Using classified text and deep learning algorithms to identify support for financial advantage and provide notice
Deep learning is used to identify specific, potential financial advantage for an enterprise that are hidden in internal electronic documents. The system involves mining and using existing classifications of data (e.g., from previously sorted documents) to train one or more deep learning algorithms, and then examining internal electronic documents with the trained algorithm, to generate a scored output that will enable enterprise personnel to evaluate the identified documents for a potential financial advantage to the enterprise.
1. A method of using classified text and deep learning algorithms to identify financial advantage comprising:
creating or accessing one or more training datasets for textual data corresponding to one or more financial classifications, wherein said financial classifications comprises one or more financial advantage of interest;
training one or more deep learning algorithms using said one or more training datasets;
extracting an internal electronic document of an enterprise;
applying said one or more deep learning algorithms to said internal electronic document to identify and report any one of said one or more advantages of interest;
determining if said identified one of said one or more advantages of interest is a false positive or a true positive;
re-training said one or more deep learning algorithms if said identified one of said one or more advantages of interest is a false positive; and
saving said internal electronic document in a true positive database if said identified one of said one or more reported advantages of interest is a true positive.
2. The method of claim 1 , wherein said one or more deep learning algorithms is a framework for natural language processing of text.
3. The method of claim 1 , wherein said one or more deep learning algorithms is a recurrent neural network with a multiplicity of layers and various features, including but not limited to long short-term memory or gated recurrent units.
4. The method of claim 1 , wherein the one or more deep learning algorithms have been trained with different classifiers using previously classified data sourced and provided by one or more subject matter experts to become models for specific financial advantages.
5. The method of claim 1 , wherein each one of said one or more deep learning algorithms has also been trained with one or more datasets unrelated to the advantages of interest.
6. The method of claim 1 , wherein said internal electronic document is indexed.
7. The method of claim 6 , wherein each one of said one or more deep learning algorithms scores the data for accuracy with the deep learning model classification of the data.
8. The method of claim 7 , wherein said report any of said one or more advantages of interest comprises providing the scores and related data to one or more designated users.
9. The method of claim 8 , wherein the report may be limited to scores which surpass a specified threshold associated with each of said one or more deep learning algorithms.
10. The method of claim 1 , wherein said one or more training datasets is obtained by mining one or more previously sorted documents.
11. A method of using classified text and deep learning algorithms to identify financial advantage comprising:
creating one or more training datasets by mining financial databases for textual data corresponding to one or more financial advantages of interest;
training one or more deep learning algorithms using said one or more training datasets;
extracting and indexing one or more internal electronic documents;
applying said one or more deep learning algorithms to said one or more internal electronic documents to identify and report any one of said one or more advantages of interest;
determining if said identified one of said one or more advantages of interest is a false positive or a true positive;
re-training said one or more deep learning algorithms if said identified one of said one or more advantages of interest is a false positive; and
saving said internal electronic documents in a true positive database for evaluation if said identified one of said one or more advantages of interest is a true positive.
12. The method of claim 11 , wherein each one of said one or more deep learning algorithms scores the data for accuracy with the deep learning model classification of the data.
13. The method of claim 12 , wherein said report said one of said one or more credits of interest comprises providing the scores and related data to one or more designated users.
14. The method of claim 13 , wherein the report may be limited to scores which surpass a specified threshold associated with each of said one or more deep learning algorithms.
15. A method of using classified text and deep learning algorithms to identify financial advantage comprising:
creating one or more training datasets for textual data corresponding to one or more financial classifications, wherein said financial classifications comprises one or more financial advantages of interest;
training one or more deep learning algorithms using said one or more training datasets;
extracting and indexing one or more internal electronic documents of an enterprise;
applying said one or more deep learning algorithms to said one or more internal electronic documents to identify any one of said one or more financial advantage of interest; and
reporting said identified one of said one or more financial advantages of interest for action by the enterprise.
16. The method of claim 15 , further comprising retraining said one or more deep learning algorithms if said identified one of said one or more financial advantages of interest is a false positive.
17. The method of claim 16 , wherein true or false positives are confirmed or rejected by a user through a graphical user interface.
18. The method of claim 15 , wherein said one or more training datasets is obtained by mining one or more previously sorted documents.