Published 12-09-2024
Keywords
- Data Quality,
- Financial Data Management,
- Anomaly Detection,
- Missing Data Handling,
- Machine Learning
- Data Models,
- Model Scalability,
- Data Validation,
- Artificial Intelligence,
- Financial Systems ...More
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
In order to maintain data integrity, operational effectiveness, and regulatory compliance, anomaly detection is a crucial duty in the financial and insurance sectors. This study offers a thorough framework for anomaly identification that uses cutting-edge techniques and scalable system designs to address anomalies and improve data quality. The study stresses a methodical approach, starting with a careful examination of current data models to pinpoint gaps and weak points. Stakeholder engagements and feedback assimilation are combined to improve the procedure.
The use of sophisticated outlier detection methods, including scatterplots and Mahalanobis distance, in conjunction with real-time template mapping to compare data to ideal benchmarks is one of the major developments. Regression imputation, KNN algorithms, and decision trees are used to handle missing data, and the results show a significant 57% improvement in data quality. Horizontal scaling, elastic schema integration, and normalisation techniques highlight scalable model architecture, which is in line with changing business requirements.
Key performance indicators (KPIs), quality assurance frameworks, and service-level agreements (SLAs) are used to assess the efficacy of the suggested approaches. These actions show better decision-making accuracy, less operational hazards, and increased system performance. In addition to advancing theory, this study provides practitioners with practical advice on how to improve anomaly detection and data quality standards in the insurance and finance industries.
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