AUTOMATION OF FRAUD DETECTION WITH MACHINE LEARNING UTILIZING PUBLICLY AVAILABLE FORMS

    公开(公告)号:US20250061469A1

    公开(公告)日:2025-02-20

    申请号:US18234911

    申请日:2023-08-17

    Abstract: Systems and methods for alerting an organization about activity that may be fraudulent. Systems may include a computer processor, a storage module, a cleaning module, a preprocessing module, a features extraction module, and a machine learning module. The computer processor may be configured to run a fraud detection engine by collecting publicly available electronic forms every 36 hours, using the modules to store the forms, clean the data, preprocess the data, and run a machine learning model to extract features and to determine if a threshold indicating a risk of fraud has been exceeded. The machine learning models include a liquid, solvency, and profitability ratio classification model, a disclosure classification model, a sentiment analysis model, an anomaly detection classification model, an ownership analysis classification model, and an ESG disclosure classification model. When exceeding a threshold, the computer processor may notify an administrator of the exceeded threshold's identity.

    System and method for transpilation of machine interpretable languages

    公开(公告)号:US12147422B2

    公开(公告)日:2024-11-19

    申请号:US17557208

    申请日:2021-12-21

    Abstract: Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may train a machine learning model using source syntax trees and target dialect syntax trees, which may configure the model to output source dialect keys and their corresponding target dialect queries. The computing platform may execute the corresponding target dialect queries to identify whether they are valid. For a valid target dialect query, the computing platform may store the valid target dialect query and first source dialect keys corresponding to the valid target dialect query in a lookup table. For an invalid target dialect query resulting in error, the computing platform may: 1) identify a cause of the error; 2) generate a transliteration rule to correct the error; and 3) store, in the lookup table, the invalid target dialect query, second source dialect keys corresponding to the invalid target dialect query, and the transliteration rule.

    USER-SIDE NON-FUNGIBLE TOKEN STORAGE USING SUPER NON-VOLATILE RANDOM ACCESS MEMORY

    公开(公告)号:US20240029056A1

    公开(公告)日:2024-01-25

    申请号:US17868235

    申请日:2022-07-19

    Inventor: Elvis Nyamwange

    Abstract: A system for leveraging local/user-side resources (i.e., memory) to store Non-Fungible Tokens (NFTs) and conduct NFT-related computational processes required for generating/minting or exchanging an NFT. The local/user device is equipped with super Non-Volatile Random Access Memory (NVRAM), which operates in accordance with a resource-sharing protocol, such as Network Block Device (NBD) protocol or the like. The resource-sharing protocol is registered with the user's NFT digital wallet, which is in communication with the distributed trust computing networks and, thus links the local/user-side resources (i.e., NVRAM) with the distributed trust computing network for resource sharing capabilities.

    System and Method for Efficient Transliteration of Machine Interpretable Languages

    公开(公告)号:US20230129782A1

    公开(公告)日:2023-04-27

    申请号:US17557456

    申请日:2021-12-21

    Abstract: Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may configure a client application to use a custom driver when communicating with an enterprise database. The computing platform may receive a database query formatted in a first database format corresponding to a first database. The computing platform may translate, using a query translation library, the database query from the first database format into a second database format corresponding to a second database, which may cause the custom driver to execute a transliteration process using pre-verified query keys stored in the query translation library to convert the database query from the first database format into the second database format. The computing platform may execute the translated database query on the second database to obtain a query result, and may send the query result to the client application.

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