CN111369139A - Individual credit risk assessment method, system, terminal and storage medium - Google Patents
Individual credit risk assessment method, system, terminal and storage medium Download PDFInfo
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- 238000012502 risk assessment Methods 0.000 title claims abstract description 30
- 238000000034 method Methods 0.000 title claims description 28
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- 238000004422 calculation algorithm Methods 0.000 claims abstract description 28
- 238000011156 evaluation Methods 0.000 claims abstract description 4
- 239000011159 matrix material Substances 0.000 claims description 14
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- 238000004590 computer program Methods 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 7
- 238000012163 sequencing technique Methods 0.000 claims description 5
- 238000005307 time correlation function Methods 0.000 claims description 2
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000005295 random walk Methods 0.000 description 4
- 230000000644 propagated effect Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 1
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Abstract
The invention discloses an individual credit evaluation method, which is based on the information of a relationship network and adverse events of a user; establishing an assumed condition, setting the risk weight of the user node, and acquiring the risk weight of the user nodeuConnected to each othernA set of other user nodes; analyzing and processing the risk weight of the node of the user with the adverse credit event by using a time function, and transmitting the risk weight to the node connected with the current node of the user; the personalized PageRank algorithm is improved, all nodes are traversed through the algorithm, and the risk weight of the adverse credit events of all nodes in the relational network is calculated; and sorting according to the risk weight to obtain a user risk sorting table based on the influence of the adverse credit events. By the scheme, individual credit risk assessment data of the user during data application and release can be acquired more accurately, and leakage risk of data release is reduced. The scheme also discloses an individual credit risk assessment system, a terminal and a storage medium, and the system has the beneficial effects.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to an individual credit risk assessment method, a system, a terminal and a storage medium.
Background
The credit of the individual user can change according to the events related to the individual, if the individual has an adverse credit event or is influenced by an adverse credit event, the credit of the individual user can be reduced, otherwise, the credit of the individual user can be increased, and the influence of the adverse credit event on the individual can be gradually reduced along with the increase of the time.
The traditional individual credit risk assessment method adopts the risk of the user calculated by selecting a PageRank algorithm. The basic idea of the traditional PageRank algorithm is as follows: in a directed graph, a user starts to visit from any node, when jumping to the next node, the user randomly selects the next visiting node from all directed edges of the current node by using a probability c, or jumps to any node and starts a new round of random walk by using the probability of (1-c), the above processes are repeated until the probability that the user stays at any node is stable, and the more nodes point to the node p for one node p, the greater the weight of the node p, namely:
r＝(1-c)Mr+cu
wherein r is a PageRank value, which represents the probability that the node is visited, c is the probability of restarting random walk, u is the probability of being selected when the node restarts random walk, in the PageRank, the probability of being selected by each node is equal, and M is a normalized adjacency matrix.
However, the traditional PageRank algorithm does not conform to the actual situation, and in an actual use scene, when each node in the network restarts random walk, the selected probability is different, but based on the preference of the user, the algorithm has a certain bias. Therefore, the PageRank algorithm is improved later, and a personalized PageRank algorithm is provided.
Compared with the traditional PageRank algorithm, the personalized PageRank algorithm is improved by the following steps: assuming that when the user randomly walks again, the user cannot randomly choose to jump to any node, but choose a node from a specific node set, and meanwhile, when initializing the weight of the node, the node in the specific node set is treated differently from other nodes, and when calculating to a stable state, the user-preferred node and related nodes can obtain better weight. For the node p, the calculation method of the personalized PageRank is as follows:
r＝(1-c)Mr+cv
where v is a preference vector of the user, representing the importance of each node in the relationship network for a given preference vector, i.e. the user's preferences.
However, in the problem of individual credit risk analysis, the weight influence between two nodes is related to the occurrence time of the adverse credit events besides the adverse credit events themselves, and according to the general knowledge, the adverse credit events occurring at closer time have larger influence on the current situation, and vice versa, but the personalized PageRank algorithm does not consider the influence of time on the node weight.
Disclosure of Invention
The invention aims to provide an individual credit risk assessment method, which is based on the acquisition of a relationship network and an adverse credit event of a user to be tested, analyzes and calculates the risk weight of all nodes in the user relationship network according to a time function and an improved personalized PageRank calculation method to obtain individual credit risk assessment data, can calculate the individual credit risk assessment data of the user more accurately, and reduces the leakage probability of the user data.
The purpose of the invention is realized by the following technical scheme:
an individual credit risk assessment method, comprising the steps of:
s1, obtaining the user relation network U ═ GU,VU> (wherein G)UIs a set of user nodes, V, in a relational networkUIs a set of edges in a relational network;
s2, establishing assumption conditions, assuming that the risk weight of the user node U is w, and the n other user nodes connected to the user node U are U ═ U { (U) }1,u2,...,un}；
S3, acquiring an adverse credit event of a user node u, and transmitting the risk weight of the node u to a node connected with u by using a time correlation function delta (u, t);
s4, the personalized PageRank algorithm is improved, and the improved personalized PageRank algorithm is utilized to traverse all nodes in the user relationship network and simultaneously complete the risk weight calculation of the adverse credit event;
s5, according to the risk weight, all users in the user relationship network are sorted, and a user risk sorting list based on the influence of adverse credit events is obtained.
Further, the improvement process of the personalized PageRank algorithm in step S4 includes the following sub-steps:
s401, adding a time attenuation factor into an adjacent matrix, and converting an original adjacent matrix M into a weight matrix W with time attenuation;
s402, the weight distribution of the nodes is independent of the node degrees, the weight influence of the nodes with high heights is amplified, the weight influence obtained by neighbor nodes of the nodes with different degrees in the process of propagation is ensured to be on a scale, and the finally improved personalized PageRank calculation method is obtained.
Further, in the substep S401, if δ is an exponential time decay function, then:
wherein β is a decay constant indicating the rate of the decrease of the influence of the past information, t is the interval between the time when the bad credit event occurred and the current time, and when t is 0, the bad credit event is currently occurring, after exponential time decay function, the original adjacency matrix M is transformed into a weight matrix W with time decay added, and at this time, the improved PageRank value is calculated by:
r＝(1-c)Wr+cv
further, the finally improved personalized PageRank algorithm obtained in the substep S402 is as follows:
r＝(1-c)Wr+cz'
if v is a preference vector of the user, d is the degree of a node, z is a result of multiplying each element in the vector v and each element in the vector d one by one, and z' is obtained by normalizing z.
Further, an individual credit risk assessment system is provided, comprising: the information acquisition unit is used for acquiring a relationship network of a user; the risk weight setting unit is used for setting the risk weight of the user node and acquiring other user nodes connected with the user node; the credit risk analysis unit acquires the adverse credit event information of the user, analyzes and processes the risk weight of the node of the user with the adverse credit event by using a time function, and transmits the risk weight to the node connected with the current node of the user; the credit risk calculation unit is used for traversing all nodes in the user relationship network by utilizing an improved personalized PageRank algorithm and calculating the risk weight of all nodes in the user relationship network; and the credit risk evaluation unit sequences all users in the user relationship network according to the risk weight to obtain a user credit risk sequencing table based on the influence of adverse credit events and evaluate the individual credit risk.
Further, an individual credit risk assessment terminal is provided, which includes: a memory for storing a computer program; a processor for processing the computer program to implement the steps of the individual credit risk assessment method described above.
Further, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned individual credit risk assessment method.
The invention has the beneficial effects that: the individual credit risk assessment method provided by the invention is based on the time influence problem, needs to further improve the personalized PageRank algorithm, can more accurately acquire individual credit risk assessment data when a user applies for issuing data, and reduces the leakage risk of data issuing.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a system configuration diagram of the present invention.
Detailed Description
In order to more clearly understand the technical features, objects, and effects of the present invention, embodiments of the present invention will now be described with reference to the accompanying drawings.
As shown in fig. 1, in an embodiment of the present invention, an individual credit risk assessment method includes:
(1) given a user relationship network U ═ GU,VU> (wherein G)UIs a set of user nodes, V, in a relational networkUIs a set of edges in a relational network;
(2) assuming that there is a user node U with a risk weight w, the n other user nodes connected to the user node U are U ═ U1,u2,...,un}；
(3) Assuming that a user node u has a certain bad credit event, there is a time-dependent function δ (u, t) to conduct the risk weight of the node u to the node connected to u;
(4) the personalized PageRank algorithm is improved, and the improved personalized PageRank algorithm is utilized to traverse all nodes in the user relationship network and simultaneously complete the conducting calculation of the risk weight of the adverse credit event;
(5) and sequencing all users in the user relationship network according to the risk weight to obtain a user risk sequencing list based on the influence of the adverse credit events.
The method for improving the personalized PageRank algorithm comprises the following steps:
(1) a time decay factor is added to the adjacency matrix. Assuming δ to be an exponential time decay function, then there are:
wherein β is a decay constant indicating the rate of the decrease of the influence of the past information, t is the interval between the time when the bad credit event occurred and the current time, and when t is 0, it indicates that the bad credit event is currently occurring, after exponential time decay function, the original adjacency matrix M is transformed into a weight matrix W with time decay, and in this case, the PageRank value is calculated by:
r＝(1-c)Wr+cv
(2) the weight distribution of the nodes is independent of the node degree: in the personalized PageRank, (1-c) Qr represents that the weight influence brought by the bad credit event of the node is dispersedly propagated to the neighbor nodes, but when the weights are equal, the high-level node is propagated to the lower weight influence of the neighbor nodes, and the low-level node is propagated to the higher weight influence of the neighbor nodes. In the improved personalized PageRank algorithm, the weight influence of the height nodes is amplified, so that the weight influence obtained by neighbor nodes of nodes with different degrees on one scale during propagation is ensured.
Finally, the improved personalized PageRank calculation method is as follows:
r＝(1-c)Wr+cz'
if v is a preference vector of the user, d is the degree of a node, z is a result of multiplying each element in the vector v and each element in the vector d one by one, and z' is obtained by normalizing z.
And iteratively executing an improved personalized PageRank algorithm on all nodes in the relational network, and sorting according to the risk weight to finally obtain a user risk sorting table based on the influence of adverse credit events so as to evaluate the individual credit risk.
In this embodiment, as shown in fig. 2, an individual credit risk assessment system is provided, which includes: the information acquisition unit is used for acquiring a relationship network of a user; the risk weight setting unit is used for setting the risk weight of the user node and acquiring other user nodes connected with the user node; the credit risk analysis unit acquires the adverse credit event information of the user, analyzes and processes the risk weight of the node of the user with the adverse credit event by using a time function, and transmits the risk weight to the node connected with the current node of the user; the credit risk calculation unit is used for traversing all nodes in the user relationship network by utilizing an improved personalized PageRank algorithm and calculating the risk weight of all nodes in the user relationship network; and the credit risk evaluation unit sequences all users in the user relationship network according to the risk weight to obtain a user credit risk sequencing table based on the influence of adverse credit events and evaluate the individual credit risk.
In this embodiment, an individual credit risk assessment terminal is provided, including: a memory for storing a computer program; a processor for processing the computer program to implement the steps of the individual credit risk assessment method described above.
In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when being executed by a processor, implements the steps of the above-mentioned individual credit risk assessment method.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. An individual credit risk assessment method, comprising the steps of:
s1, obtaining the user relation network U ═ GU,VU> (wherein G)UIs a set of user nodes, V, in a relational networkUIs a set of edges in a relational network;
s2, establishing assumption conditions, assuming that the risk weight of the user node U is w, and the n other user nodes connected to the user node U are U ═ U { (U) }1,u2,...,un}；
S3, acquiring an adverse credit event of a user node u, and transmitting the risk weight of the node u to a node connected with u by using a time correlation function delta (u, t);
s4, the personalized PageRank algorithm is improved, and the improved personalized PageRank algorithm is utilized to traverse all nodes in the user relationship network and simultaneously complete the conducting calculation of the risk weight of the adverse credit event;
s5, according to the risk weight, all users in the user relationship network are sorted, and a user risk sorting list based on the influence of adverse credit events is obtained.
2. The method for assessing the risk of an individual credit as claimed in claim 1, wherein the step of improving the personalized PageRank algorithm in step S4 comprises the following substeps:
s401, adding a time attenuation factor into an adjacent matrix, and converting an original adjacent matrix M into a weight matrix W with time attenuation;
s402, the weight distribution of the nodes is independent of the node degrees, the weight influence of the nodes with high heights is amplified, the weight influence obtained by neighbor nodes of the nodes with different degrees in the process of propagation is ensured to be on a scale, and the finally improved personalized PageRank calculation method is obtained.
3. The method as claimed in claim 2, wherein in the substep S401, if δ is an exponential time decay function, then the method comprises:
wherein β is a decay constant indicating the rate of the decrease of the influence of the past information, t is the interval between the time when the bad credit event occurred and the current time, and when t is 0, the bad credit event is currently occurring, after exponential time decay function, the original adjacency matrix M is transformed into a weight matrix W with time decay added, and at this time, the improved PageRank value is calculated by:
r＝(1-c)Wr+cv。
4. the method as claimed in claim 2, wherein the personalized PageRank algorithm finally improved in the substep S402 is:
r＝(1-c)Wr+cz'
if v is a preference vector of the user, d is the degree of a node, z is a result of multiplying each element in the vector v and each element in the vector d one by one, and z' is obtained by normalizing z.
5. An individual credit risk assessment system, further comprising:
the information acquisition unit is used for acquiring a relationship network of a user;
the risk weight setting unit is used for setting the risk weight of the user node and acquiring other user nodes connected with the user node;
the credit risk analysis unit acquires the adverse credit event information of the user, analyzes and processes the risk weight of the node of the user with the adverse credit event by using a time function, and transmits the risk weight to the node connected with the current node of the user;
the credit risk calculation unit is used for traversing all nodes in the user relationship network by utilizing an improved personalized PageRank algorithm and calculating the risk weight of all nodes in the user relationship network;
and the credit risk evaluation unit sequences all users in the user relationship network according to the risk weight to obtain a user credit risk sequencing table based on the influence of adverse credit events and evaluate the individual credit risk.
6. An individual credit risk assessment terminal, comprising:
a memory for storing a computer program;
a processor for processing the computer program to implement the steps of the individual credit risk assessment method of any one of claims 1 to 4.
7. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the individual credit risk assessment method according to any one of claims 1 to 4.
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|---|---|---|---|---|
| US20150220639A1 (en) * | 2014-01-31 | 2015-08-06 | MAX-PLANCK-Gesellschaft zur Förderung der Wissenschaften e.V. | Computer-implemented method and apparatus for determining a relevance of a node in a network |
| CN108550077A (en) * | 2018-04-27 | 2018-09-18 | 信雅达系统工程股份有限公司 | A kind of individual credit risk appraisal procedure and assessment system towards extensive non-equilibrium collage-credit data |
| CN110634060A (en) * | 2018-06-21 | 2019-12-31 | 马上消费金融股份有限公司 | User credit risk assessment method, system, device and storage medium |
| US20210248676A1 (en) * | 2018-07-18 | 2021-08-12 | Advanced New Technologies Co., Ltd. | Method and device for performing credit evaluation on copyright users on the basis of blockchain |
| US20210287284A1 (en) * | 2016-09-26 | 2021-09-16 | Bionic 8 Analytics Ltd. | Method, system and computer program product for processing social data |
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- 2020-03-03 CN CN202010139716.5A patent/CN111369139A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150220639A1 (en) * | 2014-01-31 | 2015-08-06 | MAX-PLANCK-Gesellschaft zur Förderung der Wissenschaften e.V. | Computer-implemented method and apparatus for determining a relevance of a node in a network |
| US20210287284A1 (en) * | 2016-09-26 | 2021-09-16 | Bionic 8 Analytics Ltd. | Method, system and computer program product for processing social data |
| CN108550077A (en) * | 2018-04-27 | 2018-09-18 | 信雅达系统工程股份有限公司 | A kind of individual credit risk appraisal procedure and assessment system towards extensive non-equilibrium collage-credit data |
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