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Role of project management methodologies in achieving project success and business value creation in financial services IT projects

Increasingly, project-based organizations, have been implementing project management methodology for specific projects based on the dynamic environment. Financial services face challenges in the form of unstable market conditions, economies and technology, strict controls, and higher stress to meet customer demands and maximize profits. Many IT initiatives fail to provide the desired outcomes as per the project budgets, project types, and project contexts. Some are also unable to deliver business value to the customers due to the adoption of an unsuitable project management methodology. Regardless of the wide variety of choices, project managers often fail to decide on available alternatives. Project managers tend to tailor projects based on criteria that may not be associated with the overall project objectives rationally. The goal of this newlineresearch work is to assess the role of hybrid and agile project management approaches in newlineachieving success and creating business value considering project contexts for financial newlineservices IT projects. A conceptual research framework and various propositions were built on the literature analysis. To analyse the theoretical support, data was gathered through an online questionnaire. The survey was administered to project managers who were newlineinvolved at different levels in IT project development in financial service organizations. The study findings demonstrate that the various key success factors vary significantly across hybrid and agile project management methodologies considering different success measures in IT projects. And, such factors and processes play a vital role, especially in large-size high-technology projects. This research work enriches the project management literature and offers practical guidance by applying the contingency approach for comparing the effect of key success factors applicable to financial service IT projects from the viewpoint of hybrid and agile project management approaches.

PhD

Role of project management methodologies in achieving project success and business value creation in financial services IT provides

Increasingly, project-based organizations, have been implementing
project management methodology for specific projects based on the dynamic environment. Financial services face challenges in the form of unstable market conditions, economies and technology, strict controls, and higher stress to meet customer demands and maximize profits. They are compelled to provide services that are tailored to changing customer interests as a result of technological innovations and developments. Financial services especially information technology-based businesses find it difficult to sustain considering the dynamic behavior of the market in terms of efficiency and controlling the cost based on market changes. Moreover, financial services are also constrained to manage stakeholders while maintaining flexibility and responsive processes and focusing on technological advancement to improve success in projects. Many IT initiatives fail to provide the desired outcomes as per the project budgets, project types, and project contexts. Some are also unable to deliver business value to the customers due to the adoption of an unsuitable project management methodology. Regardless of the wide variety of choices, project managers often fail to decide on available alternatives. Project managers tend to tailor projects based on criteria that may not be associated with the overall project objectives rationally. The goal of this research work is to assess the role of hybrid and agile project management approaches in achieving success and creating business value considering project contexts for financial services IT projects.

PhD

Development of Privacy Preserving Machine Learning Techniques Using Secure Multi-Party Computation

Machine learning (ML) has brought about a paradigm shift in insight generation across various domains, including healthcare, finance, and pharma, by leveraging historical data. However, the effectiveness of ML solutions hinges on the seamless collaboration between data owners, model owners, and ML clients while ensuring that privacy concerns are meticulously addressed. Unfortunately, existing privacy-preserving solutions have not been able to offer efficient and confidential ML training and inference. This has led to an increased focus on Privacy-Preserving Machine Learning (PPML), which has become a flourishing area of research aimed at safeguarding the privacy of machine learning stakeholders. In this regard, the present research introduces novel techniques for private ML inference and training of models using Secure Multi-Party Computation (SMPC) and Differential Privacy (DP) methods on horizontally and vertically partitioned datasets. The proposed techniques are implemented using Python with open-source libraries such as SyMPC and PyDP to ensure confidential inference and model protection. The findings across various parameters illustrate the effectiveness of the suggested techniques in addressing the privacy concerns of model owners and inference clients, with no significant impact on accuracy and a linear influence on performance as the privacy parameters, such as secure nodes count within the SMPC cluster. are increased. Furthermore, the privacy gain is substantiated by information privacy measures such as Mutual Information and KL-Divergence across different privacy budgets, which demonstrate empirically that privacy can be preserved with high ML accuracy and minimal performance cost.

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