Automated resource pool characteristic improvement system
Features are disclosed for identifying an operation for an object of a pool of objects to improve a pool characteristic of the object pool. For example, a system can identify an operation or task for a particular business loan of a pool of business loans to improve a rating of the pool of business loans. The system can monitor data associated with each of the objects to automatically identify particular objects. For example, the system can identify particular objects to be improved or particular objects to be removed from the pool of objects. The system can identify particular operations to improve the pool characteristic of the pool of objects and cause performance of the operations without modifying private data of the object pool by which the objects can be defined.
1 . A computer-implemented method of identifying a transformation operation for an object pool to improve a pool characteristic of the object pool without modifying private data defining individual objects of the object pool, the method comprising:
as implemented by a hardware processor of a data analysis computing system in communication via one or more networks with a pool generation computing system and a ratings computing system, wherein the hardware processor is configured to execute computer-executable instructions:
receiving, by the data analysis computing system, from the pool generation computing system, via the one or more networks, object data identifying an object pool, the object pool comprising a plurality of objects that are pooled such the plurality of objects are movable between entities, each object of the plurality of objects having a discrete impact on a pool characteristic of the object pool based at least in part on the object pool comprising the plurality of objects, wherein each object of the plurality of objects identifies an electronic file comprising an electronic signature, wherein each object of the plurality of objects is included in the object pool based at least in part on validation of a corresponding electronic signature, wherein each electronic file of the plurality of electronic files identifies a first entity, a second entity, and movable physical property, wherein a third party computing system defines components data that identifies a connection between the plurality of movable physical properties and the plurality of objects, wherein the plurality of objects are defined based at least in part on the components data, and wherein the data analysis computing system does not have authorization to modify the components data, the plurality of electronic files, or the plurality of movable physical properties;
monitoring, in real time, first data associated with the object pool, wherein monitoring, in real time, the first data associated with the object pool comprises performing a web crawl;
identifying, by the data analysis computing system, using one or more first network communications received from the ratings computing system via the one or more networks, the pool characteristic of the object pool based at least in part on the object data, wherein the pool characteristic comprises a first dynamic assessment of the plurality of objects at a particular time as compared to another object pool, wherein the pool characteristic is based at least in part on an evaluation of the components data, the plurality of electronic files, or the plurality of movable physical properties, wherein the pool characteristic varies over time based at least in part on modification of at least one of the components data, the plurality of electronic files, or the plurality of movable physical properties;
determining, by the data analysis computing system, for each object of the plurality of objects, a respective object characteristic based at least in part on the object data, wherein the respective object characteristic comprises a second dynamic assessment of the object at a particular time as compared to another object, wherein at least a portion of the plurality of object characteristics are based at least in part on a portion of the components data associated with the object, a respective electronic file identified by the object, or a respective movable physical property of the object, wherein the respective object characteristic varies over time based at least in part on modification of at least one of the portion of the components data associated with the object, the respective electronic file identified by the object, or the respective movable physical property of the object;
identifying, by the data analysis computing system, a particular object of the plurality of objects based at least in part on one or more thresholds associated with the object pool, wherein identifying the particular object comprises, for each object of the plurality of objects:
modelling, by the data analysis computing system, an environment of the object;
simulating, by the data analysis computing system, performance of an operation of a plurality of operations within the modeled environment, wherein the data analysis computing system is programmed to differentiate between operations that result in a modification of data that the data analysis computing system is not authorized to modify or access and operations that result in a modification of data that the data analysis computing system is authorized to modify or access, wherein each operation of the plurality of operations comprises an operation to modify a non-object or an object not included in the object pool such that data associated with the first entity or the second entity is modified, wherein each operation indirectly causes modification to an attribute of the object without modifying the portion of the components data associated with the object, the respective electronic file identified by the object, or the respective movable physical property of the object,
generating, by the data analysis computing system, a predicted object characteristic of the object based at least in part on the respective object characteristic of the object and the simulated performance of the operation,
generating, by the data analysis computing system, a predicted pool characteristic of the object pool based at least in part on the predicted object characteristic of the object, wherein the method further comprises normalizing results of the simulated performance of the operation by generating the predicted object characteristic and the predicted pool characteristic, and
determining, by the data analysis computing system, whether the one or more thresholds are satisfied based on one or more of:
the predicted pool characteristic, the predicted object characteristic,
a difference between the predicted object characteristic and the respective object characteristic,
a difference between the predicted pool characteristic and the pool characteristic,
a rate of change of the respective object characteristic, or
a rate of change of the pool characteristic, wherein identifying the particular object is based at least in part on determining whether the one or more thresholds are satisfied;
identifying, by the data analysis computing system, a particular operation, of the plurality of operations or a second plurality of operations, based at least in part on the normalized results of the simulated performance of the operation, determining whether the one or more thresholds are satisfied, and monitoring, in real time, the first data associated with the object pool, wherein each operation of the second plurality of operations comprises an operation to modify the object pool;
causing, by the data analysis computing system, automatic performance of the particular operation by a first computing device without modifying the components data, the plurality of electronic files, or the plurality of movable physical properties, wherein data security of the components data, the plurality of electronic files, and the plurality of movable physical properties are improved based on causing automatic performance of the particular operation without modifying the components data, the plurality of electronic files, or the plurality of movable physical properties;
causing, by the data analysis computing system, using one or more second network communications received from the ratings computing system via the one or more networks, generation of an updated pool characteristic of the object pool based at least in part on causing automatic performance of the particular operation; and
causing, by the data analysis computing system, modification of second data associated with the object pool based on the updated pool characteristic, wherein the pool characteristic and the updated pool characteristic result in different modifications of the second data associated with the object pool.
2 . The computer-implemented method of claim 1 , wherein the at least a portion of the plurality of object characteristics are further based at least in part on a plurality of attributes of the plurality of objects, wherein causing automatic performance of the particular operation comprises:
causing modification of a particular attribute of the particular object, wherein the modification of the particular attribute causes an adjustment to the pool characteristic.
3 . The computer-implemented method of claim 1 , wherein causing generation of the updated pool characteristic comprises:
transmitting, to the ratings computing system via the one or more networks, a request for the updated pool characteristic based at least in part on causing automatic performance of the particular operation; and
obtaining, from the ratings computing system via the one or more networks, the updated pool characteristic based at least in part on transmitting, to the ratings computing system via the one or more networks, the request for the updated pool characteristic.
4 . The computer-implemented method of claim 1 , wherein simulating performance of the operation comprises performing a monte carlo simulation based on one or more random variables associated with the object.
5 . The computer-implemented method of claim 1 , wherein simulating performance of the operation comprises:
identifying one or more attributes of the object comprising the attribute of the object; and
identifying one or more attributes of the environment, wherein the one or more attributes of the environment indicate how the respective object characteristic is determined, wherein modelling the environment comprises:
modelling the environment based on the one or more attributes of the object and the one or more attributes of the environment, wherein the modeled environment comprises at least the object and at least one secondary object, and wherein the one or more attributes of the object are defined based at least in part on the at least one secondary object.
6 . The computer-implemented method of claim 1 , wherein identifying the particular object further comprises:
determining the simulated performance of the operation does not satisfy one or more additional thresholds, wherein determining the simulated performance of the operation does not satisfy one or more additional thresholds comprises at least one of:
determining indirect modification to the attribute of the object does not satisfy one or more sub-thresholds,
determining that the data analysis computing system is not authorized to perform the operation,
determining the simulated performance of the operation results in at least one of a decreased pool characteristic of the object pool as compared to the pool characteristic or a decreased object characteristic of the object as compared to the respective object characteristic, or
determining the simulated performance of the operation results in a modification to at least one of the portion of the components data associated with the object, the respective electronic file identified by the object, or the respective movable physical property of the object; and
based at least in part on determining the simulated performance of the operation does not satisfy the one or more additional thresholds, at least one of:
removing the operation from the plurality of operations,
causing display of a graphical user interface via a user computing device, the graphical user interface indicating the operation,
adjusting the operation,
adjusting a second operation of the plurality of operations, or
generating a third operation to add to the plurality of operations.
7 . The computer-implemented method of claim 1 , wherein the particular object is a first object, further comprising:
causing removal of the first object from the plurality of objects;
causing addition of a second object to the plurality of objects, wherein the second object is identified based at least in part on one or more similarities between the first object and the second object;
causing an adjustment to an attribute of the first object; or
causing provision of the object pool from a third entity to a fourth entity.
8 . The computer-implemented method of claim 1 , wherein identifying the particular object further comprises, for each object of the plurality of objects:
comparing the predicted object characteristic to the one or more thresholds; and
determining whether the object qualifies for rehabilitation based at least in part on comparing the predicted object characteristic to the one or more thresholds,
the method further comprising:
causing removal of the particular object from the plurality of objects based at least in part on determining that the particular object does not qualify for rehabilitation.
9 . The computer-implemented method of claim 1 , wherein identifying the particular object further comprises, for each object of the plurality of objects:
comparing the predicted object characteristic to the one or more thresholds; and
determining whether the object qualifies for rehabilitation based at least in part on comparing the predicted object characteristic to the one or more thresholds,
the method further comprising:
causing an adjustment to an attribute of the particular object based at least in part on determining the particular object qualifies for rehabilitation.
10 . The computer-implemented method of claim 1 , further comprising:
identifying the particular object for removal from the plurality of objects; and
transmitting, to the pool generation computing system via the one or more networks, a request to remove the particular object from the plurality of objects based at least in part on identifying the particular object for removal from the plurality of objects.
11 . The computer-implemented method of claim 1 , further comprising:
determining a particular predicted pool characteristic of the object pool based at least in part on identifying the particular object and identifying the particular operation;
transmitting, to a second computing device, the particular predicted pool characteristic; and
receiving, from the second computing device, a request to cause performance of the particular operation based at least in part on transmitting, to the second computing device, the particular predicted pool characteristic.
12 . The computer-implemented method of claim 1 , wherein identifying the particular object comprises, for each object of the plurality of objects:
identifying one or more computing devices comprising at least one of a first computing device associated with the first entity or a second computing device associated with the second entity;
monitoring network traffic associated with the one or more computing devices, wherein monitoring the network traffic comprises monitoring computer transmissions, posts, or network traffic statistics associated with the one or more computing devices; and
updating the respective object characteristic based at least in part on the network traffic.
13 . The computer-implemented method of claim 1 , wherein identifying the particular object comprises, for each object of the plurality of objects:
providing the object data to a machine learning model, wherein the machine learning model is trained on training data to identify at least one of the predicted object characteristic, the respective object characteristic, one or more attributes of the object, or a respective operation for the respective object to adjust the one or more attributes, wherein the machine learning model is configured to output the at least one of the predicted object characteristic, the respective object characteristic, the one or more attributes of the object, or the respective operation for the respective object based at least in part on the object data; and
obtaining an output of the machine learning model, wherein the output identifies the at least one of the predicted object characteristic, the respective object characteristic, the one or more attributes of the object, or the respective operation for the respective object.
14 . The computer-implemented method of claim 1 , wherein the particular object corresponds to a business loan, wherein the business loan is established between the first entity and the second entity, wherein an object characteristic of the particular object is based at least in part on one or more of:
a bank rating associated with at least one of the first entity or the second entity;
a number of trade lines associated with the at least one of the first entity or the second entity;
a number of loans associated with the at least one of the first entity or the second entity;
a standing of a business associated with the at least one of the first entity or the second entity;
a status of a business associated with the at least one of the first entity or the second entity;
a quantity of assets of a business associated with the at least one of the first entity or the second entity; or
an age of a business associated with the at least one of the first entity or the second entity.
15 . The computer-implemented method of claim 1 , wherein the particular object corresponds to a business loan, wherein the business loan is established between the first entity and the second entity, wherein the particular operation comprises an operation to establish or recommend establishment of at least one of:
an additional business loan for at least one of the first entity or the second entity;
a plurality of business loans for the at least one of the first entity or the second entity;
a lease for the at least one of the first entity or the second entity; or
a trade line for the at least one of the first entity or the second entity.
16 . The computer-implemented method of claim 1 , wherein each object characteristic of the plurality of object characteristics identifies a value of a corresponding object of the plurality of objects, wherein the pool characteristic identifies a value of the object pool, wherein the updated pool characteristic identifies an updated value of the object pool, and wherein the pool characteristic is based at least in part on the plurality of object characteristics.
17 . The computer-implemented method of claim 1 , wherein a respective movable physical property of the particular object corresponds to a business loan amount.
18 . A computer-implemented method of identifying a task for a business loan of a pool of business loans to improve a rating of the pool of business loans, enabling a user to cause an update to the rating of the pool of business loans without modifying private data associated with the business loan, the method comprising:
as implemented by a hardware processor of a data analysis computing system in communication via one or more networks with a pool generation computing system and a ratings computing system, wherein the hardware processor is configured to execute computer-executable instructions:
identifying, by the data analysis computing system, using one or more first network communications received from the pool generation computing system via the one or more networks, a set of business loan data, wherein the set of business loan data is associated with the pool of business loans;
monitoring, in real time, data associated with the pool of business loans, wherein monitoring, in real time, the data associated with the pool of business loans comprises performing a web crawl;
analyzing, by the data analysis computing system, the set of business loan data, wherein analyzing the set of business loan data comprises, for each business loan of the pool of business loans, without modifying private data associated with the business loan:
identifying, by the data analysis computing system, one or more attributes associated with the business loan,
predicting, by the data analysis computing system, an adjustment of a value of the business loan based at least in part on an adjustment of an attribute of the one or more attributes,
predicting, by the data analysis computing system, an adjustment of a value of the pool of business loans based at least in part on the adjustment of the value of the business loan, wherein the value of the pool of business loans is based on the private data associated with the business loan, wherein the value of the pool of business loans is based on one or more second network communications received by the data analysis computing system from the ratings computing system, and
comparing, by the data analysis computing system, the predicted adjusted value of the pool of business loans to one or more thresholds;
determining, by the data analysis computing system, that an adjusted value of at least one business loan of the pool of business loans is associated with an adjusted value of the pool of business loans that satisfies the one or more thresholds based at least in part on analyzing the set of business loan data, wherein the adjusted value of the at least one business loan is based at least in part on the adjustment of the attribute;
determining, by the data analysis computing system, one or more tasks for an entity associated with the at least one business loan based at least in part on monitoring, in real time, the data associated with the pool of business loans and in response to determining that the adjusted value of the at least one business loan is associated with the adjusted value of the pool of business loans, wherein the entity comprises a pool holder of the pool of business loans, a loan holder of the at least one business loan, a prospective pool holder of the pool of business loans, or a prospective loan holder of the at least one business loan;
routing, by the data analysis computing system, the one or more tasks to a computing device; and
causing, by the data analysis computing system, the computing device to automatically perform the one or more tasks without modifying private data associated with the pool of business loans, wherein performance of the one or more tasks results in a corresponding adjusted value of the pool of business loans, and wherein data security of the private data associated with the pool of business loans is improved based on causing the computing device to automatically perform the one or more tasks without modifying the private data associated with the pool of business loans.
19 . A data analysis apparatus comprising:
a memory circuit storing computer-executable instructions; and
a hardware processor of a data analysis computing system in communication via one or more networks with a pool generation computing system and a ratings computing system, wherein the hardware processor is configured to execute the computer-executable instructions, wherein execution of the computer-executable instructions causes the hardware processor to:
identify, by the data analysis computing system, using one or more first network communications received from the pool generation computing system via the one or more networks, a first set of loan data and a second set of loan data, wherein the first set of loan data is associated with a first pool of loans and the second set of loan data is associated with a second pool of loans, wherein the first pool of loans is associated with a first rating and the second pool of loans is associated with a second rating, and wherein the first rating is based on private data associated with the first pool of loans and the second rating is based on private data associated with the second pool of loans, wherein the first rating and the second rating are based on one or more second network communications received by the data analysis computing system from the ratings computing system;
monitor, in real time, data associated with at least one of the first pool of loans or the second pool of loans, wherein to monitor, in real time, the data associated with the at least one of the first pool of loans or the second pool of loans the execution of the computer-executable instructions causes the hardware processor to perform a web crawl;
identify, by the data analysis computing system, for each loan of the first pool of loans, one or more first attributes and, for each loan of the second pool of loans, one or more second attributes;
predict, by the data analysis computing system, for each loan of the first pool of loans, without modifying the private data associated with the first pool of loans, an adjustment of a first loan characteristic based at least in part on an adjustment of an attribute of the one or more first attributes to generate a first predicted plurality of loan characteristics;
predict, by the data analysis computing system, an adjustment of the first rating for the first pool of loans based at least in part on the first predicted plurality of loan characteristics;
predict, by the data analysis computing system, for each loan of the second pool of loans, without modifying the private data associated with the second pool of loans, an adjustment of a second loan characteristic based at least in part on an adjustment of an attribute of the one or more second attributes to generate a second predicted plurality of loan characteristics;
predict, by the data analysis computing system, an adjustment of the second rating for the second pool of loans based at least in part on the second predicted plurality of loan characteristics;
compare, by the data analysis computing system, adjustment of the first rating to the adjustment of the second rating;
generate, by the data analysis computing system, an alert based at least in part on comparing the adjustment of the first rating to the adjustment of the second rating;
route, by the data analysis computing system, the alert to a user computing device; and
cause, by the data analysis computing system, a computing device to automatically perform one or more tasks without modifying the private data associated with the first pool of loans or the private data associated with the second pool of loans, wherein the one or more tasks are identified based at least in part on monitoring, in real time, the data associated with the at least one of the first pool of loans or the second pool of loans, wherein performance of the one or more tasks results in a corresponding adjusted value of the first pool of loans or the second pool of loans, and wherein data security of the private data associated with the first pool of loans and the private data associated with the second pool of loans are improved based on causing the computing device to automatically perform the one or more tasks without modifying the private data associated with the first pool of loans or the private data associated with the second pool of loans.
20 . A computer-implemented method of identifying a transformation operation for an object pool to improve a pool characteristic of the object pool without modifying private data defining individual objects of the object pool, the method comprising:
as implemented by a hardware processor of a data analysis computing system in communication via one or more networks with a pool generation computing system and a ratings computing system, wherein the hardware processor is configured to execute computer-executable instructions:
receiving, by the data analysis computing system, from the pool generation computing system, via the one or more networks, object data identifying an object pool, the object pool comprising a plurality of objects that are pooled such the plurality of objects are movable between entities, each object of the plurality of objects having a discrete impact on a pool characteristic of the object pool based at least in part on the object pool comprising the plurality of objects, wherein each object of the plurality of objects identifies an electronic file comprising an electronic signature, wherein each object of the plurality of objects is included in the object pool based at least in part on validation of a corresponding electronic signature, wherein each electronic file of the plurality of electronic files identifies a first entity, a second entity, and movable physical property, wherein a third party computing system defines components data that identifies a connection between the plurality of movable physical properties and the plurality of objects, wherein the plurality of objects are defined based at least in part on the components data, and wherein the data analysis computing system does not have authorization to modify the components data, the plurality of electronic files, or the plurality of movable physical properties;
identifying, by the data analysis computing system, using one or more first network communications received from the ratings computing system via the one or more networks, the pool characteristic of the object pool based at least in part on the object data, wherein the pool characteristic comprises a first dynamic assessment of the plurality of objects at a particular time as compared to another object pool, wherein the pool characteristic is based at least in part on an evaluation of the components data, the plurality of electronic files, or the plurality of movable physical properties, wherein the pool characteristic varies over time based at least in part on modification of at least one of the components data, the plurality of electronic files, or the plurality of movable physical properties;
determining, by the data analysis computing system, for each object of the plurality of objects, a respective object characteristic based at least in part on the object data, wherein the respective object characteristic comprises a second dynamic assessment of the object at a particular time as compared to another object, wherein at least a portion of the plurality of object characteristics are based at least in part on a portion of the components data associated with the object, a respective electronic file identified by the object, or a respective movable physical property of the object, wherein the respective object characteristic varies over time based at least in part on modification of at least one of the portion of the components data associated with the object, the respective electronic file identified by the object, or the respective movable physical property of the object;
identifying, by the data analysis computing system, a particular object of the plurality of objects based at least in part on one or more thresholds associated with the object pool, wherein identifying the particular object comprises, for each object of the plurality of objects:
modelling, by the data analysis computing system, an environment of the object;
simulating, by the data analysis computing system, performance of an operation of a plurality of operations within the modeled environment, wherein the data analysis computing system is programmed to differentiate between operations that result in a modification of data that the data analysis computing system is not authorized to modify or access and operations that result in a modification of data that the data analysis computing system is authorized to modify or access, wherein each operation of the plurality of operations comprises an operation to modify a non-object or an object not included in the object pool such that data associated with the first entity or the second entity is modified, wherein each operation indirectly causes modification to an attribute of the object without modifying the portion of the components data associated with the object, the respective electronic file identified by the object, or the respective movable physical property of the object,
generating, by the data analysis computing system, a predicted object characteristic of the object based at least in part on the respective object characteristic of the object and the simulated performance of the operation,
generating, by the data analysis computing system, a predicted pool characteristic of the object pool based at least in part on the predicted object characteristic of the object, wherein the method further comprises normalizing results of the simulated performance of the operation by generating the predicted object characteristic and the predicted pool characteristic, and
determining, by the data analysis computing system, whether the one or more thresholds are satisfied based on one or more of:
the predicted pool characteristic, the predicted object characteristic,
a difference between the predicted object characteristic and the respective object characteristic,
a difference between the predicted pool characteristic and the pool characteristic,
a rate of change of the respective object characteristic, or
a rate of change of the pool characteristic, wherein identifying the particular object is based at least in part on determining whether the one or more thresholds are satisfied;
identifying, by the data analysis computing system, a particular operation, of the plurality of operations or a second plurality of operations, based at least in part on the normalized results of the simulated performance of the operation and determining whether the one or more thresholds are satisfied, wherein each operation of the second plurality of operations comprises an operation to modify the object pool;
causing, by the data analysis computing system, automatic performance of the particular operation by a first computing device without modifying the components data, the plurality of electronic files, or the plurality of movable physical properties, wherein data security of the components data, the plurality of electronic files, and the plurality of movable physical properties are improved based on causing automatic performance of the particular operation without modifying the components data, the plurality of electronic files, or the plurality of movable physical properties, wherein causing automatic performance of the particular operation comprises:
routing machine instructions, via a network connection, to the first computing device, wherein in response to receiving the machine instructions, the first computing device automatically performs the particular operation, wherein the particular operation comprises an operation to automatically call, text, message, email, or mail a reference associated with a particular entity, and wherein the first computing device awaits receipt of the machine instructions to perform the particular operation;
causing, by the data analysis computing system, using one or more second network communications received from the ratings computing system via the one or more networks, generation of an updated pool characteristic of the object pool based at least in part on causing automatic performance of the particular operation; and
causing, by the data analysis computing system, modification of data associated with the object pool based on the updated pool characteristic, wherein the pool characteristic and the updated pool characteristic result in different modifications of the data associated with the object pool.
21 . The computer-implemented method of claim 20 , wherein the object data and the pool characteristic vary over time such that the object pool at a first time is different from the object pool at a second time.
22 . A computer-implemented method of identifying a task for a business loan of a pool of business loans to improve a rating of the pool of business loans, enabling a user to cause an update to the rating of the pool of business loans without modifying private data associated with the business loan, the method comprising:
as implemented by a hardware processor of a data analysis computing system in communication via one or more networks with a pool generation computing system and a ratings computing system, wherein the hardware processor is configured to execute computer-executable instructions:
identifying, by the data analysis computing system, using one or more first network communications received from the pool generation computing system via the one or more networks, a set of business loan data, wherein the set of business loan data is associated with the pool of business loans;
analyzing, by the data analysis computing system, the set of business loan data, wherein analyzing the set of business loan data comprises, for each business loan of the pool of business loans, without modifying private data associated with the business loan:
identifying, by the data analysis computing system, one or more attributes associated with the business loan,
predicting, by the data analysis computing system, an adjustment of a value of the business loan based at least in part on an adjustment of an attribute of the one or more attributes,
predicting, by the data analysis computing system, an adjustment of a value of the pool of business loans based at least in part on the adjustment of the value of the business loan, wherein the value of the pool of business loans is based on the private data associated with the business loan, wherein the value of the pool of business loans is based on one or more second network communications received by the data analysis computing system from the ratings computing system, and
comparing, by the data analysis computing system, the predicted adjusted value of the pool of business loans to one or more thresholds;
determining, by the data analysis computing system, that an adjusted value of at least one business loan of the pool of business loans is associated with an adjusted value of the pool of business loans that satisfies the one or more thresholds based at least in part on analyzing the set of business loan data, wherein the adjusted value of the at least one business loan is based at least in part on the adjustment of the attribute;
determining, by the data analysis computing system, one or more tasks for an entity associated with the at least one business loan in response to determining that the adjusted value of the at least one business loan is associated with the adjusted value of the pool of business loans, wherein the entity comprises a pool holder of the pool of business loans, a loan holder of the at least one business loan, a prospective pool holder of the pool of business loans, or a prospective loan holder of the at least one business loan;
routing, by the data analysis computing system, the one or more tasks to a computing device; and
causing, by the data analysis computing system, the computing device to automatically perform the one or more tasks without modifying private data associated with the pool of business loans, wherein performance of the one or more tasks results in a corresponding adjusted value of the pool of business loans, and wherein data security of the private data associated with the pool of business loans is improved based on causing the computing device to automatically perform the one or more tasks without modifying the private data associated with the pool of business loans, wherein causing the computing device to automatically perform the one or more tasks without modifying the private data associated with the pool of business loans comprises:
routing machine instructions, via a network connection, to the computing device, wherein in response to receiving the machine instructions, the computing device automatically performs the one or more tasks, wherein the one or more tasks comprises a task to automatically call, text, message, email, or mail a reference associated with a particular entity, and wherein the computing device awaits receipt of the machine instructions to perform the one or more tasks.
23 . A data analysis apparatus comprising:
a memory circuit storing computer-executable instructions; and
a hardware processor of a data analysis computing system in communication via one or more networks with a pool generation computing system and a ratings computing system, wherein the hardware processor is configured to execute the computer-executable instructions, wherein execution of the computer-executable instructions causes the hardware processor to:
identify, by the data analysis computing system, using one or more first network communications received from the pool generation computing system via the one or more networks, a first set of loan data and a second set of loan data, wherein the first set of loan data is associated with a first pool of loans and the second set of loan data is associated with a second pool of loans, wherein the first pool of loans is associated with a first rating and the second pool of loans is associated with a second rating, and wherein the first rating is based on private data associated with the first pool of loans and the second rating is based on private data associated with the second pool of loans, wherein the first rating and the second rating are based on one or more second network communications received by the data analysis computing system from the ratings computing system;
identify, by the data analysis computing system, for each loan of the first pool of loans, one or more first attributes and, for each loan of the second pool of loans, one or more second attributes;
predict, by the data analysis computing system, for each loan of the first pool of loans, without modifying the private data associated with the first pool of loans, an adjustment of a first loan characteristic based at least in part on an adjustment of an attribute of the one or more first attributes to generate a first predicted plurality of loan characteristics;
predict, by the data analysis computing system, an adjustment of the first rating for the first pool of loans based at least in part on the first predicted plurality of loan characteristics;
predict, by the data analysis computing system, for each loan of the second pool of loans, without modifying the private data associated with the second pool of loans, an adjustment of a second loan characteristic based at least in part on an adjustment of an attribute of the one or more second attributes to generate a second predicted plurality of loan characteristics;
predict, by the data analysis computing system, an adjustment of the second rating for the second pool of loans based at least in part on the second predicted plurality of loan characteristics;
compare, by the data analysis computing system, adjustment of the first rating to the adjustment of the second rating;
generate, by the data analysis computing system, an alert based at least in part on comparing the adjustment of the first rating to the adjustment of the second rating;
route, by the data analysis computing system, the alert to a user computing device; and
cause, by the data analysis computing system, a computing device to automatically perform one or more tasks without modifying the private data associated with the first pool of loans or the private data associated with the second pool of loans, wherein performance of the one or more tasks results in a corresponding adjusted value of the first pool of loans or the second pool of loans, and wherein data security of the private data associated with the first pool of loans and the private data associated with the second pool of loans are improved based on causing the computing device to automatically perform the one or more tasks without modifying the private data associated with the first pool of loans or the private data associated with the second pool of loans, wherein to cause the computing device to automatically perform the one or more tasks without modifying the private data associated with the first pool of loans or the private data associated with the second pool of loans, the execution of the computer-executable instructions causes the hardware processor to:
route machine instructions, via a network connection, to the computing device, wherein in response to receiving the machine instructions, the computing device automatically performs the one or more tasks, wherein the one or more tasks comprises a task to automatically call, text, message, email, or mail a reference associated with a particular entity, and wherein the computing device awaits receipt of the machine instructions to perform the one or more tasks.