Artificially Intelligent System and Method for Automatic Generation and Transmission of Digital Receipts

A system for transaction data security, the system comprising:

• at least one convolutional neural network configured to receive at least a first set of training data and a second set of training data;
• and
• at least one processor communicatively coupled to at least one memory, the at least one memory storing one or more instructions for executing the method by the at least one processor, the method comprising:
• receiving raw transaction data from a transaction terminal;
• extracting line-item text using optical character recognition and natural language processing to extract and structure the authenticated data into normalized data;
• executing anomaly detection and fraud analysis against the normalized data by an artificial intelligence detection engine, the artificial intelligence detection engine comprising:
• an unsupervised learning component comprising one or more machine learning models supported by the at least one convolutional neural network and trained on user transaction history, the unsupervised learning component employing a clustering algorithm to group a plurality of transactions with similar characteristics, and further employing isolation forests which isolate anomalous transactions by distinguishing the anomalous transactions from normal data distributions;
• and
• a supervised learning component comprising one or more machine learning models trained on historical fraud data and further trained on regulatory compliance data to identify fraud patterns, the supervised learning component employing at least one random forest algorithm to the normalized data;
• the executing of the anomaly detection and fraud analysis further comprising:
• generating and dynamically updating an adaptive threshold for high risk of fraud, the adaptive threshold being determined by the supervised learning component;
• calculating a risk score for any of the plurality of transactions by the unsupervised learning component;
• blocking any of the plurality of transactions upon determination that the risk score exceeds the adaptive threshold;
• and
• enabling a multi-factor authentication requirement upon determination that the risk score exceeds the adaptive threshold.

A non-transitory computer-readable storage medium having embodied thereon instructions which, when executed by a processor, perform the steps of a method, the method comprising:

• receiving raw transaction data from a transaction terminal;
• extracting line-item text using optical character recognition and natural language processing to extract and structure the authenticated data into normalized data;
• executing anomaly detection and fraud analysis against the normalized data by an artificial intelligence detection engine, the artificial intelligence detection engine comprising:
• an unsupervised learning component comprising one or more machine learning models supported by at least one neural network and trained on user transaction history, the unsupervised learning component employing a clustering algorithm to group a plurality of transactions with similar characteristics, and further employing isolation forests which isolate anomalous transactions by distinguishing the anomalous transactions from normal data distributions;
• and
• a supervised learning component comprising one or more machine learning models trained on historical fraud data and further trained on regulatory compliance data to identify fraud patterns, the supervised learning component employing at least one random forest algorithm to the normalized data;
• the executing of the anomaly detection and fraud analysis further comprising:
• generating and dynamically updating an adaptive threshold for high risk of fraud, the adaptive threshold being determined by the supervised learning component;
• calculating a risk score for any of the plurality of transactions by the unsupervised learning component;
• blocking any of the plurality of transactions upon determination that the risk score exceeds the adaptive threshold;
• and
• enabling a multi-factor authentication requirement upon determination that the risk score exceeds the adaptive threshold.