John Fields

Systems and methods disclosed herein provide automatic expense categorization of transactions or expenditures based on a machine learning (ML) model trained using anonymized transactional data for expenditures that are stored in a public blockchain. The anonymized transactional data for the expenditure and the expense category, may be distributed throughout the blockchain network and recorded in the blockchain. In some implementations, an expenditure may be submitted to the blockchain network… Show more

Systems and methods disclosed herein provide automatic expense categorization of transactions or expenditures based on a machine learning (ML) model trained using anonymized transactional data for expenditures that are stored in a public blockchain. The anonymized transactional data for the expenditure and the expense category, may be distributed throughout the blockchain network and recorded in the blockchain. In some implementations, an expenditure may be submitted to the blockchain network for expense categorization. The transactional data for the expenditure may be anonymized to remove confidential and personal identifying information (PII) before it is distributed throughout the blockchain network. Each participating node of the blockchain network may utilize the ML model to identify an expense category for the expenditure. The participating nodes may provide a consensus mechanism in order to arrive at a shared understanding of how to categorize the expenditure. Show less

The exemplary embodiments provide an automated control compliance evidence manager that is responsible for gathering evidence of compliance with controls. The control compliance evidence manager may operate on an ongoing basis. In some exemplary embodiments, the evidence is gathered and available for review in real time or near real time. The steps for gathering the compliance evidence may be specified at the time a control is established so that there is consistency in what is gathered and how… Show more

The exemplary embodiments provide an automated control compliance evidence manager that is responsible for gathering evidence of compliance with controls. The control compliance evidence manager may operate on an ongoing basis. In some exemplary embodiments, the evidence is gathered and available for review in real time or near real time. The steps for gathering the compliance evidence may be specified at the time a control is established so that there is consistency in what is gathered and how the evidence is gathered. A user may be required to provide an itemization of what evidence is to be gathered and where. The evidence may be stored in an immutable fashion. In some exemplary embodiments, the evidence may be cryptographically hashed or otherwise encrypted and referenced by a secure distributed ledger, like a blockchain. The distributed ledger may be visible to concerned parties. Show less

A system and method for using device characteristics to authenticate a transaction is disclosed. A first device can be used to request a transaction, and a second device can be used to approve the transaction. A push notification or authentication code is transmitted to a second device, and approval of the transaction is completed based in part on the hardware characteristics of the second device. A security database stores the hardware characteristics of the second device and links the… Show more

A system and method for using device characteristics to authenticate a transaction is disclosed. A first device can be used to request a transaction, and a second device can be used to approve the transaction. A push notification or authentication code is transmitted to a second device, and approval of the transaction is completed based in part on the hardware characteristics of the second device. A security database stores the hardware characteristics of the second device and links the characteristics to authentication profiles such that the second device can be used as a physical authentication device in multi-factor authentication. Show less