MLOps in Finance: Automating Compliance & Fraud Detection |
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| © 2025 by IJCTT Journal | ||
| Volume-73 Issue-4 |
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| Year of Publication : 2025 | ||
| Authors : Balajee Asish Brahmandam | ||
| DOI : 10.14445/22312803/IJCTT-V73I4P105 | ||
How to Cite?
Balajee Asish Brahmandam, "MLOps in Finance: Automating Compliance & Fraud Detection," International Journal of Computer Trends and Technology, vol. 73, no. 4, pp. 35-41, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I4P105
Abstract
In recent years, the finance sector has experienced a paradigm shift with the advent of Machine Learning (ML) models that automate previously manual processes, improve operational efficiency, and enhance decision-making. In response to increasingly complicated rules and an uptick in cybersecurity threats, fragmented financial institutions need strong compliance tracking and fraud detection systems. Machine Learning Operations (MLOps) can help financial institutions automate and govern their machine learning model lifecycle. MLOps is what you get when you combine Machine Learning with DevOps. This is step one in deploying Machine-Learning models using CI/CD. MLOps can help financial institutions streamline their procedures to detect fraud and comply with regulations. They can also accelerate the development and execution of models. Using MLOps for compliance, financial institutions like banks can streamline the examination of large amounts of transaction data to ensure regulatory compliance. Traditional compliance operations involve a manual review of transactions, a labor intensive process prone to errors. Models that detect breaches classify suspicious activities and emit real-time alerts make it much easier for MLOps. This enables them to address the shortcomings of the current approaches. With sophisticated criminals, humans simply cannot detect them. Machine Learning offers a flexible solution to this problem. Automating the retraining model with the emerging fraud trends through MLOps makes detection very effective. Thus, keeping models explainable and transparent is a key advantage of MLOPs within the financial industry. This is exceptionally essential for operational and regulatory reasons. Organizations in the financial space could leverage strong monitoring tools and performance indicators to obtain guarantees that their models are functioning as intended and are auditable. MLOps solutions help govern and stabilize machine learning processes like version control, automated model testing, and model repeatability, among many more. Data security, model bias, and MLOps scalability are key challenges the banking sector is trying to tackle. If we know the proper methodologies and apply the best practices, we can ensure that the advantages of MLOps outweigh the risks. MLOps: An Urgent Need for the Banking Sector to Automate Compliance and Fraud Detection. Banks and other financial institutions can significantly improve their risk management, fraud detection, and regulatory compliance capabilities by standardizing their integration, deployment, and monitoring of machine learning models. This will make the financial system more efficient and secure.
Keywords
Financial Compliance, Fraud Detection, Machine Learning, and Mechanization in Investment MLOps.
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