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Technical Evaluation and Professional Reflection on a Cloud-Based Machine Learning Fraud Detection Solution
This individual technical evaluative report forms the reflective and critical component of the Machine Learning on Cloud module. It builds directly upon a group project involving the development of a machine learning solution for financial fraud detection. The report requires each student to critically evaluate the technical decisions made within the group solution while also reflecting on their individual contribution, teamwork experience and professional development. The first component, Technical Evaluation, accounts for 50% of the individual assessment and has a suggested allocation of approximately 1,000 words. Students critically examine the group’s machine learning solution, including the approaches adopted for data preprocessing, model selection and evaluation. They are expected to discuss the suitability of the chosen techniques and metrics, identify limitations and explain how individual technical decisions affected the final performance and outcome of the fraud-detection solution. The second component, Reflection and Professional Issues, also accounts for 50% and has a suggested allocation of approximately 1,000 words. Students reflect critically on their personal contribution to the project and their experience of working within a team. The discussion addresses challenges encountered, lessons learned and how the experience contributed to technical and professional development. Professional and ethical considerations form an important part of the reflection. Relevant issues include algorithmic bias, fairness, sustainability and data privacy, particularly in relation to machine learning applications within financial services and cloud environments. Students are also required to attach their Seminar Activity Tracker as an appendix, providing evidence of weekly participation and knowledge development. The assessment therefore combines technical critique with reflective practice, requiring students to demonstrate that they understand not only how a machine learning solution was developed, but also why specific technical decisions were made, their consequences, the limitations of the resulting system and the wider ethical and professional implications of deploying AI on cloud infrastructure. Overview word count: approximately 315 words. The brief also states that AI may assist with areas such as grammar, structure, organisation of ideas and suggestions, but the main content, analysis and conclusions must be the student's own work, and AI use must be declared.
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Machine Learning / Cloud Computing
3,984 words
Machine Learning on Cloud: Fraud Detection Model Design, Training and Evaluation
This postgraduate group project focuses on the design, development and critical evaluation of a cloud-based machine learning solution for financial fraud detection. The scenario involves a financial services organisation seeking to detect fraudulent transactions in order to reduce financial losses and improve customer security. Students are required to analyse an appropriate dataset, develop a machine learning solution, evaluate its effectiveness and critically consider the suitability of cloud technologies for deployment. The project begins with a cloud feasibility study, requiring critical comparison of at least two major machine learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Evaluation criteria include performance, scalability, cost, compliance, integration and vendor lock-in, followed by a justified recommendation. Students then conduct exploratory data analysis to identify patterns, anomalies and correlations using appropriate visualisations such as heatmaps, histograms and boxplots. A substantial component addresses data preprocessing and class imbalance. Students are expected to clean and transform the data, apply scaling and encoding, perform feature engineering and investigate approaches such as SMOTE, undersampling and cost-sensitive learning. Each preprocessing choice must be justified in terms of its potential impact on model performance. Students must select and train at least two machine learning models, with suggested approaches including Logistic Regression, Random Forest, XGBoost and Neural Networks. Model development incorporates cross-validation and hyperparameter tuning. Evaluation uses fraud-relevant measures including Precision, Recall, F1 score, AUC and precision-recall curves, supported by confusion matrices, ROC curves and feature-importance visualisations. The project concludes with critical consideration of professional and ethical issues in cloud-based AI, including bias, fairness, transparency, data privacy and sustainability. The overall assessment therefore integrates cloud-platform evaluation, machine learning development, imbalanced classification, model evaluation and responsible AI practice. Overview word count: approximately 340 words.
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Cloud Computing / Big Data Technologies / Cyber Security
2,500 words
Cloud and Big Data Security Application: Design, Implementation and Evaluation
This assessment for the Cloud and Big Data Technologies module requires students to design, implement and evaluate an individual cloud-based or distributed data application. The project focuses on practical solutions involving the complex transformation, processing, storage and security of big data within cloud environments. Students are expected to demonstrate how distributed data can be organised in the cloud, how data pipelines can be used to access or process distributed databases, and how appropriate security controls can be incorporated into the resulting architecture. Students have considerable freedom when selecting their application. Suggested project directions include developing a data-science solution using SQL or MongoDB with cloud storage and an appropriate security policy; implementing privacy-preserving distributed processing using techniques such as Differential Privacy; creating multi-party authentication and group-based access-control mechanisms; or designing Multi-Level Security, Attribute-Based Encryption or Role-Based Access Control solutions. Projects may also examine distributed or cloud applications using security protocols such as SSH, SSL or IPsec. Creativity and originality are explicitly encouraged. The written component is a Design and Implementation Document of no more than approximately 2,500 words. It should present the project aims and objectives, application concept, cloud and security technologies, functional and security requirements, architecture and design decisions, protocols, access-control mechanisms, implementation process, achievements, problems encountered and overall evaluation. Relevant diagrams, such as interaction or sequence diagrams, may be used to explain system behaviour and architecture. The assessment also requires submission of the functioning Cloud and Big Data Security application and a 7-minute highlight demonstration video. The video should demonstrate the application's major features, implementation details, security functionality and, where appropriate, attack scenarios. Assessment places strong emphasis on the quality of the design and implementation documentation, originality, use of advanced features, and the overall effort and technical quality of the completed application. Students are therefore expected to demonstrate independent development rather than simply reproduce an existing tutorial.
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