Mr. Rajesh Kumar

ML Power Swift message validation for Sanction Screening

Abstract:

When it comes to banking operations, traditional architecture and obsolete software systems are by no means extraneous. However, the introduction of such systems does carry with itself the burden of technical debt, since it inhibits the ease of change, invites operational risk, and makes the raising of the cost of maintaining such a system inevitable. Making use of technical debt automation is a reasonable approach that is remarkably helpful in identifying, measuring, and prioritizing code and architecture deficiencies in such legacy systems in a structured way. In this article, we explore the methods and techniques aimed at the detection of technical debt in bankrelated systems with a focus on software analysis, particularly static analysis, and architectural metrics, and consider regimes of their estimation incorporating machine learning algorithms. In addition, the study addresses certain other characteristics that are application-dependent including issues of compliance, concerns about the availability of services, and compliance with modern digital technologies. The findings turn the attention toward the advantages of introducing automated evaluation tools to simplify the appreciation process, reduce the exposure of the bank to risk and enhance the prospects of updating bank software environments. Monolithic architecture and legacy banking applications, typically based on old technologies, remain significant to financial institutions. However, the operation and improvement of such systems increase the technical debt that damages changes, imposes operational risk, and raises maintenance costs. To combat this, there is an analysis of which is quite systematic – automated technical debt analysis, which is designed to find, measure and prioritize coding and architectural problems in such systems. The article studies and facilities of the automated identification of technical debt in banking software development applies, focusing mainly on how these can be achieved through software static analysis, architectural metrics, and prediction models that rely on artificial intelligence. Furthermore, some defects investigate technology dependence compliance, antifraud, and satisfaction from digital area. The findings further emphasize the significance of automated assessment systems to speed up the making of decisions process, reduce risk, and smoothen the process of transformation.

Profile:

Rajesh Kumar is an accomplished Vice President and Engineering Team Lead with 17 years of experience in software development, system architecture, and payments technology. He specializes in ACH payments, microservices architecture, Core Java, and TIBCO to microservices migration, with expertise in cloud system design, hybrid cloud solutions, and AI-powered development tools. Rajesh has led large-scale application modernization, migration, and transformation projects, delivering scalable, production-ready solutions across financial services. Skilled in frontend and backend technologies, relational databases, CI/CD pipelines, and Agile/Scrum methodologies, he combines deep technical knowledge with leadership capabilities to mentor teams, drive operational efficiency, and ensure successful delivery of complex enterprise solutions.