CN111461905A - Vehicle insurance fraud and claim evasion method and device, computer equipment and storage medium - Google Patents
Vehicle insurance fraud and claim evasion method and device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN111461905A CN111461905A CN202010468178.4A CN202010468178A CN111461905A CN 111461905 A CN111461905 A CN 111461905A CN 202010468178 A CN202010468178 A CN 202010468178A CN 111461905 A CN111461905 A CN 111461905A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- user
- historical
- photo
- photos
- 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
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000012015 optical character recognition Methods 0.000 claims abstract description 14
- 238000004590 computer program Methods 0.000 claims description 16
- 230000011218 segmentation Effects 0.000 claims description 7
- 238000011156 evaluation Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 238000000605 extraction Methods 0.000 claims description 4
- 238000012549 training Methods 0.000 claims description 4
- 239000000203 mixture Substances 0.000 claims 1
- 239000002994 raw material Substances 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000001603 reducing effect Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
- G06F16/5846—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using extracted text
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
- G06V10/751—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/14—Image acquisition
- G06V30/148—Segmentation of character regions
- G06V30/153—Segmentation of character regions using recognition of characters or words
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Multimedia (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Business, Economics & Management (AREA)
- Library & Information Science (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Databases & Information Systems (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Artificial Intelligence (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Technology Law (AREA)
- General Business, Economics & Management (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention provides a method, a device, computer equipment and a storage medium for avoiding vehicle insurance claims cheating, wherein the method comprises the steps of obtaining a certificate photo, a license plate photo and a damaged part photo of a vehicle uploaded by a user; identifying certificate photos and license plate photos uploaded by a user through OCR (optical character recognition), and acquiring user information; according to the vehicle damaged part photos, matching historical vehicle damaged part photos with the most similar similarity from historical claim cases of insurance companies and historical claim cases of the same industry collected by a big data layer through a picture retrieval matching algorithm to identify the vehicle damaged condition marked in the cases; calculating the fraud risk level of the user according to the user information and the damage condition of the vehicle in the case; and if the fraud risk level of the user is higher than the preset value, directly rejecting the claim or going through a manual claim settlement channel according to the corresponding level, and adding the fraud user into the black list library. The invention can effectively avoid the risk of cheating claims of car owners and reduce the operation cost.
Description
Technical Field
The invention relates to a method and a device for avoiding claims from cheating, computer equipment and a storage medium, in particular to a method and a device for avoiding claims from cheating on vehicle insurance, computer equipment and a storage medium.
Background
At present, the situations of multiple insurance mergence claim settlement and porcelain-hitting fraud claim of many car owners generally exist, so that the car insurance operation faces a great bottleneck in controlling the claim rate, on one hand, the company needs to reduce the claim rate and reduce the non-compliant claim settlement requests, and on the other hand, the company needs to control the labor cost well. Most of the current insurance company's claim settlement modes need manual participation, and more manual work needs to be invested in the aspects of controlling fraud and the like to judge, so that two major goals of operation are opposite in the present situation, and the two major goals are considered.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: a vehicle insurance claims cheating and evading method, device, computer equipment and storage medium are provided, aiming at avoiding the claims cheating risk of vehicle owners.
In order to solve the technical problems, the invention adopts the technical scheme that: a vehicle insurance fraud claim evasion method comprises the steps of,
s20, acquiring a certificate photo, a license plate photo and a damaged part photo of the vehicle uploaded by a user;
s30, recognizing the certificate photo and the license plate photo uploaded by the user through OCR, and acquiring user information;
s40, matching historical vehicle damaged part photos with the most similar similarity from historical claim cases of insurance companies and historical claim cases of the same industry collected from a big data layer through a picture retrieval matching algorithm according to the vehicle damaged part photos to identify the vehicle damaged condition marked in the cases;
s50, calculating the fraud risk level of the user according to the user information and the damage condition of the vehicle in the case;
and S60, if the fraud risk level of the user is higher than the preset value, directly rejecting the claim or going through a manual claim settlement channel according to the corresponding level, and adding the fraudulent user into the black list library.
Further, the step S30 includes,
s31, recognizing character areas in the certificate photo and the license plate photo uploaded by the user, and picking out the character areas;
s32, carrying out rectangular segmentation on the character area, and splitting the character area into different characters;
s33, performing character prediction on the split character according to a supervision algorithm;
and S34, recognizing the whole character according to the predicted character.
Further, the step S31 specifically includes,
traversing the whole photo through a sliding window algorithm, judging by the characteristics of the supervised label training sample, finding a character area, and performing rectangular extraction on the character area.
Further, the step S32 specifically includes,
and performing rectangular segmentation on the character area, performing one-dimensional sliding window movement in a rectangle, judging the character interval, and splitting the character.
Further, the vehicle insurance fraud claim evasion method also comprises the steps of,
and S10, constructing a claim settlement data set according to the historical claim settlement cases of the insurance company and the historical claim settlement cases of the same industry through an AI clustering algorithm.
Further, in step S40, the image retrieval matching algorithm is a grayscale-based template matching algorithm, and the specific process of matching is,
s41, taking the photo of the damaged part of the vehicle as an image template, and traversing all sub-images from the historical claim settlement cases of the insurance company and the historical claim settlement cases of the same industry collected from the big data layer;
and S42, matching the damaged part photo of the vehicle with all the traversed sub-images one by one, and taking the sub-image with the closest similarity as the damaged condition of the vehicle in the case.
Further, the algorithm formula of the template matching algorithm based on the gray scale is as follows:
the invention also provides a vehicle insurance claims cheating and avoiding device, which comprises,
the photo acquisition module is used for acquiring certificate photos, license plate photos and damaged part photos of the vehicle uploaded by a user;
the information identification module is used for identifying certificate photos and license plate photos uploaded by a user through an OCR (optical character recognition) module and acquiring user information;
the damage assessment module is used for matching historical damaged vehicle part photos with the closest similarity from historical claim cases of insurance companies and historical claim cases of the same industry collected from a big data layer through a picture retrieval matching algorithm according to the damaged vehicle part photos so as to identify the damaged condition of the vehicle winning the bid in the case;
the risk evaluation module is used for calculating the fraud risk level of the user according to the user information and the damage condition of the vehicle in the case;
and the risk processing module is used for directly rejecting the claims or removing the manual claims channel according to the corresponding grade when the fraud risk grade of the user is higher than the preset value.
The invention also provides computer equipment which comprises a memory and a processor, wherein the memory is stored with a computer program, and the processor executes the computer program to realize the vehicle insurance fraud and claim evasion method.
The present invention also provides a storage medium storing a computer program which, when executed by a processor, can implement the vehicle insurance claims cheating and evading method as described above.
The invention has the beneficial effects that: the certificate photos and the license plate photos uploaded by a user are identified through OCR, information of the user is obtained, the photos of the damaged parts of the vehicles are collected through a big data layer, historical claim settlement cases of insurance companies and historical claim settlement cases of the same industry are matched, the closest photos of the damaged parts of the historical vehicles are matched and used as the conditions of damage of the vehicles marked in the cases, the risk of cheating the users is calculated, the high risk of cheating is directly rejected or a manual claim settlement channel is passed, high odds rate caused by cheating is reduced, the loss reducing effect is achieved, the cheating users are added into a black name list library, and the grading precision of a customer management system is improved.
Drawings
The following detailed description of the invention refers to the accompanying drawings.
FIG. 1 is a flowchart of a vehicle insurance fraud claim evasion method according to an embodiment of the present invention;
FIG. 2 is a flow chart of text message identification according to an embodiment of the present invention;
FIG. 3 is a block diagram of a vehicle insurance fraud claim evasion apparatus according to an embodiment of the present invention;
FIG. 4 is a block diagram of an information recognition module according to an embodiment of the present invention;
FIG. 5 is a schematic block diagram of a computer device in accordance with one embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As shown in fig. 1, the first embodiment of the present invention is: a vehicle insurance fraud claim evasion method comprises the steps of,
s20, acquiring a certificate photo, a license plate photo and a damaged part photo of the vehicle uploaded by a user;
s30, recognizing the certificate photo and the license plate photo uploaded by the user through OCR, and acquiring user information;
as shown in fig. 2, the step S30 includes,
s31, recognizing character areas in the certificate photo and the license plate photo uploaded by the user, and picking out the character areas;
s32, carrying out rectangular segmentation on the character area, and splitting the character area into different characters;
s33, performing character prediction on the split character according to a supervision algorithm;
and S34, recognizing the whole character according to the predicted character.
Further, the step S31 specifically includes,
traversing the whole photo through a sliding window algorithm, judging by the characteristics of the supervised label training sample, finding a character area, and performing rectangular extraction on the character area.
Further, the step S32 specifically includes,
and performing rectangular segmentation on the character area, performing one-dimensional sliding window movement in a rectangle, judging the character interval, and splitting the character.
S40, matching historical vehicle damaged part photos with the most similar similarity from historical claim cases of insurance companies and historical claim cases of the same industry collected from a big data layer through a picture retrieval matching algorithm according to the vehicle damaged part photos to identify the vehicle damaged condition marked in the cases;
further, in step S40, the image retrieval matching algorithm is a grayscale-based template matching algorithm, and the specific process of matching is,
s41, taking the photo of the damaged part of the vehicle as an image template, and traversing all sub-images from the historical claim settlement cases of the insurance company and the historical claim settlement cases of the same industry collected from the big data layer;
and S42, matching the damaged part photo of the vehicle with all the traversed sub-images one by one, and taking the sub-image with the closest similarity as the damaged condition of the vehicle in the case.
The big data layer is based on a hadoop system, and is used for constructing storage systems such as a file storage HDFS (Hadoop distributed file system) and structured data HIVE (high-level hierarchy), HBASE (hybrid hierarchical ordered hierarchy) and the like.
Further, the algorithm formula of the template matching algorithm based on the gray scale is as follows:
a template matching algorithm based on gray scale: template Matching (Blocking Matching) is based on finding a sub-image similar to a template image into another image from a known template image. The gray-based matching algorithm, also called correlation matching algorithm, uses a spatial two-dimensional sliding template for matching.
In the search graph, taking an MxN size sub-graph with (i, j) as the upper left corner, and calculating the similarity of the sub-graph and the template; and traversing the whole search graph, and finding out the subgraph which is most similar to the template graph from all the accessible subgraphs as a final matching result.
Obviously, the smaller the average absolute difference D (i, j), the more similar it indicates, so the smallest D (i, j) is only needed to determine the position of the subgraph that can be matched.
S50, calculating the fraud risk level of the user according to the user information and the damage condition of the vehicle in the case; the database stores historical risk records of the user and credit evaluation information of the user, the historical risk records of the user and the credit evaluation information of the user, and the fraud risk level of the current risk of the user is given through an AI algorithm.
And S60, if the fraud risk level of the user is higher than the preset value, directly rejecting the claim or going through a manual claim settlement channel according to the corresponding level, and adding the fraudulent user into the black list library.
For the high-risk claim case, whether fraud is existed is judged through the artificial passage, and for the user with fraud history, when the photo information of the damaged part of the vehicle of the claim case is the same as or similar to the picture of the historical fraud, the possibility of fraud of the user is very high.
The beneficial effect of this embodiment lies in: the method comprises the steps of recognizing certificate photos and license plate photos uploaded by a user through OCR (optical character recognition), acquiring information of the user, collecting historical claim cases of an insurance company and historical claim cases of the same industry through a big data layer to match the photos of the damaged parts of vehicles, matching the photos of the damaged parts of the closest historical vehicles to serve as the damaged conditions of the vehicles marked in the cases, calculating the risk of cheating of the user, directly rejecting the cases with high risk of cheating or walking away a manual claim channel, so that high odds rate caused by cheating is reduced, the loss reducing effect is achieved, the cheating user is added into a black name list library, the grading precision of a customer management system is improved, cheating is replaced by AI (artificial identification), the recognition rate of medium-sized and small cases can be up to over 90%, meanwhile, manual investment is reduced, and the operation cost is reduced.
In one embodiment, the vehicle insurance fraud claim evasion method further comprises the steps of,
and S10, constructing a claim settlement data set according to the historical claim settlement cases of the insurance company and the historical claim settlement cases of the same industry through an AI clustering algorithm.
And the big data layer collects historical claim settlement cases of insurance companies and historical claim settlement cases of the same industry as a data base stone of AI intelligent operation.
AI clustering algorithm: centroid-based algorithms that update the centroid candidate as the mean of the points within the sliding window by updating the centroid candidate to locate the centroid for each group or class. These candidate sliding windows are then filtered at a post-processing stage to reduce the number of adjacent repeat points, resulting in a collection of center points and their corresponding combinations.
As shown in fig. 3, another embodiment of the present invention is: a vehicle insurance fraud claim evasion device comprises,
the photo acquisition module 20 is used for acquiring certificate photos, license plate photos and damaged part photos of the vehicle uploaded by a user;
the information identification module 30 is used for identifying the certificate photo and the license plate photo uploaded by the user through an OCR (optical character recognition), and acquiring user information;
the damage assessment module 40 is used for matching the historical damaged vehicle part photos with the closest similarity from the historical claim cases of the insurance company and the historical claim cases of the same industry collected by the big data layer through a picture retrieval matching algorithm according to the damaged vehicle part photos so as to identify the damaged condition of the vehicle marked in the case;
the risk evaluation module 50 is used for calculating the fraud risk level of the user according to the user information and the damage condition of the vehicle in the case;
and the risk processing module 60 is used for directly rejecting the claims or removing the manual claims channel according to the corresponding grade when the fraud risk grade of the user is higher than the preset value.
As shown in fig. 4, the information recognition module 30 includes,
the character region extracting unit 31 is used for identifying character regions in the certificate photo and the license plate photo uploaded by the user and extracting the character regions;
a character splitting unit 32, configured to split the text region into different characters by rectangular division;
a character prediction unit 33, configured to perform character prediction on the split character according to a supervision algorithm;
and a complete character recognition unit 34 for recognizing the entire character based on the predicted character.
Further, the text area extracting unit 31 is specifically configured to,
traversing the whole photo through a sliding window algorithm, judging by the characteristics of the supervised label training sample, finding a character area, and performing rectangular extraction on the character area.
Further, the character splitting unit 32 is specifically configured to,
and performing rectangular segmentation on the character area, performing one-dimensional sliding window movement in a rectangle, judging the character interval, and splitting the character.
Further, the vehicle insurance fraud claim evasion device also comprises,
and the claim data set construction module is used for constructing a claim data set according to the historical claim case of the insurance company and the historical claim case of the same industry through an AI clustering algorithm.
Further, in the loss assessment module 40, the image retrieval matching algorithm is a grayscale-based template matching algorithm, and the loss assessment module includes,
the traversing unit is used for traversing all sub-images from the historical claim settlement cases of insurance companies and the historical claim settlement cases of the same industry collected by the big data layer by taking the photos of the damaged parts of the vehicles as image templates;
and the matching unit is used for matching the picture of the damaged part of the vehicle with all the traversed sub-images one by one, and taking the sub-image with the closest similarity as the condition that the vehicle in the case is damaged.
It should be noted that, as will be clear to those skilled in the art, the specific implementation processes of the aforementioned vehicle insurance claim deception and avoidance apparatus and each unit may refer to the corresponding descriptions in the foregoing method embodiments, and for convenience and brevity of description, no further description is provided herein.
The above-described car insurance fraud evasion apparatus may be embodied in the form of a computer program which is executable on a computer device as shown in fig. 5.
Referring to fig. 5, fig. 5 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a terminal or a server, where the terminal may be an electronic device with a communication function, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, and a wearable device. The server may be an independent server or a server cluster composed of a plurality of servers.
Referring to fig. 5, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer programs 5032 include program instructions that, when executed, cause the processor 502 to perform a vehicle insurance claims fraud avoidance method.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the execution of the computer program 5032 in the non-volatile storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 can be caused to execute a vehicle insurance fraud evasion method.
The network interface 505 is used for network communication with other devices. Those skilled in the art will appreciate that the configuration shown in fig. 5 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation of the computer device 500 to which the present application may be applied, and that a particular computer device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
Wherein the processor 502 is configured to run a computer program 5032 stored in the memory to implement the vehicle insurance fraud evasion method as described above.
It should be understood that, in the embodiment of the present Application, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It will be understood by those skilled in the art that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program instructing associated hardware. The computer program includes program instructions, and the computer program may be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.
Accordingly, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program comprises program instructions. The program instructions, when executed by a processor, cause the processor to perform the vehicle insurance fraud evasion method as described above.
The storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, which can store various computer readable storage media.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.
The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the invention can be merged, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a terminal, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A vehicle insurance claims cheating and evading method is characterized in that: comprises the steps of (a) carrying out,
s20, acquiring a certificate photo, a license plate photo and a damaged part photo of the vehicle uploaded by a user;
s30, recognizing the certificate photo and the license plate photo uploaded by the user through OCR, and acquiring user information;
s40, matching historical vehicle damaged part photos with the most similar similarity from historical claim cases of insurance companies and historical claim cases of the same industry collected from a big data layer through a picture retrieval matching algorithm according to the vehicle damaged part photos to identify the vehicle damaged condition marked in the cases;
s50, calculating the fraud risk level of the user according to the user information and the damage condition of the vehicle in the case;
and S60, if the fraud risk level of the user is higher than the preset value, directly rejecting the claim or going through a manual claim settlement channel according to the corresponding level, and adding the fraudulent user into the black list library.
2. The vehicle insurance fraud claim evasion method of claim 1, wherein: the step S30 includes the steps of,
s31, recognizing character areas in the certificate photo and the license plate photo uploaded by the user, and picking out the character areas;
s32, carrying out rectangular segmentation on the character area, and splitting the character area into different characters;
s33, performing character prediction on the split character according to a supervision algorithm;
and S34, recognizing the whole character according to the predicted character.
3. The vehicle insurance fraud claim evasion method of claim 2, wherein: the step S31 specifically includes the steps of,
traversing the whole photo through a sliding window algorithm, judging by the characteristics of the supervised label training sample, finding a character area, and performing rectangular extraction on the character area.
4. The vehicle insurance fraud claim evasion method of claim 3, wherein: the step S32 specifically includes the steps of,
and performing rectangular segmentation on the character area, performing one-dimensional sliding window movement in a rectangle, judging the character interval, and splitting the character.
5. The vehicle insurance fraud claim evasion method of claim 1, wherein: the vehicle insurance fraud claim evasion method further comprises the steps of,
and S10, constructing a claim settlement data set according to the historical claim settlement cases of the insurance company and the historical claim settlement cases of the same industry through an AI clustering algorithm.
6. The vehicle insurance fraud claim evasion method of claim 1, wherein: in step S40, the image retrieval matching algorithm is a grayscale-based template matching algorithm, and the specific matching process is,
s41, taking the photo of the damaged part of the vehicle as an image template, and traversing all sub-images from the historical claim settlement cases of the insurance company and the historical claim settlement cases of the same industry collected from the big data layer;
and S42, matching the damaged part photo of the vehicle with all the traversed sub-images one by one, and taking the sub-image with the closest similarity as the damaged condition of the vehicle in the case.
7. The vehicle insurance fraud claim evasion method of claim 6, wherein: the algorithm formula of the template matching algorithm based on the gray level is as follows:
8. a vehicle insurance cheating claim evasion device is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the photo acquisition module is used for acquiring certificate photos, license plate photos and damaged part photos of the vehicle uploaded by a user;
the information identification module is used for identifying certificate photos and license plate photos uploaded by a user through an OCR (optical character recognition) module and acquiring user information;
the damage assessment module is used for matching historical damaged vehicle part photos with the closest similarity from historical claim cases of insurance companies and historical claim cases of the same industry collected from a big data layer through a picture retrieval matching algorithm according to the damaged vehicle part photos so as to identify the damaged condition of the vehicle winning the bid in the case;
the risk evaluation module is used for calculating the fraud risk level of the user according to the user information and the damage condition of the vehicle in the case;
and the risk processing module is used for directly rejecting the claims or removing the manual claims channel according to the corresponding grade when the fraud risk grade of the user is higher than the preset value.
9. A computer device, characterized by: the computer device comprises a memory having stored thereon a computer program and a processor that, when executed, implements the vehicle insurance fraud claim evasion method of any one of claims 1 to 7.
10. A storage medium, characterized by: the storage medium stores a computer program which, when executed by a processor, can implement the vehicle insurance claims deception and evasion method as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010468178.4A CN111461905A (en) | 2020-05-28 | 2020-05-28 | Vehicle insurance fraud and claim evasion method and device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010468178.4A CN111461905A (en) | 2020-05-28 | 2020-05-28 | Vehicle insurance fraud and claim evasion method and device, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111461905A true CN111461905A (en) | 2020-07-28 |
Family
ID=71684848
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010468178.4A Pending CN111461905A (en) | 2020-05-28 | 2020-05-28 | Vehicle insurance fraud and claim evasion method and device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111461905A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111914771A (en) * | 2020-08-06 | 2020-11-10 | 长沙公信诚丰信息技术服务有限公司 | Automatic certificate information comparison method and device, computer equipment and storage medium |
| CN114187476A (en) * | 2021-12-14 | 2022-03-15 | 中国平安财产保险股份有限公司 | Vehicle insurance information checking method, device, equipment and medium based on image analysis |
| WO2023000737A1 (en) * | 2021-07-23 | 2023-01-26 | 明觉科技（北京）有限公司 | Vehicle accident loss assessment method and apparatus |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102855298A (en) * | 2012-08-16 | 2013-01-02 | 熊兵 | Image retrieval method and system |
| CN107679996A (en) * | 2017-10-10 | 2018-02-09 | 民太安财产保险公估股份有限公司 | Vehicle insurance Claims Resolution method and system |
| CN108280769A (en) * | 2018-02-01 | 2018-07-13 | 阿里巴巴集团控股有限公司 | The data processing method of Claims Resolution business, device, electronic equipment, server |
| CN108573286A (en) * | 2018-05-10 | 2018-09-25 | 阿里巴巴集团控股有限公司 | A kind of data processing method, device, equipment and the server of Claims Resolution business |
| CN109191312A (en) * | 2018-08-07 | 2019-01-11 | 阳光财产保险股份有限公司 | A kind of anti-fraud air control method and device of Claims Resolution |
| CN109919783A (en) * | 2019-01-31 | 2019-06-21 | 德联易控科技(北京)有限公司 | Risk Identification Method, device, equipment and the storage medium of vehicle insurance Claims Resolution case |
| CN110009508A (en) * | 2018-12-25 | 2019-07-12 | 阿里巴巴集团控股有限公司 | A kind of vehicle insurance compensates method and system automatically |
| CN110443718A (en) * | 2019-07-17 | 2019-11-12 | 卓尔智联(武汉)研究院有限公司 | Vehicle insurance Claims Resolution method, computer installation and readable storage medium storing program for executing |
| CN110689440A (en) * | 2019-08-13 | 2020-01-14 | 平安科技（深圳）有限公司 | Vehicle insurance claim settlement identification method and device based on image identification, computer equipment and storage medium |
| CN110689443A (en) * | 2019-09-02 | 2020-01-14 | 中国平安财产保险股份有限公司 | Insurance data processing method and device, storage medium and server |
-
2020
- 2020-05-28 CN CN202010468178.4A patent/CN111461905A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102855298A (en) * | 2012-08-16 | 2013-01-02 | 熊兵 | Image retrieval method and system |
| CN107679996A (en) * | 2017-10-10 | 2018-02-09 | 民太安财产保险公估股份有限公司 | Vehicle insurance Claims Resolution method and system |
| CN108280769A (en) * | 2018-02-01 | 2018-07-13 | 阿里巴巴集团控股有限公司 | The data processing method of Claims Resolution business, device, electronic equipment, server |
| CN108573286A (en) * | 2018-05-10 | 2018-09-25 | 阿里巴巴集团控股有限公司 | A kind of data processing method, device, equipment and the server of Claims Resolution business |
| CN109191312A (en) * | 2018-08-07 | 2019-01-11 | 阳光财产保险股份有限公司 | A kind of anti-fraud air control method and device of Claims Resolution |
| CN110009508A (en) * | 2018-12-25 | 2019-07-12 | 阿里巴巴集团控股有限公司 | A kind of vehicle insurance compensates method and system automatically |
| CN109919783A (en) * | 2019-01-31 | 2019-06-21 | 德联易控科技(北京)有限公司 | Risk Identification Method, device, equipment and the storage medium of vehicle insurance Claims Resolution case |
| CN110443718A (en) * | 2019-07-17 | 2019-11-12 | 卓尔智联(武汉)研究院有限公司 | Vehicle insurance Claims Resolution method, computer installation and readable storage medium storing program for executing |
| CN110689440A (en) * | 2019-08-13 | 2020-01-14 | 平安科技（深圳）有限公司 | Vehicle insurance claim settlement identification method and device based on image identification, computer equipment and storage medium |
| CN110689443A (en) * | 2019-09-02 | 2020-01-14 | 中国平安财产保险股份有限公司 | Insurance data processing method and device, storage medium and server |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111914771A (en) * | 2020-08-06 | 2020-11-10 | 长沙公信诚丰信息技术服务有限公司 | Automatic certificate information comparison method and device, computer equipment and storage medium |
| WO2023000737A1 (en) * | 2021-07-23 | 2023-01-26 | 明觉科技（北京）有限公司 | Vehicle accident loss assessment method and apparatus |
| CN114187476A (en) * | 2021-12-14 | 2022-03-15 | 中国平安财产保险股份有限公司 | Vehicle insurance information checking method, device, equipment and medium based on image analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hoang et al. | Image processing–based classification of asphalt pavement cracks using support vector machine optimized by artificial bee colony | |
| WO2017148377A1 (en) | Automatic extraction method, device and system for driving licence expiration date, and storage medium | |
| Wang et al. | An effective method for plate number recognition | |
| CN111461905A (en) | Vehicle insurance fraud and claim evasion method and device, computer equipment and storage medium | |
| CN106469181B (en) | User behavior pattern analysis method and device | |
| Molina-Moreno et al. | Efficient scale-adaptive license plate detection system | |
| US8027978B2 (en) | Image search method, apparatus, and program | |
| US10354134B1 (en) | Feature classification with spatial analysis | |
| CN112651996B (en) | Target detection tracking method, device, electronic equipment and storage medium | |
| CN110852311A (en) | Three-dimensional human hand key point positioning method and device | |
| CN109376717A (en) | Personal identification method, device, electronic equipment and the storage medium of face comparison | |
| CN112749653A (en) | Pedestrian detection method, device, electronic equipment and storage medium | |
| CN109784171A (en) | Car damage identification method for screening images, device, readable storage medium storing program for executing and server | |
| CN110852327A (en) | Image processing method, image processing device, electronic equipment and storage medium | |
| Seetharaman et al. | Statistical distributional approach for scale and rotation invariant color image retrieval using multivariate parametric tests and orthogonality condition | |
| CN112926601A (en) | Image recognition method, device and equipment based on deep learning and storage medium | |
| CN113971821A (en) | Driver information determination method and device, terminal device and storage medium | |
| Povolotskiy et al. | Dynamic programming approach to template-based OCR | |
| Ganapathy et al. | A Malaysian vehicle license plate localization and recognition system | |
| CN114494994A (en) | Vehicle abnormal aggregation monitoring method and device, computer equipment and storage medium | |
| Rodriguez‐Lozano et al. | Efficient data dimensionality reduction method for improving road crack classification algorithms | |
| CN112101139B (en) | Human shape detection method, device, equipment and storage medium | |
| CN111127407B (en) | Fourier transform-based style migration forged image detection device and method | |
| Ghandour et al. | Building shadow detection based on multi-thresholding segmentation | |
| CN111753722B (en) | Fingerprint identification method and device based on feature point type |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200728 |
|
| RJ01 | Rejection of invention patent application after publication |