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Review Papers | Computer Science | United States of America | Volume 13 Issue 10, October 2024 | Popularity: 5.5 / 10

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Improve Real - Time Fraud Detection with DataOps on Resilient Elastic Platforms

Aparna Krishna Bhat

Abstract: Fraud detection refers to measures put in place to prevent criminals from obtaining monetary benefits through false claims. In the world of online commerce, scams, scams and malicious agents are harmful in many ways. Businesses should take steps to ensure that fraud is detected and stopped before it affects the business. Fraud prevention refers to the countermeasures in place to mitigate the impact that fraudsters can have on business operations, once detected. Fraud detection is the first step in identifying where the risk lies. Real - time fraud detection improves on - site management of fraudulent activities and channels that could otherwise lead to negative business outcomes. As fintech and e - commerce thrive, more and more bank payments and money transfers are facilitated through online channels, which are faster, more convenient and safer for health in the age of the coronavirus. Real - time fraud detection and prevention can be accomplished using fraud detection software, RiskOps tools, DataOps, and other risk management strategies that improve data usability. Fraud detection on elastic platforms like elastic search has the ability to detect information in real time through predefined standards and approaches that provide alerts when communication is imminent. However, the fraud detection approach works with a significant commitment to ensure consistent compliance with privacy rules, such as anonymization, which ensures that personally identifiable information is not used for malicious purposes. Therefore, the act of fraud detection requires instrumental data processing mechanisms and relies on the scalability and flexibility of elastic platforms to achieve a scalable operation.

Keywords: AI, DataOps, RiskOps, Elasticsearch, Elastic Platforms, Fraud Detection, Compliance, Data management, Data pipeline, Data processing, Real time data processing, Data enrichment

Edition: Volume 13 Issue 10, October 2024

Pages: 193 - 198

DOI: https://www.doi.org/10.21275/SR241001074626

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