Anomaly Detection in Financial and Insurance Data-Systems - Journal of AI-Assisted Scientific Discovery
Keywords:
Data Quality, Financial Data ManagementAbstract
The quality of data is fundamental to success in the financial sector, as decisions are influenced by extensive volumes of real-time data. Ensuring the precision, uniformity, and dependability of this data is crucial for upholding regulatory compliance, alleviating financial concerns, and facilitating informed strategic decisions. Financial data necessitates rigorous oversight of essential criteria including absoluteness, timeliness, accuracy, continuity, and integrity, which serve as standards for evaluating the data's trustworthiness and usability. These concepts are the cornerstone of proficient data quality management within the financial sector.
By mapping incoming data to predetermined templates that capture the complete range of expected data points, this is commonly accomplished in financial systems through real-time result template mapping. The financial decision-making process is mitigated when missing numbers are detected early on using this template-based technique.
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