US5696907A
Expired / Lapsed
US
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System and method for performing risk and credit analysis of financial service applications
Assignee: General Electric Co
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Filing: 1995-02-27
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Granted: 1997-12-09
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Expires: 2015-02-27
Abstract
Abstract The present invention discloses a method and system for performing risk and credit analysis of financial service applications with a neural network. The neural network imitates and perfects a credit manager's evaluation and decision process to control loss and guide business expansion. In particular, the neural network screens applications to control loss and to find directions where business volume can increase with a minimum increase in loss. Initially, data variables are pre-processed and applied to the neural network. The neural network in the present invention is optimized by a non-iterative regression process, as opposed to the computationally intensive back propagation algorithm.
Inventors
- 👤 Mohimm Daniel Tom
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-26 13:31
CPC Classifications
G; G06; G06Q; G06Q40/00; G06Q40/08; G06Q40/03
Claims Analysis
Total Claims: 38
Claim 1
Independent Claim
226 words
A computer implemented method for performing risk and credit analysis of financial service applications with a neural network having an input layer of processing nodes, an output layer having a processing node, and an intermediate layer of processing nodes coupling the input layer to the output layer by weighted connections, the method comprising the steps of: collecting data and status information from a plurality of previously approved financial service applications; applying the data into the input layer of the neural network; organizing the data at the input layer of the neural network into a plurality of groups, each of the plurality of groups containing variables used to perform risk and credit analysis; classifying the grouped data from each of the plurality of groups into ordinal values and categorical values; applying each of the plurality of groups of data from the processing nodes of the input layer to a separate processing node of the intermediate layer of the neural network; applying the data from each of the separate processing nodes at the intermediate layer of the neural network to the processing node of .the output layer; optimizing the weighted connections of the neural network to an approval criteria that increases approval volume of financial service applications with a minimum loss; and providing data from a recently filed financial service application to the optimized neural network for evaluation.
Claim 2
Dependent Claim
226 words
A computer implemented method for performing risk and credit analysis of financial service applications with a neural network having an input layer of processing nodes, an output layer having a processing node, and an intermediate layer of processing nodes coupling the input layer to the output layer by weighted connections, the method comprising the steps of: collecting data and status information from a plurality of previously approved financial service applications; applying the data into the input layer of the neural network; organizing the data at the input layer of the neural network into a plurality of groups, each of the plurality of groups containing variables used to perform risk and credit analysis; classifying the grouped data from each of the plurality of groups into ordinal values and categorical values; applying each of the plurality of groups of data from the processing nodes of the input layer to a separate processing node of the intermediate layer of the neural network; applying the data from each of the separate processing nodes at the intermediate layer of the neural network to the processing node of .the output layer; optimizing the weighted connections of the neural network to an approval criteria that increases approval volume of financial service applications with a minimum loss; and providing data from a recently filed financial service application to the optimized neural network for evaluation.
Claim 3
Dependent Claim
49 words
The method according to claim 1, wherein the status information from the plurality of previously approved financial service applications comprises a termination due to default in payment, an early termination by a financial service applicant, a normal termination, and a financial service account open without any delinquencies in payment.
Claim 4
Dependent Claim
61 words
The method according to claim 2, further comprising the step of arranging the status information into an acceptable class and an unacceptable class, the acceptable class comprising a normal termination, and a financial service account open without any delinquencies in payment, the unacceptable class comprising a termination due to default in payment and a early termination by a financial service applicant.
Claim 5
Dependent Claim
27 words
The method according to claim 3, further comprising the step of determining vulnerability for the data in each of the plurality of groups at the input layer.
Claim 6
Dependent Claim
35 words
The method according to claim 1, wherein the plurality of groups applied to the separate processing nodes in the intermediate layer comprise employment stability, residential stability, credit history, debt and income history, and miscellaneous variables.
Claim 7
Dependent Claim
69 words
The method according to claim 1, further comprising the step of sorting the ordinal values for each of the plurality of groups in an ordered ranking from a highest ordinal value to a lowest ordinal value, the highest ordinal value assigned a one, the lowest ordinal value assigned a zero, and values in between the highest and lowest ordinal value assigned a value scaled linearly between zero and one.
Claim 8
Dependent Claim
38 words
The method according to claim 1, wherein the weighted connections are determined according to a multiple regression analysis using the data and status information, the weighted connections are sorted, ranked, and normalized to values between zero and one.
Claim 9
Dependent Claim
32 words
The method according to claim 7, further comprising sending the weighted connections to a credit manager for examination and for inferring a direction of relaxation to achieve business expansion while controlling loss.
Claim 10
Dependent Claim
41 words
The method according to claim 1, further comprising the step of screening the recently filed financial service application in accordance with the approval criteria, the recently filed financial service application approved if risk is minimal and rejected if risk is high.
Claim 11
Independent Claim
248 words
A system for performing risk and credit analysis of financial service applications comprising: a data storage for storing data and status information from a plurality of previously approved financial service applications; means for pre-processing the data into a plurality of groups, each of the plurality of groups containing variables used to perform risk and credit analysis; a neural network having an input layer of processing nodes, an output layer having a processing node, and an intermediate layer of processing nodes coupling the input layer to the output layer by weighted connections, the pre-processed data received at the input layer of the neural network, the input layer of the neural network organizing the data into a plurality of groups, each of the plurality of groups containing variables used to perform risk and credit analysis; each of the plurality of groups of data from the processing nodes of the input layer applied to a separate processing node of the intermediate layer of, the data from each of the separate processing nodes at the intermediate layer applied to the processing node of the output layer of the neural network; means for optimizing the neural network to an approval criteria that increase approval volume of financial service applications; and a processor coupled to the optimized neural network for providing data from a recently filed financial service application to the optimized neural network, the optimized neural network determining the risk of the recently filed financial service application in accordance with the approval criteria.
Claim 12
Independent Claim
248 words
A system for performing risk and credit analysis of financial service applications comprising: a data storage for storing data and status information from a plurality of previously approved financial service applications; means for pre-processing the data into a plurality of groups, each of the plurality of groups containing variables used to perform risk and credit analysis; a neural network having an input layer of processing nodes, an output layer having a processing node, and an intermediate layer of processing nodes coupling the input layer to the output layer by weighted connections, the pre-processed data received at the input layer of the neural network, the input layer of the neural network organizing the data into a plurality of groups, each of the plurality of groups containing variables used to perform risk and credit analysis; each of the plurality of groups of data from the processing nodes of the input layer applied to a separate processing node of the intermediate layer of, the data from each of the separate processing nodes at the intermediate layer applied to the processing node of the output layer of the neural network; means for optimizing the neural network to an approval criteria that increase approval volume of financial service applications; and a processor coupled to the optimized neural network for providing data from a recently filed financial service application to the optimized neural network, the optimized neural network determining the risk of the recently filed financial service application in accordance with the approval criteria.
Claim 13
Dependent Claim
27 words
The system according to claim 10, wherein the pre-processing means classifies the grouped data from each of the plurality of groups into ordinal values and categorical values.
Claim 14
Dependent Claim
49 words
The system according to claim 11, wherein the status information from the plurality of previously approved financial service applications comprises a termination due to default in payment, an early termination by a financial service applicant, a normal termination, and a financial service account open without any delinquencies in payment.
Claim 15
Dependent Claim
59 words
The system according to claim 12, further comprising means for arranging the status information into an acceptable class and an unacceptable class, the acceptable class comprising a normal termination, and a financial service account open without any delinquencies in payment, the unacceptable class comprising termination due to default in payment and an early termination by a financial service applicant.
Claim 16
Dependent Claim
26 words
The system according to claim 13, further comprising means for determining vulnerability for the data in each of the plurality of groups at the input layer.
Claim 17
Dependent Claim
68 words
The system according to claim 11, further comprising means for sorting the ordinal values for each of the plurality of groups in an ordered ranking from a highest ordinal value to a lowest ordinal value, the highest ordinal value assigned a one, the lowest ordinal value assigned a zero, and values in between the highest and lowest ordinal value assigned a value scaled linearly between zero and one.
Claim 18
Dependent Claim
35 words
The system according to claim 10, wherein the plurality of groups applied to the separate processing nodes in the intermediate layer comprise employment stability, residential stability, credit history, debt and income history, and miscellaneous variables.
Claim 19
Dependent Claim
38 words
The system according to claim 10, wherein the weighted connections are determined according to a multiple regression analysis using the data and status information, the weighted connections are sorted, ranked, and normalized to values between zero and one.
Claim 20
Dependent Claim
34 words
The system according to claim 17, further comprising means for sending the weighted connections to a credit manager for examination and for inferring a direction of relaxation to achieve business expansion while controlling loss.
Claim 21
Dependent Claim
172 words
A computer implemented method for performing risk and credit analysis of financial service applications comprising the steps of: collecting data from a plurality of previously approved financial service applications; organizing the data into a plurality of groups each containing variables used to perform risk and credit analysis; constructing a neural network having an input layer of processing nodes, an output layer having a processing node, and an intermediate layer of processing nodes coupling the input layer to the output layer, wherein the input layer of processing nodes are separated according to each of the plurality of groups and each of the groups are applied to a separate processing node in the intermediate layer; determining weighted connections between the processing nodes in the input layer, the intermediate layer, and the output layer of the neural network with a non-iterative regression; optimizing the weighted connections of the neural network for inferring a direction to achieve business expansion; and providing data from a recently filed financial service application to the optimized neural network for evaluation.
Claim 22
Dependent Claim
172 words
A computer implemented method for performing risk and credit analysis of financial service applications comprising the steps of: collecting data from a plurality of previously approved financial service applications; organizing the data into a plurality of groups each containing variables used to perform risk and credit analysis; constructing a neural network having an input layer of processing nodes, an output layer having a processing node, and an intermediate layer of processing nodes coupling the input layer to the output layer, wherein the input layer of processing nodes are separated according to each of the plurality of groups and each of the groups are applied to a separate processing node in the intermediate layer; determining weighted connections between the processing nodes in the input layer, the intermediate layer, and the output layer of the neural network with a non-iterative regression; optimizing the weighted connections of the neural network for inferring a direction to achieve business expansion; and providing data from a recently filed financial service application to the optimized neural network for evaluation.
Claim 23
Dependent Claim
29 words
The computer-implemented method according to claim 19, further comprising the step of classifying the grouped data from each of the plurality of groups into ordinal values and categorical values.
Claim 24
Dependent Claim
70 words
The computer-implemented method according to claim 20, further comprising the step of sorting the ordinal values for each of the plurality of groups in an ordered ranking from a highest ordinal value to a lowest ordinal value, the highest ordinal value assigned a one, the lowest ordinal value assigned a zero, and values in between the highest and lowest ordinal value assigned a value scaled linearly between zero and one.
Claim 25
Dependent Claim
58 words
The computer-implemented method according to claim 19, further comprising the step of determining status information from the plurality of previously approved financial service applications, wherein the status information comprises a termination due to default in payment, an early termination by a financial service applicant, a normal termination, and a financial service account open without any delinquencies in payment.
Claim 26
Dependent Claim
62 words
The computer-implemented method according to claim 22, further comprising the step of arranging the status information into an acceptable class and an unacceptable class, the acceptable class comprising a normal termination, and a financial service account open without any delinquencies in payment, the unacceptable class comprising a termination due to default in payment and an early termination by a financial service applicant.
Claim 27
Dependent Claim
28 words
The computer-implemented method according to claim 23, further comprising the step of determining vulnerability for the data in each of the plurality of groups at the input layer.
Claim 28
Dependent Claim
36 words
The computer-implemented method according to claim 19, wherein the plurality of groups applied to the separate processing nodes in the intermediate layer comprise employment stability, residential stability, credit history, debt and income history, and miscellaneous variables.
Claim 29
Dependent Claim
25 words
The computer-implemented method according to claim 19, further comprising the step of sorting, ranking, and normalizing the weighted connections to values between zero and one.
Claim 30
Independent Claim
205 words
A system for performing risk and credit analysis of financial service applications comprising: a data storage for storing data from a plurality of previously approved financial service applications; means for pre-processing the data into a plurality of groups, each of the plurality of groups containing variables used to perform risk and credit analysis; means for constructing a neural network having an input layer of processing nodes, an output layer having a processing node, and an intermediate layer of processing nodes coupling the input layer to the output layer, wherein the input layer of processing nodes are separated according to each of the plurality of groups and each of the groups are applied to a separate processing node in the intermediate layer; means for determining weighted connections between the processing nodes in the input layer, the intermediate layer, and the output layer of the neural network with a non-iterative regression; means for optimizing the weighted connections of the neural network for inferring a direction to achieve business expansion; and a processor coupled to the optimized neural network for providing data from a recently filed financial service application to the optimized neural network, the optimized neural network determining the risk of the recently filed financial service application.
Claim 31
Independent Claim
205 words
A system for performing risk and credit analysis of financial service applications comprising: a data storage for storing data from a plurality of previously approved financial service applications; means for pre-processing the data into a plurality of groups, each of the plurality of groups containing variables used to perform risk and credit analysis; means for constructing a neural network having an input layer of processing nodes, an output layer having a processing node, and an intermediate layer of processing nodes coupling the input layer to the output layer, wherein the input layer of processing nodes are separated according to each of the plurality of groups and each of the groups are applied to a separate processing node in the intermediate layer; means for determining weighted connections between the processing nodes in the input layer, the intermediate layer, and the output layer of the neural network with a non-iterative regression; means for optimizing the weighted connections of the neural network for inferring a direction to achieve business expansion; and a processor coupled to the optimized neural network for providing data from a recently filed financial service application to the optimized neural network, the optimized neural network determining the risk of the recently filed financial service application.
Claim 32
Dependent Claim
27 words
The system according to claim 27, wherein the pre-processing means classifies the grouped data from each of the plurality of groups into ordinal values and categorical values.
Claim 33
Dependent Claim
68 words
The system according to claim 28, wherein the pre-processing means sorts the ordinal values for each of the plurality of groups in an ordered ranking from a highest ordinal value to a lowest ordinal value, the highest ordinal value assigned a one, the lowest ordinal value assigned a zero, and values in between the highest and lowest ordinal value assigned a value scaled linearly between zero and one.
Claim 34
Dependent Claim
56 words
The system according to claim 27, wherein the pre-processing means determines status information from the plurality of previously approved financial service applications, wherein the status information comprises a termination due to default in payment, an early termination by a financial service applicant, a normal termination, and a financial service account open without any delinquencies in payment.
Claim 35
Dependent Claim
60 words
The system according to claim 30, wherein the pre-processing means arranges the status information into an acceptable class and an unacceptable class, the acceptable class comprising a normal termination, and a financial service account open without any delinquencies in payment, the unacceptable class comprising a termination due to default in payment and a early termination by a financial service applicant.
Claim 36
Dependent Claim
26 words
The system according to claim 31, wherein the pre-processing means determines vulnerability for the data in each of the plurality of groups at the input layer.
Claim 37
Dependent Claim
35 words
The system according to claim 27, wherein the plurality of groups applied to the separate processing nodes in the intermediate layer comprise employment stability, residential stability, credit history, debt and income history, and miscellaneous variables.
Claim 38
Dependent Claim
24 words
The system according to claim 27, wherein the weight determining means sorts, ranks, and normalizes the weighted connections to values between zero and one.
References Cited (Backward Citations)
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| 3.5 Years (Year 4) | 2000-12-08 | 2001-06-09 | 2001-12-09 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2004-12-08 | 2005-06-09 | 2005-12-09 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2008-12-08 | 2009-06-09 | 2009-12-09 | $7700.00 | Window Closed / Lapsed |