Scaling transactions with signal analysis
Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises determining a pattern within received data based on a periodicity associated with the received data; in response to a calculated signal score associated with the determined pattern of the received data meeting or exceeding a predetermined threshold transaction amount, tracing at least one location associated with the received data; and dynamically suspending an action associated with an account that generated the received data in response to the traced location not being associated with a historical baseline of the account.
1. A computer-implemented method comprising:
transforming, by a processor set, received data from a time function to a frequency function of the received data;
applying, by the processor set, a filter to the frequency function to remove transactional noise;
utilizing, by the processor set, a fraud detection classification model to determine a pattern within the received data based on standardizing the received data and a periodicity associated with the frequency function of the received data;
generating, by the processor set, a signal score associated with the determined pattern of the received data based, at least in part, on an identified cycle value indicative of a complexity of the received data wherein the identified cycle value is based on an amount of time for a data cleaning algorithm to determine the periodicity;
in response to determining that the generated signal score meets or exceeds a predetermined threshold transaction amount, tracing, by the processor set, at least one location associated with the received data;
dynamically suspending, by the processor set, a processing of a computer-based transaction associated with an account that generated the received data in response to the traced location not being associated with a historical baseline of the account; and
in response to receiving a user input, processing, by the processor set, a suspended computer-based transaction.
2. The computer-implemented method of claim 1 , wherein determining the pattern comprises:
calculating an auto-covariance associated with the received data by converting the received data from a time domain to a frequency domain;
identifying peaks within the calculated auto-covariance associated with the received data, wherein the identified peaks are a maximum amount over a concentrated period of time;
determining a periodicity of the identified peaks by applying a statistical standardization to the identified peaks within the calculated auto-covariance; and
determining a pattern associated with the received data based on the determined periodicity of the identified peaks.
3. The computer-implemented method of claim 2 , wherein determining the periodicity of the determined pattern comprises:
converting the received data from the time domain to the frequency domain by applying a Fourier transform algorithm.
4. The computer-implemented method of claim 2 , wherein determining the periodicity of the determined pattern comprises:
tracing at least one location associated with the received data in response to a calculated frequency associated with the received data meeting or exceeding a predetermined threshold of transaction frequency.
5. The computer-implemented method of claim 4 , further comprising generating a line graph displaying the calculated auto-covariance and identified peaks of the received data.
6. The computer-implemented method of claim 5 , further comprising generating a second line graph by removing received data that does not meet or exceed the predetermined threshold transaction frequency.
7. The computer-implemented method of claim 1 , wherein the location is a physical location and a technical location.
8. A computer program product comprising:
one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising:
program instructions to transform received data from a time function to a frequency function of the received data;
program instructions to apply a filter to the frequency function to remove transactional noise;
program instructions to utilize a fraud detection classification model to determine a pattern within the received data based on standardizing the received data and a periodicity associated with the frequency function of the received data;
program instructions to generate a signal score associated with the determined pattern of the received data based, at least in part, on an identified cycle value indicative of a complexity of the received data wherein the identified cycle value is based on an amount of time for a data cleaning algorithm to determine the periodicity;
in response to determining that the generated signal score meets or exceeds a predetermined threshold transaction amount, program instructions to trace at least one location associated with the received data;
program instructions to dynamically suspend a processing of a computer-based transaction associated with an account that generated the received data in response to the traced location not being associated with a historical baseline of the account; and
in response to receiving a user input, processing, by the processor set, a suspended computer-based transaction.
9. The computer program product of claim 8 , wherein the program instructions to determine the pattern comprise:
program instructions to calculate an auto-covariance associated with the received data by converting the received data from a time domain to a frequency domain;
program instructions to identify peaks within the calculated auto-covariance associated with the received data, wherein the identified peaks are a maximum amount over a concentrated period of time;
program instructions to determine a periodicity of the identified peaks by applying a statistical standardization to the identified peaks within the calculated auto-covariance; and
program instructions to determine a pattern associated with the received data based on the determined periodicity of the identified peaks.
10. The computer program product of claim 9 , wherein the program instructions to determine the periodicity of the determined pattern comprise:
program instructions to convert the received data from the time domain to the frequency domain by applying a Fourier transform algorithm.
11. The computer program product of claim 9 , wherein the program instructions to determine the periodicity of the determined pattern comprise:
program instructions to trace at least one location associated with the received data in response to a calculated frequency associated with the received data meeting or exceeding a predetermined threshold of transaction frequency.
12. The computer program product of claim 11 , wherein the program instructions stored on the one or more computer readable storage media further comprise:
program instructions to generate a line graph displaying the calculated auto-covariance and identified peaks of the received data.
13. The computer program product of claim 12 , wherein the program instructions stored on the one or more computer readable storage media further comprise:
program instructions to generate a second line graph by removing received data that does not meet or exceed the predetermined threshold transaction frequency.
14. The computer program product of claim 8 , wherein the location is a physical location and a technical location.
15. A computer system comprising:
one or more computer processors;
one or more computer readable storage media; and
program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising:
program instructions to transform received data from a time function to a frequency function of the received data;
program instructions to apply a filter to the frequency function to remove transactional noise;
program instructions to utilize a fraud detection classification model to determine a pattern within the received data based on standardizing the received data and a periodicity associated with the frequency function of the received data;
program instructions to generate a signal score associated with the determined pattern of the received data based, at least in part, on an identified cycle value indicative of a complexity of the received data wherein the identified cycle value is based on an amount of time for a data cleaning algorithm to determine the periodicity;
in response to determining that the generated signal score meets or exceeds a predetermined threshold transaction amount, program instructions to trace at least one location associated with the received data;
program instructions to dynamically suspend a processing of a computer-based transaction associated with an account that generated the received data in response to the traced location not being associated with a historical baseline of the account; and
in response to receiving a user input, processing, by the processor set, a suspended computer-based transaction.
16. The computer system of claim 15 , wherein the program instructions to determine the pattern comprise:
program instructions to calculate an auto-covariance associated with the received data by converting the received data from a time domain to a frequency domain;
program instructions to identify peaks within the calculated auto-covariance associated with the received data, wherein the identified peaks are a maximum amount over a concentrated period of time;
program instructions to determine a periodicity of the identified peaks by applying a statistical standardization to the identified peaks within the calculated auto-covariance; and
program instructions to determine a pattern associated with the received data based on the determined periodicity of the identified peaks.
17. The computer system of claim 16 , wherein the program instructions to determine the periodicity of the determined pattern comprise:
program instructions to convert the received data from the time domain to the frequency domain by applying a Fourier transform algorithm.
18. The computer system of claim 16 , wherein the program instructions to determine the periodicity of the determined pattern comprise:
program instructions to trace at least one location associated with the received data in response to a calculated frequency associated with the received data meeting or exceeding a predetermined threshold of transaction frequency.
19. The computer system of claim 18 , wherein the program instructions stored on the one or more computer readable storage media further comprise:
program instructions to generate a line graph displaying the calculated auto-covariance and identified peaks of the received data.
20. The computer system of claim 19 , wherein the program instructions stored on the one or more computer readable storage media further comprise:
program instructions to generate a second line graph by removing received data that does not meet or exceed the predetermined threshold transaction frequency.