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US20140214648A1 - Methods and systems for automatically generating high quality adverse action notifications - Google Patents

Methods and systems for automatically generating high quality adverse action notifications Download PDF

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US20140214648A1
US20140214648A1 US14/169,400 US201414169400A US2014214648A1 US 20140214648 A1 US20140214648 A1 US 20140214648A1 US 201414169400 A US201414169400 A US 201414169400A US 2014214648 A1 US2014214648 A1 US 2014214648A1
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variables
borrower
credit
meta
application
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John W.L. Merrill
Shawn M. Budde
John B. Candido, III
Lingyun Gu
Farshad Kheiri
James P. McGuire
Douglas C. Merrill
Manoj Pinnamaneni
Marick Sinay
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Zestfinance Inc
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Zestfinance Inc
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Priority to US14/169,400 priority Critical patent/US20140214648A1/en
Assigned to ZESTFINANCE, INC. reassignment ZESTFINANCE, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: CANDIDO, JOHN B., III, MCGUIRE, JAMES P., MERRILL, DOUGLAS C., BUDDE, SHAWN M., PINNAMANENI, MANOJ, GU, LINGYUN, KHIERI, FARSHAD, MERRILL, JOHN W.L., SINAY, MARICK
Publication of US20140214648A1 publication Critical patent/US20140214648A1/en
Priority to US14/954,825 priority patent/US20160155193A1/en
Priority to US16/109,545 priority patent/US12271945B2/en
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    • G06Q40/025—
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q40/03—Credit; Loans; Processing thereof
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

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  • This invention relates generally to the personal finance and banking field, and more particularly to the field of lending and credit notification methods and systems.
  • One preferred method for automatically generating high quality adverse action notifications can include entering and/or importing a borrower dataset and a lender's credit criteria at a first computer (borrower data and lender criteria); processing the dataset variables and/or sets of variables in the lender's algorithms to identify which variables, when changed, result in an increased credit score (field selection); ranking individual variables and/or sets of variables in the borrower dataset to yield the greatest differences in a credit score (field ranking); and generating a report showing which variables and/or sets of variables, when changed, result in an acceptable credit score (reason test generation).
  • the preferred method can further include formatting the reason set generation into an adverse action letter that is understandable and usable by the consumer (adverse action letter generation).
  • adverse action letter generation formatting the reason set generation into an adverse action letter that is understandable and usable by the consumer.
  • the present invention could be used independently (by simply generating adverse action letters) or in the alternative, the present invention could also be interfaced with, and used in conjunction with, a system and method for providing credit to borrowers.
  • An example of such systems and methods is described in U.S. patent application Ser. No. 13/454,970, entitled “System and Method for Providing Credit to Underserved Borrowers, to Douglas Merrill et al, which is hereby incorporated by reference in its entirety (“Merrill Application”).
  • FIG. 1 is a diagram of a system for automatically generating high quality adverse action notifications in accordance with a preferred embodiment of the present invention.
  • FIG. 2 depicts an overall flowchart illustrating an exemplary embodiment of a method by which high quality adverse action notifications are automatically generated.
  • FIG. 3 depicts a flowchart illustrating an exemplary embodiment of a method for important field selection.
  • FIG. 4 depicts a flowchart illustrating an exemplary embodiment of a method for finding the path to adequacy.
  • FIG. 5 a depicts a flowchart illustrating an alternative exemplary embodiment short titled “swapping codes” as contained in the method for determining the path to adequacy.
  • FIG. 5 b depicts a flowchart illustrating an alternative exemplary embodiment short titled “selection by scoring” as contained in the method for determining the path to adequacy.
  • FIG. 5 c depicts a flowchart illustrating an alternative exemplary embodiment short titled “mutation” as contained in the method for determining the path to adequacy.
  • FIG. 5 d depicts a flowchart illustrating an alternative exemplary embodiment short titled “cross-over” as contained in the method for determining the path to adequacy.
  • BORROWER DEVICE shall generally refer to a desktop computer, laptop computer, notebook computer, tablet computer, mobile device such as a smart phone or personal digital assistant, smart TV, gaming console, streaming video player, or any other, suitable networking device having a web browser or stand-alone application configured to interface with and/or receive any or all data to/from the CENTRAL COMPUTER, USER DEVICE, and/or one or more components of the preferred system 10 .
  • the term “USER DEVICE” shall generally refer to a desktop computer, laptop computer, notebook computer, tablet computer, mobile device such as a smart phone or personal digital assistant, smart TV, gaming console, streaming video player, or any other, suitable networking device having a web browser or stand-alone application configured to interface with and/or receive any or all data to/from the CENTRAL COMPUTER, BORROWER DEVICE, and/or one or more components of the preferred system 10 .
  • the term “CENTRAL COMPUTER” shall generally refer to one or more sub-components or machines configured for receiving, manipulating, configuring, analyzing, synthesizing, communicating, and/or processing data associated with the borrower and lender. Any of the foregoing subcomponents or machines can optionally be integrated into a single operating unit, or distributed throughout multiple hardware entities through networked or cloud-based resources. Moreover, the central computer may be configured to interface with and/or receive any or all data to/from the USER DEVICE, BORROWER DEVICE, and/or one or more components of the preferred system 10 as shown in FIG. 1 . The CENTRAL COMPUTER may also be the same device described in more detail in the Merrill Application, incorporated by reference in its entirety.
  • BORROWER'S DATA shall generally refer to the borrower's data in his or her application for lending as entered into by the borrower, or on the borrower's behalf, in the BORROWER DEVICE, USER DEVICE, or CENTRAL COMPUTER.
  • this data may include traditional credit-related information such as the borrower's social security number, driver's license number, date of birth, or other information requested by a lender.
  • This data may also include proprietary information acquired by payment of a fee through privately or governmentally owned data stores (including without limitation, through feeds, databases, or files containing data).
  • this data may include public information available on the internet, for free or at a nominal cost, through one or more search strings, automated crawls, or scrapes using any suitable searching, crawling, or scraping process, program, or protocol.
  • borrower data could include information related to a borrower profile and/or any blogs, posts, tweets, links, friends, likes, connections, followers, followings, pins (collectively a borrower's social graph) on a social network. The list of foregoing examples is not exhaustive.
  • LENDER CRITERIA shall generally refer to the criteria by which a lender decides to accept or reject an application for credit as periodically set in the USER DEVICE or CENTRAL COMPUTER.
  • these criteria may include accept or reject criterion based on individual data points in the BORROWER'S DATA (such as length of current residence>6 months), or based on complex mathematical models that determine the creditworthiness of a borrower.
  • NETWORK shall generally refer to any suitable combination of the global Internet, a wide area network (WAN), a local area network (LAN), and/or a near field network, as well as any suitable networking software, firmware, hardware, routers, modems, cables, transceivers, antennas, and the like.
  • WAN wide area network
  • LAN local area network
  • NETWORKING SOFTWARE any suitable networking software, firmware, hardware, routers, modems, cables, transceivers, antennas, and the like.
  • Some or all of the components of the preferred system 10 can access the network through wired or wireless means, and using any suitable communication protocol/s, layers, addresses, types of media, application programming interface/s, and/or supporting communications hardware, firmware, and/or software.
  • the present invention relates to improved methods and systems for automatically generating high quality adverse action notifications, which includes notifications for individuals, and other types of entities including, but not limited to, corporations, companies, small businesses, and trusts, and any other recognized financial entity.
  • a frame of reference is in order: As many consumers know, a person's credit history is made up of a number of variables, such as the amount of debt the person is presently carrying, their income stability, their repayment history on past debt (lateness or failure to pay), and the length of their credit history. However, consumers do not often appreciate that modern credit scoring systems have significantly increased in sophistication, now containing many variables and meta-variables as well.
  • a preferred operating environment for automatically generating high quality adverse action notifications in accordance with a preferred embodiment can generally include data sources (the borrower's application 13 , and the lender's credit model 15 ), a USER DEVICE 30 , a CENTRAL COMPUTER 20 , a NETWORK 40 , and one or more communication devices from which the borrower is issued an adverse action letter, including a BORROWER DEVICE 12 , Email Server 30 , and/or a Print Server 40 .
  • the preferred system 10 can include at least: data sources (the borrower's application 13 , and the lender's credit model 15 ), and a computer to analyze and process the data sources (CENTRAL COMPUTER 20 and/or a USER DEVICE 30 ), which function to generate high quality adverse action notifications.
  • the borrower's application 13 should include one or more variables in the BORROWER DATA
  • the lender's credit model 15 should include one or more algorithms from the LENDER'S CRITERIA.
  • the preferred system 10 functions to helps borrowers determine the accuracy of his/her credit file as well as provide information to improve his/her creditworthiness, by accessing, evaluating, measuring, quantifying, and utilizing a the novel and unique methodology described below.
  • this invention relates to the preferred methodology for automatically generating high quality adverse action notifications that takes place within the CENTRAL COMPUTER 20 and/or a USER DEVICE 30 , after gathering and/or downloading the BORROWER'S DATA 13 and the LENDER CRITERIA 15 .
  • FIG. 2 provides a flowchart illustrating one preferred method for automatically generating high quality adverse action notifications which involves the following steps: (a) gathering the BORROWER DATA 100 for a failed credit application; (b) important field selection 200 (to compare BORROWER DATA against the LENDER CRITERIA 600 ); (c) field ranking 300 ; (d) reason text generation 400 ; and (e) generating adverse action letters 500 .
  • BORROWER DATA 100 all data from the borrower's failed application is temporarily gathered for collection by a computer (such as the CENTRAL COMPUTER 20 in FIG. 1 ).
  • the BORROWER DATA 100 may include classic financial data such as the borrower's current salary, length of most recent employment, and the number of bankruptcies.
  • the BORROWER DATA 100 may include other unique aspects of the borrower, such as the number of organizations the borrower has been or is currently is involved with, the number of friends the borrower has, or other non-traditional aspects of the borrower's identity and history such those identified in the Merrill Application. Subsets of BORROWER DATA 100 are used to determine the borrower's credit score.
  • fictitious BORROWER DATA 100 for Ms. “A” (a creditworthy applicant), Mr. “B” (a declined applicant), the average approved applicant, and the perfect applicant are shown below:
  • Important field selection is the creation of a list of BORROWER DATA variables whose values either reduce or increase the application's credit score by sufficiently perceptible amounts when those variables are changed, and processed through the LENDER CRITERIA 600 .
  • important field selection 200 may be accomplished by determining the shortest path between the borrower's credit application and the “perfect application” (shortest path 210 ). Alternatively, important field selection 200 may be accomplished by finding the most important changes between the borrower's application and an “adequate application” that is approved for funding (path to adequacy 220 ). Both methods are discussed below:
  • the shortest path 210 is a protocol in which a list of all fields (variables) are identified where there is difference between the BORROWER DATA and the data of a “perfect” applicant. Given that a “perfect” application (one which receives the highest possible score) will always be funded, one way to build an explanation for why a different application was not approved is to find the set of differences between the unfunded application and the perfect application. Thus, as a preliminary step, the preferred method is to record a list of fields on which the two applications differ.
  • BORROWER DATA 100 could include dozens variables or hundreds of thousands of meta-variables. And depending on the sophistication of the Lender's credit scoring system, some or most of those variables and meta-variables may not be used in determining a borrower's credit score.
  • the shortest path 210 may not be helpful to the applicant in (1) identifying flaws in his credit profile and (2) determining what actions would be necessary to improve his creditworthiness. Thus, if an applicant takes selective actions in “remedying” portions of his/her credit profile; those changes may not result in a score improvement that would meet the LENDER CRITERIA 600 . In other words, the borrower may not be able to recognize which variables are important, and which are just chaff.
  • the preferred method in the shortest path 210 includes an intermediate step that eliminates “low impact” fields (which are later omitted from the reason text generation 400 , and in turn, the adverse action letter 500 as shown earlier in FIG. 2 ).
  • the preferred method for eliminating “low impact” fields does not directly identify “low impact” fields. Rather, the focus is to find the “signals” that are important. And in order to find the signals that are important, the preferred method is to pick the variables which require the smallest transformation (i.e. the shortest path) from a given application to an application with a perfect score. A singular path may be chosen at random with signals then selected based on their relative impact. Alternatively, if multiple paths are available, then lists of variable are ranked by frequency, if possible.
  • Path-finding is a well-studied problem in machine learning in either a graph or a continuous domain, and there are many well-studied algorithms for finding optimal or near-optimal paths, including, without limitation: ant colony optimization, swarm-based optimization techniques, steepest and stochastic descent algorithms. In addition, there are many multidimensional optimization algorithms available, which has been a major area of study in computer science since the first computer was built. Other path finding algorithms may be used as well depending on suitability to the data set and/or desired outcome.
  • these path finding algorithms may be applied singularly, or in a hybrid approach, depending on whether the features of the LENDER CRITERIA and/or BORROWER DATA 100 are continuous and/or discrete.
  • a lender criterion might be discrete (e.g., Does the borrower have a job and a checking account?).
  • a borrower signal can also be discrete (e.g., is the borrower employed (yes/no)? Does the borrower have a bank account (yes/no)?).
  • a lender criterion can be continuous (weight the application negatively according to the average amount of ethanol consumed by the applicant each week).
  • the corresponding borrower signal would also be continuous (how many beers have you drunk in the last week? Glasses of wine? Mixed drinks/other distilled liquor products?)
  • the shortest path 210 may be further “filtered” whereby denials for seemingly spurious fields (such as the number of friends one has in social media), could be eliminated from the important field selection 200 list.
  • FIG. 4 provides a second perspective in illustrating the preferred method to find the shortest path 210 .
  • a comparison of known good application(s) 211 would be made against known bad application(s) 212 . From this comparison, a list of identical signals 213 and different signals 214 could be obtained. Thereafter, the incremental changes to the variables/fields that produce different signals 214 would be run against a series of selection tests 215 .
  • One test might determine if changes to individual variables, or sets of variables, result in a sufficiently improved credit score.
  • a second test may eliminate those fields, that when changed, does not result in substantial improvement—or any improvement—in the applicant's credit score.
  • a third test may include a manual filter whereby certain variables/fields are eliminated for administrative purposes.
  • a second preferred method to important field selection 200 may be achieved by finding the most important changes in a path to an adequate application (path to adequacy 220 ).
  • the path to adequacy 220 is likely to return numerous paths to fundability.
  • the preferred method for generating the path to adequacy 220 seeks the shortest paths from a given application to applications that have scores exceeding a specified threshold (where the threshold is no greater than the maximum possible value of the scoring function). The methods for doing so are similar to that found in the shortest path 210 , except that instead of comparing the borrower's profile to a perfect application, it is instead compared to a collection of accepted applicants.
  • the preferred method of the present invention is to identify a set of changes to the failing application when compared to previously collected approved applicants.
  • the preferred approach is probabilistic: taking random subsets of the set of exchanged fields, and measuring the resulting score change. In such instances, the preferred method is to use the score changes over all samples. The result turns out to be a rough weighting of the contribution of the individual fields to the final score change.
  • the third step is field ranking 300 .
  • the preferred approach for field ranking 300 will depend on whether important field selection 200 is accomplished by way of the shortest path method 210 or the path to adequacy 220 .
  • the shortest path method 210 is employed, ranking, although possible, is purely academic. Indeed, and if well specified, the truncation of the shortest path effectively creates an “all or nothing” result of a long list of fields. In other words, since all changes dictated by the shortest path are necessary to make the application fundable, there is no need to rank the important field selection 200 .
  • the preferred method would regulate the number of fields by ranking the fields so that higher-ranked fields contribute more to a passing score than lower-ranked ones.
  • the preferred ranking method would employ a voting strategy.
  • the computer performs many simultaneous searches for many paths to the specified threshold, and then the computer votes based on the number of paths a given field occurs in. Examples include, but are not limited to: membership in the greatest number of paths, changes that have the greatest impact, or some combination thereof. A complete enumeration of the methods is not possible. However, the preferred method will seek to have a meaningful correlation to signal impact, and avoids verging into an arbitrary ranking or scoring function, where possible. Notwithstanding, arbitrary ranking or scoring functions are an alternative method.
  • An alternate method to field ranking 300 is to estimate the “contribution” of each field in each path to the final score difference. As stated above, one method to do so is to take random samples of the fields for any given path and compute the score that arose from just using values in those fields, and take the average difference across all paths containing each field as an importance score (while ranking fields according to their importance).
  • the preferred method for identifying “contributions,” is either accomplished by using (1) a ranking by scoring methodology, or (2) through a genetic algorithm.
  • either electronic method can be used to more efficiently select the most regularly occurring sets of high-impact changes that could be made within a set of paths (or aggregated portions of paths) that result in credit approval.
  • the ranking by scoring method significantly reduces the number of searches for adequate paths (or portions thereof) that would lead to an acceptable credit score. Rather than using a purely random selection of variables, the ranking by scoring groups items into small sets to be evaluated tournament style. Thus, by limiting the number of sets that may be grouped, ranking by scoring effectively ranks a limited, yet decreasingly random population of paths, which is thereafter ranked.
  • a simple example may provide a helpful background: As shown in FIG. 5 a (single associated exchange score), occurs when the values of one set of deficient variables (ID 301 ), has their values replaced (exchange list 303 ) which results in a new, and preferably acceptable, resulting credit score (score 302 ).
  • ranking may be made by “ranking by scoring.”
  • ranking importance scores is accomplished by replacing the values in an initial set of variables (original selection 310 ) with a second set of values (revised ranking by scoring 311 ), and then by scoring the possible replacements. This process is would likely be given a limited universe (e.g., computer, please select 1,000 random sets of variables), then continue exchanging combinations of variables—tournament style—until the most potent changes are identified and ranked.
  • a genetic algorithm In the alternative to ranking by scoring, the use of a genetic algorithm may be employed. Genetic algorithms are a well-studied area of computational science that seeks to generate useful solutions to optimization and search problems. In the instant invention, a genetic algorithm would seek out the “pieces of the paths” that most frequently, and most effectively, produce an acceptable credit score.
  • a genetic algorithm uses the evolutionary processes of crossover and mutation to randomly assemble new offspring from an existing population of solutions.
  • the parent solutions are then “selected” to generate offspring in proportion to their fitness. The more fit, or better matched to the achieving a credit worthy score, an individual model is, the more often it will contribute its genetic information to subsequent generations.
  • a genetic algorithm would first engage in mutation (randomly identifying sets of variables and changing values within those sets of variables), “cross over” the sets of variables (i.e., find the most effective sets of variables and values to change), and then “select” a population of paths that are more impactful than others. This process would be iteratively repeated and optimized through “generations” of changes within the sets of variables to determine how effectively each set of changes lead to a passing credit score. During each successive generation, a proportion of the existing population is selected to breed a new generation. Individual solutions are selected through a fitness-based process, where fitter solutions (sets of changes that quantitatively produce greater changes in the credit score) are typically more likely to be selected.
  • mutation replaces the swap point 320 between one exchange list and another.
  • cross-over replaces an initial set of variables/values (original selection 310 ) and with a second set of variables/values (revised ranking by scoring 311 ) by mutating possible replacements amongst various possibilities.
  • mutation and/or cross-over operate to produce a number of different candidates, which are then ranked by their scores (highest to lowest), and then resampled with a weight according to each score.
  • Applicant C Applicant D Applicant E Age 61 35 37 Employment No, but . . . Yes Yes Checking account No Yes Yes Income $50,000 pension $35,000 $30,000 Distance 0 25 27
  • the most “important reasons” for each of the three applicants would first look for randomly selected sets of swaps. Each of those swaps would then be scored. By comparing each applicant to funded applications, the preferred method would generate a set of frequencies for each variable (or randomly selected set thereof). Using random substitution of values for each variable (or sets of variables) would take an inordinate period of time. Therefore, the preferred method could exchange values individually, or in blocks. This resulting set produces an ordering: application C needed a job and a checking account, application D would need to live closer to work, and application E would need a checking account and to live closer.
  • these ranking protocols fall into two categories: continuous parameters and discrete search space.
  • continuous parameters algorithms search parameters such as regression and Lyapunov functional reduction are particularly well suited.
  • discrete search space other suitable search space algorithms, such as pure random search, simulated annealing, and/or other genetic algorithms are better suited.
  • the preferred method is to gather Mr. B's BORROWER DATA as well as extract the subset of previous applications with scores fundability threshold.
  • the preferred method for important field selection 200 is to create an initial population of exemplars consisting of an index into that subset and a bit vector of the same length as the list of features for the LENDER CRITERIA 600 . Each exemplar will be scored by taking Mr. B's un-awarded loan and replacing the list items where the bit vector is 1 with the values from the indexed element of the subset.
  • the preferred method is to compute the score of Mr. B's modified list. This process will be iteratively repeated until an appropriate termination criterion has been reached (e.g., all paths to fundability have been identified or the method-defined maximum number of paths has been identified).
  • Mr. B is a simple and straightforward genetic algorithm, wherein the preferred method has found that the population converges to a set of exemplars that represent changes to fields/variable that produce significant improvements in Mr. B's creditworthiness (i.e. yielding an acceptable risk profile to issue a loan.).
  • the fourth and fifth steps are reason text generation 400 and generating an adverse action letter 500 .
  • reason text generation 400 involves recording a list of items with the largest possible weights.
  • Credit scoring systems often perform veracity checks with third-party data sources that supply information on the borrower. And if a borrower's profile is inconsistent with what is self-reported and/or has values outside the “norm” of other borrowers, those fields will be flagged, and often result in a deduction from the borrower's credit score. Thus, there is a strong probability that important errors will show up with high ranks. Since the values associated with those errors and the sources from which the erroneous signals were drawn will be listed, consumers will be able to recognize opportunities for significantly improving their scores by correcting errors in credit agency files or in their own application data.
  • adverse action letters 500 requires additional steps and procedures.
  • the creation of adverse action letters 500 may be resolved within the standard boundaries of well-studied machine learning paradigms.
  • the “filtered” field list would then be translated to associated qualitative entries. For example, a variable or meta-variable associated with “number of addresses” would have at least one text entry associated with it (so called “report classes”), such as “your residential address has changed many times in the past five years, indicating that your employment is unstable.”
  • Report classes are lender-defined, examples of which include messages that are prescriptive (“Establish and maintain a bank account for more than 2 years” or “Avoid overdrawing your checking account and try to schedule your essential payments so you aren't late with your bills”), descriptive (“Lexis-Nexis reports have multiple social security numbers associated with your name and address. That could be in error, and, if so, should be corrected,”), and/or monitory (“One or more of the fields in your application exhibits features highly correlated with fraud. You should look at items reported on your application and correct any errors therein.”).
  • the preferred method generates a labeled set of training exemplars which connect the weight pattern for a given application to the report class or classes with which the application is associated. Thereafter, the preferred embodiment could use standard classification techniques such as support vector machines, k means, learned vector quantization, or EM to build a labeling function.
  • standard classification techniques such as support vector machines, k means, learned vector quantization, or EM to build a labeling function.
  • any of the above-described processes and methods may be implemented by any now or hereafter known computing device.
  • the methods may be implemented in such a device via computer-readable instructions embodied in a computer-readable medium such as a computer memory, computer storage device or carrier signal.

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Cited By (6)

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JP2017054495A (ja) * 2015-09-12 2017-03-16 スルガ銀行株式会社 事前与信枠及び推奨与信枠算出装置
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