System and method for identifying comparables

1. A computer-implemented method for identifying comparables, the method comprising:

implementing a machine learning algorithm operating on a computer processor configured to:

collect, through an electronic interface, input data from a plurality of electronic data sources of a potential comparable for inclusion into a transfer pricing benchmarking set, wherein the transfer pricing benchmarking set is generated by a transfer pricing benchmarking activity which comprises search and selection of unrelated parties to establish a benchmark for the pricing of a cross-border transaction between one or more related parties under common ownership or control, wherein the electronic data sources include: a business description from a commercially available database, financial data of the potential comparable, and a SIC or NACE code associated with the potential comparable;

determine, via the machine learning algorithm, a function, product, and service of the potential comparable using a corresponding function classifier, product classifier, and service classifier;

receive, through the electronics interface, attributes of a tested party;

automatically execute, via the machine learning algorithm, a scoring process to calculate a similarity score for the potential comparable, wherein the similarity score represents a similarity between the potential comparable and the tested party;

automatically generate, via the machine learning algorithm, a recommendation to accept the potential comparable as an acceptable comparable for transfer pricing benchmarking, reject the potential comparable as a rejected comparable, or subject the potential comparable to further review; and

generate, via the machine learning algorithm, synthetic training data for the machine learning algorithm by (1) predicting a plurality of labels and keyword counts from the plurality of electronic data sources, (2) generating heuristic labels for the predicted plurality of labels based on a plurality of heuristic rules, the rules based on experience with prior similar problems, and (3) generating probabilistic training labels for a semi-supervised deep learning model; and

improve the machine learning algorithm by using the synthetic training data and feedback from the automatically generated recommendation to train the machine learning algorithm.

2. The computer-implemented method of claim 1 , further comprising:

automatically generating, with the computer processor, a written justification for the recommendation to accept or reject the potential comparable, wherein the written justification is specific to the potential comparable that has been analyzed.

3. The computer-implemented method of claim 2 , wherein the written justification is acceptable for justifying a transfer pricing use case.

4. The computer-implemented method of claim 1 , wherein the electronic data sources further include text obtained from a website of the comparable.

5. The computer-implemented method of claim 1 , further comprising:

generating, with the computer processor, probabilistic training data for the product classifier and the service classifier; and

training, with the computer processor, the product classifier and the service classifier using the probabilistic training data.

6. The computer-implemented method of claim 5 , wherein the probabilistic training data is generated using heuristic rules to generate heuristic labels.

7. The computer-implemented method of claim 6 , wherein the probabilistic training data is generated using an unsupervised label model.

8. The computer-implemented method of claim 1 , further comprising determining a risk of the potential comparable using a corresponding risk classifier.

9. A computer-implemented system for identifying comparables, the system comprising:

an electronic storage device; and

a machine learning algorithm operating on a computer processor that is programmed to:

collect, through an electronic interface, input data from a plurality of electronic data sources of a potential comparable for inclusion into a transfer pricing benchmarking set, wherein the transfer pricing benchmarking set is generated by a transfer pricing benchmarking activity which comprises search and selection of unrelated parties to establish a benchmark for the pricing of a cross-border transaction between one or more related parties under common ownership or control, wherein the electronic data sources include: a business description from a commercially available database, financial data of the potential comparable, and a SIC or NACE code associated with the potential comparable;

determine a function, product, and service of the potential comparable using a corresponding function classifier; product classifier, and service classifier;

receive, through the electronic interface, attributes of a tested party;

automatically execute a scoring process to calculate a similarity score for the potential comparable, wherein the similarity score represents a similarity between the potential comparable and the tested party;

automatically generate a recommendation to accept the potential comparable as an acceptable comparable for transfer pricing benchmarking, reject the potential comparable as a rejected comparable, or subject the potential comparable to further review;

generate, via the machine learning algorithm, synthetic training data for the machine learning algorithm by (1) predicting a plurality of labels and keyword counts from the plurality of electronic data sources, (2) generating heuristic labels for the predicted plurality of labels based on a plurality of heuristic rules, the rules based on experience with prior similar problems, and (3) generating probabilistic training labels for a semi-supervised deep learning model; and

improve the machine learning algorithm by using the synthetic training data and feedback from the automatically generated recommendation to train the machine learning algorithm.

10. The computer-implemented system of claim 9 , wherein the computer processor is further programmed to:

automatically generate a written justification for the recommendation to accept or reject the potential comparable, wherein the written justification is specific to the potential comparable that has been analyzed.

11. The computer-implemented system of claim 10 , wherein the written justification is acceptable for justifying a transfer pricing use case.

12. The computer-implemented system of claim 9 , wherein the electronic data sources further include text obtained from a website of the comparable.

13. The computer-implemented system of claim 9 , wherein the computer processor is further programmed to

generate probabilistic training data for the product classifier and the service classifier; and

train the product classifier and the service classifier using the probabilistic training data.

14. The computer-implemented system of claim 13 , wherein the probabilistic training data is generated using heuristic rules to generate heuristic labels.

15. The computer-implemented system of claim 14 , wherein the probabilistic training data is generated using an unsupervised label model.

16. The computer-implemented system of claim 9 , wherein the computer processor is further programmed to determine a risk of the potential comparable using a corresponding risk classifier.

17. A computer-implemented method for identifying comparables, the method comprising:

implementing a machine learning algorithm operating on a computer processor configured to:

(a) collect, through an electronic interface, input data from a plurality of electronic data sources of a potential comparable for inclusion into a transfer pricing benchmarking set, wherein the transfer pricing benchmarking set is generated by a transfer pricing benchmarking activity which comprises search and selection of unrelated parties to establish a benchmark for the pricing of a cross-border transaction between one or more related parties under common ownership or control, wherein the electronic data sources include: (i) a business description from a commercially available database, (ii) financial data of the potential comparable, (iii) a SIC or NACE code associated with the potential comparable, and (iv) text obtained from a website of the potential comparable;

(b) training, with the computer processor, the function classifier, the product classifier, and the service classifier using the labeled training data and the probabilistic training data;

(c) determine, via the machine learning algorithm, a function, product, and service of the potential comparable using the corresponding function classifier; product classifier, and service classifier;

(d) receive, through the electronic interface, attributes of a tested party;

(e) automatically execute, the machine learning algorithm, a scoring process to calculate a similarity score for the potential comparable, wherein the similarity score represents a similarity between the potential comparable and the tested party;

(f) automatically generate, via the machine learning algorithm, a recommendation to accept the potential comparable as an acceptable comparable for transfer pricing benchmarking or to reject the potential comparable as a rejected comparable; and

(g) automatically generate, via the machine learning algorithm, a written justification for the recommendation to accept or reject the potential comparable, wherein the written justification is specific to the potential comparable that has been analyzed

(h) generate, via the machine learning algorithm, synthetic training data for the machine learning algorithm by (1) predicting a plurality of labels and keyword counts from the plurality of electronic data sources, (2) generating heuristic labels for the predicted plurality of labels based on a plurality of heuristic rules, the rules based on experience with prior similar problems, and (3) generating probabilistic training labels for a semi-supervised deep learning model; and

(i) improve the machine learning algorithm by using the synthetic training data and feedback from the automatically generated recommendation to train the machine learning algorithm.