https://repositori.mypolycc.edu.my/jspui/handle/123456789/6669 2026-04-17T07:37:26Z 2026-04-17T07:37:26Z Mrisha, Samuel Hudson Sun, Xixiang https://repositori.mypolycc.edu.my/jspui/handle/123456789/7165 2025-10-29T03:00:12Z 2024-01-01T00:00:00Z

Title: SWAHILI CONSUMERISM. A RACE AGAINST TIME TO DIGITAL TRANSFORMATION Authors: Mrisha, Samuel Hudson; Sun, Xixiang Abstract: In a departure from biased conventional western narratives this study “benches the benchmarks" of western consumer behavior orientation, providing a novel outlook on emerging markets with a particular focus on the Swahili population. By employing a distinctive observation approach to scrutinize the interactions between telecommunication companies and their customers on social media platforms, this study bolstered credibility and alleviated the Hawthorne effect. In an innovative twist, the study employed the game theory to scrutinize the social media interaction scenarios, uncovering misunderstandings in how telecom companies perceive their interactions with customers in the realm of social media complaints management. Findings of the study exposes an unsuspected actor in the business ecosystem emerging as the likely culprit in hindering digitalization efforts. This study uniquely challenges the prevailing metanarratives of consumer behavior by uniquely categorizing the Swahili population as a medium context culture capable of embracing and reshaping consumerism in the digital era.

2024-01-01T00:00:00Z Wiljanen, Daniel Mothersell, William Jaideep Motwani https://repositori.mypolycc.edu.my/jspui/handle/123456789/7157 2025-10-28T03:01:01Z 2023-01-01T00:00:00Z

Title: PERFORMANCE IMPROVEMENT INTERVENTIONS: AN ACTION RESEARCH CASE STUDY Authors: Wiljanen, Daniel; Mothersell, William; Jaideep Motwani Abstract: The purpose of this action research study was to design, implement, and evaluate the impact of interventions created to solve a specific organizational problem pertaining to the Service Technicians. This problem arose as Service Technicians failed to accurately account for their time on their timecards. On further investigation, it was found that this problem occurred in 30% of the timecards. Also, the failure of Service Technicians to accurately complete their timecards appeared to be randomly distributed with no visible pattern by technician or type of job. The desired performance was that the Service Technicians fill out their timecards correctly 100% of the time, and that the customers were billed for clean-up charges, rather than billing their clean up time back to the company. This would ensure that their time on each customer service work order was accurately accounted for. By utilizing a systematic and simple approach to problem solving, the authors of the study were able to achieve significant outcomes for the case study company. The results of the interventions were positive, as the error rate was reduced from 30% to near 0. After accounting for $15,000 in expenditures for the project, the cost savings for the first year were $60,000. The return on investment was 400%.

2023-01-01T00:00:00Z Tejas Belgamwar Ninad Sayare https://repositori.mypolycc.edu.my/jspui/handle/123456789/7156 2025-10-28T03:01:06Z 2021-01-01T00:00:00Z

Title: INVENTORY MANAGEMENT USING DEMAND SALES FORECASTING Authors: Tejas Belgamwar; Ninad Sayare Abstract: The purpose of the exercise was to find fitting solution to the problems faced in supply chain – How to decide the Inventory? How to maintain lean operations without hampering service levels? In our experience in the industry, we have found that in the lack of a better solution, companies have adopted simplistic methods, incorrectly assessing their need and in turn taking a strategic backseat in the competitive space. Ironically the answer to this archaic problem has always been in their hands – Data. Our module leverages sophisticated statistical models like Holts winter, AR, ARMA, ARIMA & SARIMA available in Python Libraries. The module is made so that an average excel user will be able to leverage available data and decide optimum inventory.

2021-01-01T00:00:00Z Saurabh Kakkar https://repositori.mypolycc.edu.my/jspui/handle/123456789/7151 2025-10-28T03:00:43Z 2025-01-01T00:00:00Z

Title: ADVANCING CREDIT RISK MODELING THROUGH GENERATIVE ARTIFICIAL INTELLIGENCE: METHODS, APPLICATIONS, AND CHALLENGES Authors: Saurabh Kakkar Abstract: Credit risk modeling is an important component of the financial decision-making process because it determines whether or not credit is given and whether or not the credit given is appropriately used. Classical approaches to logistic regression, scorecards, and sophisticated machine learning techniques have proven helpful in providing institutions with practical prediction capabilities. The same issues, however, confuse these models: unstructured financial data cannot be modelled, rare default events are challenging to model, and there is always a trade-off between model accuracy and model explainability. Recent breakthroughs in generative artificial intelligence (AI) provide a new potential pathway to overcome such inadequacies. The paper explores the potential use of generative models such as generative adversarial networks (GANs), variational autoencoders (VAEs), transformer-based language models, and some of the newer diffusion methods to create more advanced credit risk models. Generative AI practices are assessed on their capability to produce realistic borrower data, model rare credit occurrences, credit scoring with multi-modal data, and dynamic stress testing conditions. It also discusses how generative models can support and augment the existing methodologies in portfolio-level fraud detection, anomaly detection, and risk assessment. Given these opportunities, its adoption in practice presents a challenging problem. Compliance with Basel III/IV and data protection regulations, algorithmic bias and unfair lending outcomes, extensive computational needs, and interpretability of black-box models have become of concern. The barriers to these must be overcome by designing explainable generative AI, Just and Equal education systems, and a governing construct that addresses institutional and regulatory requirements. A synthesis of current approaches, applications, and issues is presented in this paper to consider the role of generative AI as not being merely a technical innovation but also a future opportunity in credit risk modeling, as it mandates a fine line between innovation and trust and compliance in the global financial system.

2025-01-01T00:00:00Z