Apparatuses and methods for calculating foreign exchange advantages

1 . An apparatus, the apparatus comprising:

at least a processor; and

a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:

acquire action data from an entity;

acquire external data from one or more data sources, wherein the external data is related to the action data;

process the action data comprising a plurality of action data elements, wherein processing the action data comprises:

augmenting the action data as a function of the external data;

generating non-identifiable action data as a function of the augmented action data, wherein generating the non-identifiable action data further comprises:

identifying personally identifiable information within the augmented action data; and

transforming the personally identifiable information within the augmented action data into non-identifiable information of the non-identifiable action data to anonymize the personally identifiable information and protect data privacy, wherein transforming the personally identifiable information comprises:

 encrypting the personally identifiable information using an encryption algorithm to transform the personally identifiable information into hash using at least one cryptographic hash function so that sensitive information of the personally identifiable information can be anonymized to reduce risk of a data breach over network communications;

identifying data irregularity of the non-identifiable action data, wherein

identifying the data irregularity comprises:

generating irregularity training data, wherein:

 the irregularity training data comprises correlations between exemplary anonymized action data and exemplary data irregularities; and

 the irregularity training data is updated iteratively through a feedback loop;

training an irregularity machine-learning model using the irregularity training data; and

identifying the data irregularity of the non-identifiable action data using the trained irregularity machine-learning model; and

classifying the non-identifiable action data against a data store comprising at least a foreign exchange rate;

generate a conversion record as a function of the non-identifiable action data and the data irregularity, by:

generating a foreign exchange score;

generating, by a linear optimization module, an optimized foreign exchange score by using a linear optimization program configured to optimize an objective function, given at least a constraint, wherein the at least a constraint comprises a plurality of conversion threshold requirements pertaining to a transaction; and

scoring, by the linear optimization module, a conversion threshold requirement using the objective function; and

output, prior to a foreign exchange transfer and over a data network, the conversion record to a third-party computing device operated by an end user, wherein:

the conversion record is presented to the third-party computing device in a notification format as a nudge; and

the nudge is structured to selectively encourage the end user to initiate the foreign exchange transfer or discourage the end user to initiate the foreign exchange transfer based on content of the conversion record including the data irregularity.

2 . The apparatus of claim 1 , wherein augmenting the action data further comprises:

identifying a target data point within the action data; and

integrating the external data into the target data point to generate the augmented action data.

3 . The apparatus of claim 1 , wherein processing the action data further comprises:

training an action data classifier using action training data, wherein the action training data comprises a plurality of action data element sets as input correlated to a plurality of originators and receivers as output; and

classifying the plurality of action data elements to at least one originator and one receiver using the trained action data classifier.

4 . The apparatus of claim 1 , wherein generating the conversion record further comprises:

constructing a decision tree as a function of the plurality of conversion threshold requirements, wherein:

the decision tree comprises a plurality of nodes; and

each node of the plurality of nodes comprises a data structure comprising at least one conversion threshold requirement of the plurality of conversion threshold requirements;

generating the foreign exchange score as a function of a decision tree traversal based on the non-identifiable action data; and

generating the conversion record as a function of the foreign exchange score.

5 . The apparatus of claim 4 , wherein the plurality of conversion threshold requirements comprises a data irregularity threshold requirement.

6 . The apparatus of claim 4 , wherein traversing the decision tree comprises:

comparing the non-identifiable action data against the at least one conversion threshold requirement of the plurality of conversion threshold requirements at a first node of the plurality of nodes; and

passing the non-identifiable action data to a second node of the plurality of nodes as a function of the comparison, wherein the second node is connected to the first node.

7 . The apparatus of claim 4 , wherein the traversing the decision tree comprises:

generating a foreign exchange sub-score for each node of the plurality of nodes as a function of the non-identifiable action data and the at least one conversion threshold requirement associated with each node of the plurality nodes.

8 . The apparatus of claim 4 , wherein generating the foreign exchange score further comprises:

aggregating a plurality of foreign exchange sub-scores associated with the plurality of nodes of the decision tree into the foreign exchange score; and

generating the conversion record as a function of the aggregation of the plurality of foreign exchange sub-scores.

9 . The apparatus of claim 4 , wherein the plurality of conversion threshold requirements further comprises a real-time process threshold requirement.

10 . A method, the method comprising:

acquiring, by at least a processor, action data from an entity;

acquiring, by the at least a processor, external data from one or more data sources, wherein the external data is related to the action data;

processing, by the at least a processor, the action data comprising a plurality of action data elements, wherein processing the action data comprises:

augmenting the action data as a function of the external data;

generating non-identifiable action data as a function of the augmented action data, wherein generating the non-identifiable action data further comprises:

identifying personally identifiable information within the augmented action data; and

transforming the personally identifiable information within the augmented action data into non-identifiable information of the non-identifiable action data to anonymize the personally identifiable information and protect data privacy, wherein transforming the personally identifiable information comprises:

encrypting the personally identifiable information using an encryption algorithm to transform the personally identifiable information into hash using at least one cryptographic hash function so that sensitive information of the personally identifiable information can be anonymized to reduce risk of a data breach in network communications;

identifying data irregularity of the non-identifiable action data, wherein identifying the data irregularity comprises:

generating irregularity training data, wherein:

the irregularity training data comprises correlations between exemplary anonymized action data and exemplary data irregularities; and

the irregularity training data is updated iteratively through a feedback loop;

training an irregularity machine-learning model using the irregularity training data; and

identifying the data irregularity of the non-identifiable action data using the trained irregularity machine-learning model; and

classifying the non-identifiable action data against a data store comprising at least a foreign exchange rate;

generating, by the at least a processor, a conversion record as a function of the non-identifiable action data and the data irregularity, by:

generating a foreign exchange score;

generating, by a linear optimization module, an optimized foreign exchange score by using a linear optimization program configured to optimize an objective function, given at least a constraint, wherein the at least a constraint comprises a plurality of conversion threshold requirements pertaining to a transaction; and

scoring, by the linear optimization module, a conversion threshold requirement using the objective function; and

outputting, by the at least a processor, prior to a foreign exchange transfer and over a data network, the conversion record to a third-party computing device operated by an end user, wherein:

the conversion record is presented to the third-party computing device in a notification format as a nudge;

the nudge is structured to selectively encourage the end user to initiate the foreign exchange transfer or discourage the end user to initiate the foreign exchange transfer based on content of the conversion record including the data irregularity.

11 . The method of claim 10 , further comprising:

identifying, by the at least a processor, a target data point within the action data; and

integrating, by the at least a processor, the external data into the target data point to generate the augmented action data.

12 . The method of claim 10 , further comprising:

training, by the at least a processor, an action data classifier using action training data, wherein the action training data comprises a plurality of action data element sets as input correlated to a plurality of originators and receivers as output; and

classifying, by the at least a processor, the plurality of action data elements to at least one originator and one receiver using the trained action data classifier.

13 . The method of claim 10 , further comprising:

constructing, by the at least a processor, a decision tree as a function of the plurality of conversion threshold requirements, wherein:

the decision tree comprises a plurality of nodes; and

each node of the plurality of nodes comprises a data structure comprising at least one conversion threshold requirement of the plurality of conversion threshold requirements;

generating, by the at least a processor, the foreign exchange score as a function of a decision tree traversal based on the non-identifiable action data; and

generating, by the at least a processor, the conversion record as a function of the foreign exchange score.

14 . The method of claim 13 , wherein the plurality of conversion threshold requirements comprises a data irregularity threshold requirement.

15 . The method of claim 13 , further comprising:

comparing, by the at least a processor, the non-identifiable action data against the at least one conversion threshold requirement of the plurality of conversion threshold requirements at a first node of the plurality of nodes; and

passing, by the at least a processor, the non-identifiable action data to a second node of the plurality of nodes as a function of the comparison, wherein the second node is connected to the first node.

16 . The method of claim 13 , further comprising:

generating, by the at least a processor, a foreign exchange sub-score for each node of the plurality of nodes as a function of the non-identifiable action data and the at least one conversion threshold requirement associated with each node of the plurality nodes.

17 . The method of claim 13 , further comprising:

aggregating, by the at least a processor, a plurality of foreign exchange sub-scores associated with the plurality of nodes of the decision tree into the foreign exchange score; and

generating, by the at least a processor, the conversion record as a function of the aggregation of the plurality of foreign exchange sub-scores.

18 . The method of claim 13 , wherein the plurality of conversion threshold requirements further comprises a real-time process threshold requirement.