Abstract: A method and a system for automating a due diligence process are provided. The method includes: receiving identification information that relates to a customer; selecting, from a global set of due diligence questions based on the received identification information, a group of due diligence questions to be applied to the customer; determining a document source via which documents containing relevant information about the customer are accessible; retrieving, from the document source, a set of documents that relate to the potential customer; extracting, from the retrieved documents, information that is relevant to the selected group of due diligence questions with respect to the customer; and outputting the extracted information. Each of the determination of the document source, the retrieval of the set of documents, and the extraction of the relevant information is performed automatically by executing artificial intelligence algorithms that implement machine learning techniques.

Abstract: A method and a system for automatically processing financial documents to generate knowledge graphs that convey information relating to entities of interest and relationships between those entities are provided. The method includes: receiving a document; extracting raw text included in the document; identifying, based on the extracted raw text, a set of entities that are named in the document; determining respective relationship information that corresponds to respective pairs of entities; constructing a knowledge graph that illustrates respective relationships among the respective pairs of entities; and outputting the knowledge graph. The determination of the respective relationship information may be performed by applying an artificial intelligence (AI) algorithm that is trained by using historical data that relates to the set of entities.

Abstract: Systems and methods for multi-agent based fraud detection are disclosed. A method may include: providing a generator configuration file comprising a plurality of transaction behaviors and a number or proportion of generator agents to act in accordance with each transaction behavior; providing a detector configuration file comprising a detector parameter for a plurality of detector agents to use; generating a first set of test data using the generator agents based on the transaction behavior, wherein the first set of generated test data may include a first set of generated test transactions, and each generated test transactions may include a fraud indicator based on the transaction behavior; training a plurality of detector agents using the first set of generated test data and the detector configuration file, wherein each detector agent outputs a first trained model object; and outputting a first trained detection model based on the first trained model objects.

Abstract: Systems and methods for applying game theory for fraud detection. Rather than inspecting every transaction record, embodiments are directed to limiting incoming suspicious transaction records according to a schedule. The schedule may define time windows for various clients and transactions. These time windows may filter down the stream of incoming transaction records to a subset. As a result, fraud may be detected by strategically allocating resources in an optimal way rather than attempting to inspect each and every instance of transaction.