Building a dataset having requisite number of fraud samples to train a multi-class machine learning model for fraud detection

A non-transitory machine-readable medium storing one or more sequences of instructions for aiding fraud detection, wherein execution of said one or more instructions by one or more processors contained in a digital processing system causes said digital processing system to perform the actions of:

• receiving a first historical data at a first time instance;
• identifying a first set of transactions tagged as fraud in said first historical data;
• if a count of said first set of transactions is below a threshold:
• forming a training data and a test data from said historical data, wherein said test data includes said first set of transactions tagged as fraud;
• generating, based on said training data, a first one-class machine learning (ML) model that is able to predict said first set of transactions when said test data is provided as input to said first one-class ML model;
• applying said first one-class ML model to a set of transactions of an inference data to identify whether each transaction is an anomaly or not;
• receiving an input data indicating whether each transaction identified as said anomaly is a fraud transaction or not;
• and
• updating said first historical data by adding said set of transactions and
• tagging each fraud transaction as being fraud to form an updated historical data, wherein said updated historical data is used for training a multi-class ML model after said count of said first set of transactions is greater than or equal to said threshold.

A digital processing system comprising:

• a random access memory (RAM) to store instructions for aiding fraud detection;
• and
• one or more processors to retrieve and execute the instructions, wherein execution of the instructions causes the digital processing system to perform the actions of:
• receiving a first historical data at a first time instance;
• identifying a first set of transactions tagged as fraud in said first historical data;
• if a count of said first set of transactions is below a threshold:
• forming a training data and a test data from said historical data, wherein said test data includes said first set of transactions tagged as fraud;
• generating, based on said training data, a first one-class machine learning (ML) model that is able to predict said first set of transactions when said test data is provided as input to said first one-class ML model;
• applying said first one-class ML model to a set of transactions of an inference data to identify whether each transaction is an anomaly or not;
• receiving an input data indicating whether each transaction identified as said anomaly is a fraud transaction or not;
• and
• updating said first historical data by adding said set of transactions and tagging each fraud transaction as being fraud to form an updated historical data,
• wherein said updated historical data is used for training a multi-class ML model after said count of said first set of transactions is greater than or equal to said threshold.