiments. It should be appreciated that numerous conventional modifications and further innovations may be made to these specific embodiments, based on the technical concepts of the present application; but such conventional modifications and further innovations may be made without departing from the technical spirit of the present application, and such conventional modifications and further innovations are also intended to fall within the scope of the claims of the present application.
Claims (10)
1. A method for dynamically adjusting an accident risk model database based on privacy calculations, comprising:
acquiring an accident risk modeling sample of an insurance company client;
carrying out privacy intersection on the accident risk modeling sample to obtain an intersection sample;
obtaining independent variable information and dependent variable information according to the label library of the intersection sample associated data source side;
calculating the average IV value of the tag library according to the independent variable information and the dependent variable information;
and dynamically adjusting the ordering and pricing mechanism of the tag library according to the average IV value.
2. The method for dynamically adjusting an accident risk model database based on privacy calculations of claim 1, wherein the accident risk modeling samples include at least name, identification number, phone number, and backtracking time.
3. The method for dynamically adjusting an accident risk model database based on privacy calculations of claim 1, wherein a left-associated query is employed when associating a tag library on a data source side according to the intersection sample.
4. The method for dynamically adjusting an accident risk model database based on privacy calculations of claim 1, wherein the tag library comprises a warranty library, a shopping preference library, an inverse selection crowd library, and an overdue library.
5. The method for dynamically adjusting a risk of accident model database based on privacy calculations according to claim 1, wherein when the ordering and pricing mechanism of the tag library is dynamically adjusted according to the average IV value, the larger the average IV value is, the larger the tag library weight or exposure value corresponding to the average IV value is.
6. A method for dynamically adjusting an accident risk model database based on privacy calculations, comprising:
acquiring an accident risk modeling sample of an insurance company client;
carrying out privacy intersection on the accident risk modeling sample to obtain an intersection sample;
obtaining independent variable information and dependent variable information according to the label library of the intersection sample associated data source side;
calculating the deletion rate of the tag library according to the independent variable information and the dependent variable information;
and dynamically adjusting the ordering and pricing mechanism of the tag library according to the deletion rate.
7. The method for dynamically adjusting a risk of accident model database based on privacy calculations of claim 6, wherein when the ordering and pricing mechanism of the tag library is dynamically adjusted according to the deletion rate, the higher the deletion rate is, the smaller the tag library weight or exposure value is.
8. An apparatus for dynamically adjusting an accident risk model database based on privacy calculations, comprising:
the data uploading module is used for acquiring an accident risk modeling sample of an insurance company client;
the privacy intersection module is used for performing privacy intersection on the accident risk modeling sample to obtain an intersection sample;
the tag library processing module is used for obtaining independent variable information and dependent variable information according to the tag library of the intersection sample associated data source side;
the privacy statistics module is used for calculating the average IV value of the tag library according to the independent variable information and the dependent variable information;
and dynamically adjusting the ordering and pricing mechanism of the tag library according to the average IV value.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 5 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310560865.2A CN116522102A (en) | 2023-05-17 | 2023-05-17 | Method and device for dynamically adjusting accident risk model database based on privacy calculation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310560865.2A CN116522102A (en) | 2023-05-17 | 2023-05-17 | Method and device for dynamically adjusting accident risk model database based on privacy calculation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116522102A true CN116522102A (en) | 2023-08-01 |
Family
ID=87406348
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310560865.2A Pending CN116522102A (en) | 2023-05-17 | 2023-05-17 | Method and device for dynamically adjusting accident risk model database based on privacy calculation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116522102A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104834983A (en) * | 2014-12-25 | 2015-08-12 | 平安科技（深圳）有限公司 | Business data processing method and device |
| CN111144738A (en) * | 2019-12-24 | 2020-05-12 | 太平金融科技服务（上海）有限公司 | Information processing method, information processing device, computer equipment and storage medium |
| US20220255764A1 (en) * | 2021-02-06 | 2022-08-11 | SoterOne, Inc. | Federated learning platform and machine learning framework |
| CN115292750A (en) * | 2022-08-24 | 2022-11-04 | 上海阵方科技有限公司 | Privacy logistic regression method and system applied to financial scene |
| CN116049909A (en) * | 2023-01-28 | 2023-05-02 | 腾讯科技（深圳）有限公司 | Feature screening method, device, equipment and storage medium in federal feature engineering |
-
2023
- 2023-05-17 CN CN202310560865.2A patent/CN116522102A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104834983A (en) * | 2014-12-25 | 2015-08-12 | 平安科技（深圳）有限公司 | Business data processing method and device |
| CN111144738A (en) * | 2019-12-24 | 2020-05-12 | 太平金融科技服务（上海）有限公司 | Information processing method, information processing device, computer equipment and storage medium |
| US20220255764A1 (en) * | 2021-02-06 | 2022-08-11 | SoterOne, Inc. | Federated learning platform and machine learning framework |
| CN115292750A (en) * | 2022-08-24 | 2022-11-04 | 上海阵方科技有限公司 | Privacy logistic regression method and system applied to financial scene |
| CN116049909A (en) * | 2023-01-28 | 2023-05-02 | 腾讯科技（深圳）有限公司 | Feature screening method, device, equipment and storage medium in federal feature engineering |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11763027B1 (en) | Rules-based data access systems and methods | |
| US11907876B2 (en) | Autonomic discrete business activity management method | |
| US20220005125A1 (en) | Systems and methods for collecting and processing alternative data sources for risk analysis and insurance | |
| US11676087B2 (en) | Systems and methods for vulnerability assessment and remedy identification | |
| US20210357790A1 (en) | Agent aptitude prediction | |
| De‐Arteaga et al. | Algorithmic fairness in business analytics: Directions for research and practice | |
| US9075848B2 (en) | Methods, systems, and computer program products for generating data quality indicators for relationships in a database | |
| US20160232546A1 (en) | Computer processing of financial product information and information about consumers of financial products | |
| US20070294232A1 (en) | System and method for analyzing patent value | |
| US20120166228A1 (en) | Computer-implemented systems and methods for providing automobile insurance quotations | |
| US9275125B1 (en) | System for organizing data from a plurality of users to create individual user profiles | |
| US9495639B2 (en) | Determining document classification probabilistically through classification rule analysis | |
| US20030229553A1 (en) | Automated online underwriting | |
| CN108885631B (en) | Method and system for contract management in a data marketplace | |
| US20150026079A1 (en) | Systems and methods for determining packages of licensable assets | |
| CN113742492A (en) | Insurance scheme generation method and device, electronic equipment and storage medium | |
| JP2003196476A (en) | Security policy creation support system and security measure determination support system | |
| Verma | Application of Machine Learning for Fraud Detection–A Decision Support System in the Insurance Sector | |
| WO2019144035A1 (en) | Systems and methods for collecting and processing alternative data sources for risk analysis and insurance | |
| Agosto et al. | Bayesian learning models to measure the relative impact of ESG factors on credit ratings | |
| US20200013097A1 (en) | Connectivity Hub with Data-Hiding Features | |
| US11367146B2 (en) | Life insurance policy application process and system | |
| US20220164374A1 (en) | Method of scoring and valuing data for exchange | |
| CN116522102A (en) | Method and device for dynamically adjusting accident risk model database based on privacy calculation | |
| US20220058658A1 (en) | Method of scoring and valuing data for exchange |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |