MSc in Information Systems

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/2481

Students enrolled in the MSc in Information Systems programme are required to submit a thesis as a compulsory component of their degree requirements. This collection contains merit-based theses submitted by postgraduate students specialising in Information Systems. Abstracts are available for public viewing, while the full texts can be accessed on-site within the library.

CC BY-NC-ND 4.0 License

Theses and Dissertations of the Sri Lanka Institute of Information Technology (SLIIT) are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .

Browse

Now showing 1 - 20 of 29

Open Access
A Flask-Based System for Measuring and Analyzing Confidence in Interviewee Speech Using Speech Recognition Technology
(Sri Lanka Institute of Information Technology, 2025-12) Dangalla, H.P.
Confidence is vital in the interview process, as it is a key determinant of credibility and competence. Nevertheless, conventional approaches to evaluating confidence are highly dependent on human judgment, which brings in bias and variability. This article suggests an effective machine learning platform which uses convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to overcome these shortcomings, offering an objective and scalable method to determine confidence levels in speech during interviews. In this architecture, CNNs extract the spatial characteristics of audio spectrograms, paying attention to the key prosodic variations in pitch and tone that act as confidence indicators. Meanwhile, LSTMs learn the time-varying behavior of these features, enabling the system to identify change in speech rate and time-varying pauses. These models can jointly identify speech as confident or non-confident with 92.5 percent accuracy on labeled data. This system is more precise, recalls higher, and has a better F1 score than current methods. Although the model demonstrates potential in confidence detection, it struggles with extrapolating across accents and languages due to overfitting. But it has a lot of potential in the future as a tool. To overcome future challenges, more diverse datasets and sophisticated methods such as data augmentation and transfer learning can be implemented to enhance the adaptability of the system. Such a framework might be of immense use in practical situations when conducting job interviews, educational evaluations, and coaching in speech delivery, giving consistent, objective measures of confidence. The resultant system might help enhance fairer judgments, offer constructive criticism to applicants, and contribute to making informed choices, benefiting the science of affective computing. It also paves the way to scalable, real-time solutions that could improve human-AI interaction and enhance communication dynamics in various areas.
Open Access
A Metric-Driven Framework for Evaluating Large Language Models in Software Testing: Insights from Industry Experts
(Sri Lanka Institute of Information Technology, 2025-12) Perera, V. I. T.
The integration of Large Language Models (LLMs) into software testing workflows has introduced new opportunities for automation, but also raised critical questions regarding the reliability, maintainability, and effectiveness of the generated test cases. This study addresses the lack of standardized evaluation practices by proposing and validating a comprehensive metric-driven framework: STEAM-LLM (Software Test Effectiveness Assessment Model for LLM-generated tests). Through a mixed-methods research design involving structured surveys and expert interviews, the framework identifies three core independent variables: Prompt Engineering Level, Human Intervention, and Input Specification Quality and evaluates their impact on FaultRevealing Power and Maintainability & Stability. The inclusion of Edit Effectiveness as a mediator, along with contextual moderators such as Task Complexity, Developer Experience, and LLM Class, reflects the nuanced dynamics of LLM-assisted testing. Confounding variables including Baseline Project/Test Quality and Time Spent on Understanding the Task were also accounted for to ensure valid assessments. The framework was empirically validated and supported by metric thresholds (e.g., ≥80% coverage, ≤10% smell density), offering practical benchmarks for industry adoption. The findings demonstrate that structured prompts, strategic human oversight, and high-quality inputs are essential for producing reliable and maintainable LLM-generated test cases. This research contributes a theoretically robust and practically applicable model for evaluating AI-assisted software testing, laying the foundation for future experimentation, tooling, and integration into continuous development environments.
Open Access
A Risk-Based Complexity Metric for Measuring Scope Creep’s Effect on Agile Test Automation and QA Efficiency
(Sri Lanka Institute of Information Technology, 2025-12) Karunasinghe,A. D. A. L.
Scope creep remains a persistent challenge in Agile software development, often leading to disrupted testing cycles, reduced automation efficiency, and compromised product quality. This research introduces a novel, risk-based complexity metric Scope Creep Impact Metric (SCIM) designed to quantify the impact of requirement volatility on Agile Quality Assurance (QA). The study also proposes a composite QA Efficiency Index to measure testing performance under changing scope conditions. A mixed-methods approach was adopted, combining statistical correlation analysis with expert validation and a retrospective case study. SCIM integrates eight key parameters, including frequency and complexity of scope changes, test coverage loss, defect trends, and execution delays. The QA Efficiency Index aggregates indicators such as defect count, detection rate, regression runtime, and test execution timing. The findings revealed strong correlations between SCIM and QA disruption metrics, particularly test coverage and runtime. Hypothesis testing confirmed that complexity and risk severity are more predictive of QA inefficiency than change frequency alone. Based on these insights, an Adaptive Scope-Creep Risk Framework for Quality Assurance (ASCR-QA) was developed and validated through simulation. The framework enables Agile teams to detect scope-related risks early, adapt testing strategies, and allocate resources effectively. This research contributes a structured, empirically grounded methodology for managing scope creep in Agile environments. It offers both theoretical advancement and practical tools for improving QA resilience, supporting more predictable and high-quality software delivery.
Open Access
AI Driven Job Recommendation System on Education and Personal Preferences
(Sri Lanka Institute of Information Technology, 2025-12) Fathima Shukra K S
Traditional career advising approaches have many disadvantages, including a narrow focus on individual assessments, standardized testing, ignoring unique circumstances of individuals, or assessment of labor market contexts or individuals. This paper describes the design and validation of an artificial intelligence (AI)-based career recommendation system that incorporates contextually relevant information including labor market trends and an individual’s educational history, abilities, work experience, and preferences to deliver the most relevant career advice. The system uses a hybrid recommendation approach that utilizes both content-based and collaborative filtering, examining the attributes of the individual as well as information about similar users that diagnostic contexts. The recommendation system uses current labor market information that is accessed via available APIs and web scraping, providing current information relevant for the recommendations. The research uses a quantitative and experimental research design with about 1,000 participants that included students, recent graduates, and early-career workers. The system is powered by machine-learning algorithms including a neural network, decision trees, and ensemble approaches and evaluates using various understanding methods for understanding performance including precision, recall, and F1-score. The system was developed as a web application using Flask, and enables users to easily enter data, see visual recommendations, and provide feedback to improve evaluation. The final contributions of this research are the ability to deliver better approach to career decision-making using form of personalized advice, provide career development planning that aligns to market conditions, provide improved generic educational planning insights, and validate a hybrid way of filtering career information for use in career guidance.
Open Access
AI-Driven Help Desk Integration: Enhancing Customer Support with Chatbots, Sentiment Analysis, and SLA Automation
(Sri Lanka Institute of Information Technology, 2025-11) Nimnadi Dilsika
This research investigates the integration of Artificial Intelligence (AI) into help desk systems to improve customer service efficiency, accuracy, and overall satisfaction. The proposed AI-driven help desk framework combines three intelligent components: a chatbot for instant and automated responses, a sentiment analysis engine to detect and interpret customer emotions, and a Service Level Agreement (SLA) management module that ensures real-time tracking of response and resolution performance. Using a dataset of 40,000 simulated support tickets, the system was evaluated for key metrics such as response time, SLA compliance, and customer satisfaction levels. The results demonstrated notable improvements, including faster response rates, higher SLA adherence, and enhanced emotional understanding in customer interactions. Overall, the study confirms that AI integration transforms traditional help desks into proactive, data-driven, and emotionally intelligent service environments. Future advancements will focus on predictive SLA modeling, multilingual capabilities, and multimodal sentiment analysis for broader adaptability.
Open Access
AI-DRIVEN SELF-HEALING TEST AUTOMATION FOR ENTERPRISE SOFTWARE SYSTEMS
(Sri Lanka Institute of Information Technology, 2025) Jinarathna, H. D. R. J.
Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) platforms are backbone tools for today’s businesses, helping teams around the company work together efficiently. But because these systems are huge and always changing, testing them gets tricky. Methods we’ve traditionally used—whether having testers run scripts by hand or running automated scripts—can’t keep up. To solve these problems, I’ve built a test automation framework that uses AI to repair itself as it runs. By incorporating Natural Language Processing (NLP) for spotting system changes and Reinforcement Learning (RL) for teaching tests to heal and get sharper, the framework learns to find when a test has broken, to fix the code on the fly, and to keep fine-tuning itself, so people hardly need to step in. I shaped the system by talking to QA engineers about the roadblocks and running pilot cases in a pretend ERP setup. Those conversations, plus the numbers, helped us tweak the design so it feels less like a lab gadget and more like a teammate. Early results are encouraging—flake tests bounce back 35% more often and testers spend 25% less time rewriting logic by hand. My research helps Software Quality Assurance Engineers, learners, and software businesses by offering an easy-to-understand, adaptable way to test big-company software. The results show how using smart technology can make software testing faster, cheaper, and better.
Open Access
Analyzing the Performance of Different Text Classification Algorithms for “Dhivehi” Documents
(SLIIT, 2024-12) Mohamed, F.R
This research investigates the effectiveness of various machine learning classification algorithms applied to Dhivehi text-based documents. Dhivehi, the official language of the Maldives, presents unique linguistic challenges for text classification due to its limited digital resources and distinct grammatical structure. The study aims to identify the most suitable algorithm for classifying Dhivehi documents and to provide insights into optimizing text classification approaches for less- resourced languages. The research systematically evaluates the performance of several machine learning algorithms, including Support Vector Machines (SVM), Naive Bayes, Decision Trees, XGboost , Random Forest and Neural Networks. These algorithms are applied to a diverse dataset of Dhivehi text, encompassing various genres and topics. The study employs a rigorous methodology involving data preprocessing, feature extraction, and model training and testing. Performance metrics such as accuracy, precision, recall, and F1-score are used to compare the efficacy of each algorithm. Additionally, the research explores the impact of different text representation techniques, including bag-of-words, TF-IDF, and word embeddings, on classification performance. The findings offer valuable insights into optimizing text classification methods for low-resource languages and aim to advance natural language processing tools specifically tailored for “Dhivehi.” The evaluation highlights that K-Neighbors achieved the highest performance, with an accuracy of 64.7% and F1 scores (macro: 0.640, weighted: 0.642), demonstrating a strong balance between precision and recall. Support Vector Machines (accuracy: 63.9%) and XGBoost (accuracy: 62.8%) also showed competitive results, with SVM slightly outperforming XGBoost in F1 metrics. Decision Tree exhibited the lowest performance across all metrics. By identifying the most effective classification algorithms and representation techniques, this research aims to enhance the accuracy and efficiency of Dhivehi text classification tasks. The results will have practical applications in areas such as sentiment analysis, document categorization, and information retrieval systems tailored for the Dhivehi language. Furthermore, the dataset is publicly available on Mendeley data under the name “Dhivehi Categories data set” to foster future research and innovation in this domain.
Open Access
Assessing the Feasibility and Effectiveness of Blockchain Technology for Safeguarding Governmental Data Integrity: Focus on Sensitive Diplomatic Communications
(SLIIT, 2024-12) Dilshanie, A.G.C.
This thesis investigates the feasibility and effectiveness of implementing blockchain technology to safeguard governmental data integrity, with a specific focus on sensitive diplomatic communications. Given the increasing importance of data security in an era of digital transformation, this research adopts a comprehensive mixed-method approach. It combines quantitative surveys and qualitative interviews with government IT professionals, blockchain experts, and policy makers to assess the technological, operational, and regulatory dimensions of blockchain implementation. The study reveals that blockchain technology offers substantial benefits in enhancing data security, primarily due to its inherent characteristics such as immutability, transparency, and decentralized architecture. These features are particularly crucial for maintaining the integrity and confidentiality of sensitive diplomatic communications, which are often vulnerable to unauthorized access and tampering. The research highlights that blockchain’s ability to create tamper-proof records and ensure transparent transactions aligns well with the needs of secure diplomatic exchanges. However, the study also identifies several significant challenges that need to be addressed. Issues such as scalability, high energy consumption, regulatory compliance, and the complexity of integrating blockchain with existing governmental IT systems are notable barriers to effective implementation. To address these challenges, the study proposes a detailed framework that includes technical solutions, policy adjustments, and strategic recommendations. This framework
Embargo
Business Continuity through Crisis Situations: Evaluation of Impact of ERP Systems on Business Performance through Crisis Situations in SME of Apparel Industry in Sri Lanka.
(2021) Ranasinghe, N.M.
The economic development is highly impacted by Micro, Small and medium enterprises. In the past few years Micro, Small and medium enterprises have become highly competitive, and the number of organizations have increased with the economic growth. This also has caused Micro, Small medium enterprises to face many challenges and with these challenges to expand their businesses and open organizations to new technologies. But in past few decades academia researchers and business world has debated on the impact that enterprise resource planning systems has on business performance. Further to the effects of enterprise resource planning systems in normal times these systems can positively or negatively impact business performance at the times of crisis. At such times, employees must work remotely due to crisis situations, and this may lead organizations to adapt into enterprise resource planning systems. Hence the main purpose of this research is to evaluate the effects of enterprise resource planning systems have on business performance specially through crisis situations. The crisis in this study mainly focuses on the covid-19 outbreak as this is the most recent crisis that global economy had to face. The globalization has resulted in a complicated, prolonged, and large-scale supply chain. Also, businesses depend on its supply chain to achieve competitive advantage. With the complexity and large-scale nature, the supply chains have become challenging to manage and have been subjected to high risk. To prevail over such supply chain interruptions, sufficient response approaches and risk managing implements should be executed to build an impervious business. This study presents understandings on whether ERP systems facilitates the above-mentioned response plans with interest to minimizing the operational destruction triggered by Covid-19. The study is conducted via exploratory research first to identify the factors which cause successful and productive utilization of ERP systems. The results suggest six likely elements. Based on the findings of the literature review, there hypothesis have been developed to conduct the quantitative analysis. The quantitative analysis is conducted by building a questionnaire and collecting data from stakeholders of Sri Lankan Micro, Small Medium Enterprises (MSME). The questionnaire consisted of thirty six questions – four questions to gather demographic data of the sample and thirty two questions covering six independent variables and two dependent variables with four questions on each variable Four hundred and twenty-three valid responses were gathered from Sri Lankan apparel micro, small and medium enterprises. Data was validated using Cronbach’s Alpha test and the correlation between the variables were assessed using the Pearson’s correlation test. The findings of the study suggests that ERP usage has a highly positive effect on business performance and ERP usage is affected by user satisfaction in a low positive manner while ERP system complexity has a negative low effect on the ERP usage. The paper here onwards builds the framework and hypotheses for the research while showing how the factors suggested the framework. Data collected survey and analysis also is depicted throughout the paper while the last section opens the discussion on how the study gradually concluded the outcomes.
Open Access
Challenges and Opportunities in Mobile Banking Adoption among Young Adults in Sri Lanka
(Sri Lanka Institute of Information Technology, 2025-09) Liyanage S L
This study examines the challenges and opportunities associated with mobile banking adoption among young adults in Sri Lanka. Mobile banking currently provides accessibility, convenience and financial inclusion, yet its adoption remains inconsistent due to issues such as security concerns, lack of trust, digital literacy gaps and infrastructure limitations. While mobile banking provides numerous benefits, such as 24/7 access to financial services, reduced dependency on physical bank branches and increased financial inclusion, significant barriers still need to be addressed. The purpose of this research is to identify barriers, examine user perceptions and propose strategies that can enhance adoption. A structured questionnaire will be administered among young adults in different geographic settings (urban, suburban, rural). To guarantee a thorough understanding, both mobile banking users and non-users will be included. Data collected will be analyzed to highlight trends in adoption, perceptions of security and the role of financial literacy. It is anticipated that the results would help banks, policymakers and tech companies create mobile banking systems in Sri Lanka that are safer, easier to use, and more inclusive. By addressing existing challenges and leveraging opportunities, the research aims to foster a more inclusive and technology-driven financial ecosystem that aligns with global banking trends.
Open Access
Customer Risk Profiling System
(Sri Lanka Institute of Information Technology, 2025-12) GUNASEKARA, G.A.R.
This thesis addresses the critical need for advanced, ethical risk quantification in the motor insurance sector, currently hampered by fragmented data and limited cross-company fraud visibility. The primary objective was to design and validate a Customer Risk Profiling System (CRPS) that integrates heterogeneous data sources and utilizes Machine Learning (ML) for dynamic risk scoring. The methodology involved aggregating data streams including claims, premiums, policy history, and external PEP/AML compliance scores and employing Gradient Boosted Trees (GBTs) to achieve a high classification Accuracy of $0.7561$ and a ROC-AUC of $0.8982$. Empirical findings confirmed the predictive power of behavioral features over linear demographic metrics, validating the choice of non-linear ensemble models. The CRPS successfully segments customers into Low, Medium, and High-Risk tiers, enabling targeted intervention. Crucially, the system embeds Explainable AI (XAI) using SHAP values and a continuous Feedback Loop to maintain accuracy against concept drift, ensuring auditability and ethical governance against potential bias. The study concludes by proposing the Insurance National Grid (NIG), a centralized platform designed to connect all insurers to the regulator. The NIG would enforce data standardization and enable cross-company fraud detection, magnifying the CRPS's impact from a firm-specific tool to a national strategic asset, thereby promoting market efficiency, compliance, and sustained sector resilience.
Open Access
Design and Validation of an AI-Enhanced Career Guidance Framework for Sri Lankan Secondary Education
(Sri Lanka Institute of Information Technology, 2025-12) Karthiha, S.
Career guidance is a decisive factor that contributes to the educational and professional path of students, but in Sri Lanka, the practices that are currently being used are mostly manual, fragmented, and unfair. This paper forms a conceptual model of an Artificial Intelligence (AI)-driven career guidance system that provides students with personalized educational and professional advice to high school students. The study is based on a mixed-methods research framework that combines both quantitative data collected in the form of the survey of 379 educational professionals working in nine provinces and the qualitative information gathered in the form of the interviews with ten experts. Key determinants of career decision-making were identified in a quantitative analysis and found to include academic performance, aptitude, personal interests, and socio-economic background, and six interrelated dimensions were identified in a qualitative thematic analysis: human-dominated guidance, family and cultural influence, perceived fairness of AI systems, ethical and infrastructural barriers, hybrid human-AI collaboration, and equity of access. The presented framework integrates these insights into a hybrid structure, which will combine AI-based analytics with advisory and parental judgment and will be culturally sensitive and ethically valid. The model is more accurate, inclusive, and efficient because it matches student profiles with the trends of the labour-market. The results prove that AI is capable of being more of a supplement to the human knowledge and can be used to increase access to data-driven counselling in even schools with scarce resources. The research has theoretical value in that it links AI technology to the career development theory and practical in that it will provide a replicable solution to the modernization of school guidance ecosystem in Sri Lanka by policymakers and educators. It ends with implementation recommendations on data ethics, transparency and capacity building to attain equitable and evidence based educational and career decisions support.
Open Access
Development of a Neural Network-Based Framework for Skin Disease Recognition
(SLIIT, 2024-12) Senadhipathi, L.A.N.M
Skin diseases impact humans, animals, and plants and are typically brought on by germs or infections. These ailments include ringworm, yeast infections, brown sports, allergies, and other conditions. Early detection can help lessen the impact of diseases. But there are other risks that the skin can encounter, one of which is illness. Fungi, bacteria, allergens, enzymes, and viruses are the main causes of skin problems. Skin conditions impair not just one's physical health but also their psychological well-being, especially in those who have damaged or even scarred skin. Identifying the condition via manual feature extractions or symptom-based approaches requires time and requires comprehensive data for accurate identification. Serious health concerns are associated with skin diseases, which require an accurate and timely diagnosis for appropriate treatment. In particular, convolutional neural networks (CNNs) have shown promising results in automated skin disease identification recently. In this study, A novel CNN-based approach is presented, achieving a 95% accuracy rate in classifying seven different types of skin diseases from the HAM10000 image dataset. Dermatoscopic images from the HAM10000 dataset are preprocessed and categorized into seven classes: basal cell carcinoma, melanoma, vascular lesions, dermatofibroma, melanocytic nevi, and benign keratosis. After extensive testing and fine-tuning, it achieved an overall accuracy of 95% on the testing set. The outcomes show that the suggested CNN-based method can accurately identify a variety of skin conditions by using the HAM10000 picture dataset. Deep learning techniques can significantly help dermatologists and other healthcare professionals diagnose skin conditions accurately and automatically, enabling them to provide prompt and efficient treatments. This work adds a great deal to the growing field of dermatological computer-aided diagnosis and offers valuable data for upcoming advancements in the identification of skin diseases.
Embargo
The Effect of Using Online Platform and Campus-Based Classrooms on Student Performance
(2021-09) Fernando, Anton Jude Kumar
As per the existing environment, the COVID-19 has caused the education system, including schools worldwide, to shut down. Nevertheless, nearly 1.2 billion students are out of school worldwide. As a result, education has undergone substantial changes with the initiation of e-learning, through which teaching is conducted essentially and through interactive platforms with the help of technology today. Although several accept that unplanned and accelerated online learning changes – with little teaching, insufficient bandwidth, and limited scheduling – will create an impediment to long-term success by creating a poor user experience, some feel substantial gains in a new composite educational paradigm. However, considering the objectives of the research, it is to examine the effect of using online platform and campus-based classrooms on student performance, examine the required level of Online Platform and Campus-Based Classrooms in the case study area and also to examine the impact and both positive and negative externalities on online platform and campus-based classrooms on student performance, international schools in western province of Sri Lanka which are significant. However based on research questions, it is to understand the factors affecting the appropriate implementation of online platform and campus-based classrooms on student performance, the economic environment have any significance on online platform and campus-based classrooms on student performance, and finally to identify the variation in the level of the implementation of online platform and campus-based classrooms on student performance, international schools in western province in the country. Nevertheless, considering the results and discussion area, it rejects the null hypothesis of the case study of using online platforms and campus- based classrooms on student performance in international schools in the western province of Sri Lanka. Furthermore, it accepted the alternative hypothesis of the case study, and there is a strong relationship between available technology, teaching interactivity, peer collaboration, student engagement and the effect of using online platforms and campus-based classrooms of online platforms and campus-based classrooms in international schools in the western province of Sri Lanka while showing certain variations according to summary item statistics and inter item iv covariance. Therefore, it is required to understand the ways and means of enhancing available technology, teaching interactivity, peer collaboration, student engagement and the effect of using online platforms in order to get better results from online platform and campus-based classrooms in international schools in western province in the country.
Open Access
Enhancing Block chain Technology in Governmental Operations: A Comprehensive Framework for User Adoption
(SLIIT, 2024-12) Gnanasekara, H. A.
The purpose of this study is to investigate the factors that affect Blockchain adoption in governmental operations in Sri Lanka, and thereby to propose a comprehensive adoption framework for Blockchain technology in Sri Lankan governmental operations. The TechnologyOrganization-Environment (TOE) framework is utilized for this purpose due to its capacity to encapsulate the complexities of technological adoption in the public sector, effectively addressing both internal (organizational) and external (environmental) factors that influence the adoption process. Given the unique structural, regulatory, and data sensitivity challenges of governmental settings, unlike many other models, TOE framework comprehensive integrates employee insights from technological, organizational, and environmental perspectives, making it both adaptable to the public sector’s needs and scalable for implementation across various government entities. More importantly, the TOE framework is uniquely designed to account for the regulatory and operational needs specific to Sri Lankan public sector institutions. It addresses critical compliance requirements, such as data privacy and security regulations, while aligning with the structural intricacies of government workflows, enabling a practical pathway for Blockchain adoption that respect the local regulatory landscape and operational demands. This study has employed various statistical methods to ensure the validity and reliability of the data collected through a structured questionnaire distributed to Grade I – IT Directors to obtain their perceptions and experiences with Blockchain technology. Using the structural equation modelling (SEM), this study reveals that all three perspective of the TOE framework (i.e. technological, organizational and environmental) are significantly influence the Blockchain technology adoption in Sri Lankan governmental operations. More specifically, SEM results show that compatibility, trust, security, support from higher authority, monetary resources, firm size, regulatory support, and rivalry pressure are critical technological, organizational and environmental determinants in developing an adoption framework for Blockchain technology in Sri Lankan governmental operations. The expected impact of the proposed framework based on the findings of this study on public sector operations includes significant improvements in processing times for administrative tasks, a reduction in risks of corruption, and enhanced citizen trust through greater transparency. By addressing the specific challenges faced by the Sri Lankan government in adopting Blockchain technology, this study contributes valuable insights to the discourse on digital transformation in public sector operations
Embargo
Enhancing the value proposition of priority Banking in SriLanaka
(Sri Lanka Institute of Information Technology, 2025-10) NAWARATHNE,I.K.S.S.
Priority banking has become a vital strategic focus for banks and financial institutions aiming to retain and grow affluent, high-net-worth client segments. In Sri Lanka, despite significant investments in branded premium offerings, evidence suggests that the actual value perceived by customers often falls short of expectations. Past studies highlight key issues, including a lack of personalization, insufficient digital integration, reactive relationship management, and limited lifestyle benefits. Customer feedback reinforces this gap, with many clients perceiving “priority” services as symbolic rather than substantive. This research investigates how Sri Lankan banks can enhance the value proposition of their priority banking services by aligning more closely with evolving elite customer expectations. Using a mixed-method approach, the study integrates surveys and interviews with relationship managers and priority clients in certain states and private banks in Sri Lanka, supported by secondary data from reports and academic studies. Findings reveal that while customers value exclusivity and trust, true loyalty is undermined by generic service delivery and limited proactive engagement. To address these gaps, this study proposes the P.E.A.K. Model, a strategic framework emphasizing Personalization (tailored financial and lifestyle solutions), Experience Integration (seamless digital- physical journeys), Anticipation (proactive, predictive engagement), and Knowledge Empowerment (financial literacy and advisory tools). The model underscores the importance of leveraging AI- driven personalization, omnichannel CRM, and well-trained relationship managers to create a more differentiated and meaningful client experience.
Open Access
Evaluating Digital Banking’s Impact on Customer Preferences and Employee Efficiency in Sri Lanka
(Sri Lanka Institute of Information Technology, 2025-12) Deshapriya,A.N.E.
The fast expansion of digital banking in Sri Lanka has significantly transformed the financial services landscape, affecting both customer choices and internal operational processes within banks. This research investigates how digital platforms influence customer behavior by examining key aspects such as convenience, trust, system reliability, ongoing IT skill enhancement, and organizational culture. Simultaneously, it evaluates employee efficiency in relation to process automation, technological preparedness, digital competence, and overall performance. A mixed-methods approach is employed, combining customer surveys with interviews conducted among employees across various banking departments. Findings reveal that perceived usefulness, and data security are central to enhancing customer satisfaction, fostering trust, and driving digital adoption. For employees, efficiency improvements are strongly linked to adequate training, streamlined systems, and effective integration of digital tools, which together support sustainable digital transformation and compliance with regulatory standards. Based on these insights, the study recommends strengthening digital infrastructure, advancing employee training programs, improving user interfaces, and promoting digital inclusivity to build customer confidence. The outcomes provide practical guidance for banks, policymakers, and IT solution providers in advancing customer-focused and digitally empowered banking services in Sri Lanka.
Embargo
Explore the Scope to Implement Intelligent Enterprise Resource Planning (I-ERP) System in Business Processes
(2021-09) Senarathna, D.M.I.P.
Businesses are known to invest a significant capacity of their funds towards the implementation of Enterprise Resource Planning (ERP) as well as Business Intelligence (BI) systems in order to gain a sustainable competitive edge, to be in par with the advantages that the latest technological advancements are able to provide and to ensure the smooth running of their work processes. In today’s challenging economic environment contained by the context of complicated BI and ERP, these techniques have become key strategical tools, which promptly influences the success of any software project implementation. Not much attention has been given to the integration of “Business Intelligence and Enterprise Resource Planning” which was convert form the traditional ERP system to an Intelligent-ERP system. A few experiments had been conducted in some organizations but most of the projects did not see a conclusion towards a successful integration or completion of the project due to various challenges faced during the implementation process. This research attempts to evaluate and assess the implementation of an Intelligent Enterprise Resource Planning (I-ERP) System in business processes in the company Norlanka Manufacturing Colombo Limited in Sri Lanka; in order to increase efficiency of business processes and use human knowledge effectively for decision making activities and to overcome business related issues such as repetitive and tedious work processes, increased costs and unproductivity. The main methodology that was utilized in this thesis was a primary method that utilized surveys to obtain information from 145 employees working in Norlanka. Using the SPSS tool, the data was analyzed using a number of statistical methods such as correlation, frequencies, descriptive and inferential analysis and the findings revealed that there was a significant link between the independent variables, business values, user engagement, user adoption, operational costs, operational efficiency with the dependent variable implementation of I-ERP in Norlanka. Furthermore, recommendations included, ensuring a change management team is in place, making sure top-level management and other key stakeholders were actively involved and the aspect of customization was considered according to the organization’s aim, objectives and scope.
Embargo
Factors Determining the User Adoption of Biometric Authentication in Sri Lankan Payment Channels
(2021) Balasuriya, D.M.
Payment channels have become an essential element in people’s lives. There are many emerging payment channels in Sri Lanka such as EDC (Electronic Data Capture)/ POS (Point of Sale)/ kiosks/ Internet Payment Gateway/ Mobile based payment solutions. With the emergence of new payment channels individuals have shown keenness on moving to digital payment channels instead of traditional methods because of its convenience. Despite its popularity it has become a source for financial frauds. The main reason being the lack of security and privacy of payment channels. Most payment channels use traditional authentication methods such as passwords, PIN (Personal Identification Number), OTP (one-time password), or digital cards. These authentication methods are vulnerable, and they are quite often compromised by fraudsters. There financial organisations are now looking at a more user/customer centric authentication method. Biometric is a user/customer centric authentication method where the authentication happens using a biometric trait of the user/customer. Since most Sri Lankans are not tech-savvy it is somewhat of a challenge to make customers use payment channels that has biometric authentication. Therefore, this study looks at the factors that will determine the user adoption of biometric authentication. In order to identify potential factors that could affect biometric adoption a comprehensive literature review was conducted. What previous researchers around the world have discovered were useful in deciding the potential factors; security and privacy, reliability, usefulness, and ease of useTo quantify the impact of each factor on biometric adoption a quantitative research study which had 390 respondents was conducted by the author. After a thorough analysis of the responses, it was identified that security and privacy, reliability, usefulness, and ease of use influences the user adoption of biometric authentication in Sri Lankan payment channels. The author believes this report will be beneficial to various stakeholders in the financial industry and the information technology industry in Sri Lanka as well as similar demographic segments.
Open Access
Impact of Artificial Intelligence towards democracy in modern society
(2021-04) Amarasekara, E.A.V.A.L
Artificial intelligence consciousness has gotten a ton of consideration lately, and it's relied upon to have a major effect on our networks later on, with both positive and negative ramifications for vote based system. In this paper, I take a gander at what Artificial intelligence brainpower can mean for common freedoms and majority rules system, assessing the outlining of difficulties, arrangements, and management work in three unique situations. I built up a hypothetical structure dependent on past investigations in this field to play out a near contextual analysis between the European Commission and two nations that are at the front line of understanding AI's difficulties, in particular Sweden and France. The discoveries show that while a few issues are perceived as basic, a few issues, like security, are focused on. As far as difficulties, arrangements, and guidelines, there are a few varieties between the three cases, however, their techniques are fairly comparable. Sweden's technique is to put resources into cultural change by empowering more AI exploration and collaboration, while additionally being steady of guidelines in specific zones. France takes a more management weighty position, suggesting some AI limitations in protection, fighting, and the work market. To make AI more accommodating, the European Commission is underlining responsibility in AI methodology. The shared factor is that the two of them overlook the issue of political race impedance and online right to speak freely, which is distinguished in the writing as one of AI's significant difficulties