Automated Data Integrity Checks for Financial Software Systems
Year: 2024
Type: article
Abstract: The integrity and security of the data that is outsourced are often threatened by an untrusted cloud server, despite the fact that cloud storage offers simple data outsourcing options. Designing security techniques that enable users to verify data integrity with reasonable computing and communication overheads is thus of utmost importance. The goal of this study is to create AI Data Quality Co-pilots, which are advanced systems designed to automatically assess and improve data quality in real time. According to AI Data Quality Co-pilots, future concerns like data drift, privacy, and inclusion won't affect the AI model's dependability or impartiality. Additionally, it discusses how co-pilot applications increase real-time base-level decision-making content and decrease erroneous fraud signals, as well as how ethical AI may be achieved by detecting and correcting biases. Businesses may expand and enhance AI with appropriate data management and maintain effective AI models with consistent high-quality data inputs by linking these co-pilots.
Author: Chinmay Mukeshbhai Gangani
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4407447376