Automation of fraud detection with machine learning utilizing publicly available forms

A method of alerting an organization about activity that may be fraudulent, the method comprising:

• collecting every 45 days or less from an electronic portal of a Security and Exchange Commission (SEC), using a computer processor, one or more forms submitted to the SEC relating to a first organization;
• wherein:
• the one or more forms submitted to the SEC comprise SEC Form 10-K, SEC Form 8-K, SEC Form 10-Q, SEC Form 4, and SEC Form SD;
• cleaning and preprocessing, using the computer processor, data found in the one or more forms to produce cleaned and preprocessed data;
• extracting, using the computer processor to run one or more machine learning models, one or more sets of features from the cleaned and preprocessed data;
• wherein the one or more sets of features comprise:
• a set of features related to liquid, solvency, and profitability ratio classification, a set of features related to disclosure classification, a set of features related to sentiment analysis, a set of features related to anomaly detection classification, a set of features related to ownership analysis classification, and a set of features related to ESG disclosure classification;
• determining, using the computer processor to run one or more machine learning models, if one or more thresholds have been exceeded indicating a risk of fraud;
• wherein the one or more machine learning models comprise:
• a liquid, solvency, and profitability ratio classification machine learning model, a disclosure classification machine learning model, a sentiment analysis machine learning model, an anomaly detection classification machine learning model, an ownership analysis classification machine learning model, and an ESG disclosure classification machine learning model;
• notifying an administrator in a second organization, using the computer processor, when one or more thresholds have been exceeded;
• and
• informing the administrator, using the computer processor, with an identity of the one or more thresholds which have been exceeded.

A system for alerting an organization about activity that may be fraudulent, the system comprising:

• a computer processor;
• a fraud detection engine comprising:
• a storage module;
• a cleaning module;
• a preprocessing module;
• a features extraction module;
• a machine learning module;
• wherein the computer processor is configured to run the fraud detection engine by performing steps comprising:
• collect every 36 hours or less one or more forms submitted to a Security and Exchange Commission (SEC) relating to a first organization from an electronic portal of the SEC;
• wherein:
• the one or more forms submitted to the SEC comprise SEC Form 10-K, SEC Form 8-K, SEC Form 10-Q, SEC Form 4, and SEC Form SD;
• store the one or more forms in the storage module;
• clean data with the cleaning module using the one or more forms found in the storage module;
• preprocess data with the preprocessing module using cleaned data from the cleaning module;
• extract one or more sets of features with the features extraction module using preprocessed data from the preprocessing module;
• wherein the one or more sets of features comprise:
• a set of features related to liquid, solvency, and profitability ratio classification, a set of features related to disclosure classification, a set of features related to sentiment analysis, a set of features related to anomaly detection classification, a set of features related to ownership analysis classification, and a set of features related to ESG disclosure classification;
• determine if a threshold indicating a risk of fraud is exceeded by using one or more machine learning models to analyze features from the features extraction module;
• wherein the one or more machine learning models comprise:
• a liquid, solvency, and profitability ratio classification machine learning model, a disclosure classification machine learning model, a sentiment analysis machine learning model, an anomaly detection classification machine learning model, an ownership analysis classification machine learning model, and an ESG disclosure classification machine learning model;
• notify an administrator in a second organization, using the computer processor, when one or more thresholds have been exceeded;
• and
• inform the administrator, using the computer processor, with an identity of the one or more thresholds which have been exceeded.