Starting a research project requires meticulous planning and a strategic approach to ensure successful outcomes. In my ongoing research on credit risk modeling, integrating project planning, management, and evaluation from the beginning is crucial. This post explores how I am preparing for these elements to enhance the effectiveness of my research.

Project Planning

  • Objectives of the Research: My primary objectives are to develop stable and accurate credit risk models for individuals with little to no credit history. This involves enhancing the predictive accuracy of existing models and ensuring their stability across different economic conditions.
  • Key Milestones and Timelines: The project is divided into several key milestones, each with specific timelines:
    • Data Collection & Analysis (Month 1): Gathering historical loan and borrower data from the Kaggle platform and perform a preliminary analysis.
    • Model Development (Months 2-4): Developing and training machine learning models using the collected data .
    • Iterative Testing and Validation (Months 5-6): Test the models and validate their performance using cross-validation techniques.
    • Final Evaluation and Reporting (Month 7): Conduct a comprehensive evaluation of the models and compile the findings into a final report.
  • Resource Allocation and Budgeting: Effective resource allocation is crucial for the success of the project. Key resources include access to high-quality data, computational power for model training, and software tools for data analysis. Budgeting will focus on data acquisition, software licenses, and any necessary hardware upgrades.
  • Alignment with Research Goals: The project plan is designed to align with the overarching goals of improving credit risk assessment methods. By setting clear milestones and allocating resources efficiently, the project aims to achieve significant advancements in model accuracy and stability.

Project Management Approach

  • Overview of Project Management Methodologies: I will employ Agile (Stellman & Greene, 2014) methodologies, which are well-suited for research projects that require flexibility and iterative development. Agile allows for continuous feedback and improvements, which is essential for refining complex models like those used in credit risk assessment.
  • Tools and Techniques for Managing the Project:
    • Gantt Charts: Gantt charts (Meardon, 2024) will be used to visualize the project timeline, including key milestones and task dependencies. This will help in planning and tracking progress, ensuring that the project stays on schedule.
    • Kanban Boards: Kanban boards will help in managing ongoing tasks, allowing for a clear view of what needs to be done, what is in progress, and what has been completed. This tool is especially useful for managing day-to-day activities and ensuring a steady workflow (Rehkopf, 2024).
  • Strategies for Self-Management and Staying on Track: Given that this project is primarily an individual effort, self-management strategies are critical. These include setting daily and weekly goals, regular progress reviews, and maintaining a disciplined work schedule. Using tools like Trello (Trello, 2024) for task management and Google Calendar (Google, 2024) for scheduling will help in staying organized and focused.
  • Planned Tool Utilisation: A Gantt chart will outline the project phases from data collection to final reporting, helping to identify critical paths and potential bottlenecks. Meanwhile, the Kanban board will visually represent individual task progress, ensuring all activities align with the project timeline.

Research Evaluation Approach

  • Planned Methods for Assessing and Evaluating Outcomes: The evaluation of research outcomes will involve both quantitative and qualitative methods. The models will be assessed by key performance indicators (KPIs) such as predictive accuracy and model stability, as measured by the gini stability metric (Herman et al., 2024).
  • Specific Metrics for Evaluating Success:
    • Predictive Accuracy: This will be measured using metrics like the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) (Google, 2022) and accuracy scores (Google, 2022a). High predictive accuracy indicates that the model effectively distinguishes between different credit risk levels.
    • Model Stability: Evaluated using the Gini Stability Metric (Herman et al., 2024), which assesses how the model’s performance remains consistent across different economic scenarios and demographic segments.
  • Proposed Data Collection and Analysis Techniques:
    • Data Collection: Secondary data from the Kaggle platform (Kaggle, 2024) will be used, providing a rich dataset of borrower characteristics and loan outcomes (Herman et al., 2024).
    • Data Analysis: Ensemble machine learning techniques, such as Random Forests and Gradient Boosting Machines, will be employed due to their robustness and ability to handle large datasets with complex interactions Barbaglia et at., 2021; Gunnarsson et al., 2021; Xia et al., 2017).
    • Cross-Validation: This technique will be used to validate the models by training them on different subsets of the data and combining their predictions to improve overall accuracy and stability (Berrar, 2018).
  • Importance of Iterative Testing and Validation: Iterative testing allows for continuous refinement of the models. Continous refinement is an important aspect of agile methodology (Stellman & Greene, 2014). By regularly testing and validating the models, any issues can be identified and addressed early, ensuring that the final models are robust and reliable.

Integrating the Elements

  • Interrelation of Project Plan, Management Strategies, and Evaluation Methods: The project plan provides a roadmap, detailing what needs to be done and when. The management strategies, such as Agile methodologies and visual tools, ensure that the project stays on track and adapts to any changes. The evaluation methods continuously assess progress and guide adjustments, ensuring that the research objectives are met.
  • Tool Integration: For example, during the model development phase, the Gantt chart will help in scheduling the tasks, the Kanban board will track the progress of each task, and the evaluation metrics will assess the models’ performance. If any issues are identified during testing, the Agile approach will allow for quick adjustments and re-evaluation.
  • Anticipated Benefits of a Cohesive Approach: A cohesive approach ensures that all elements of the project are aligned and working towards the same goals. This not only improves efficiency and effectiveness but also enhances the overall quality of the research outcomes.

Summary

A holistic approach to project planning, management, and evaluation is crucial for the success of research projects. By integrating relevant tools and techniques from the outset, I aim to complete this research project in a timely and efficient manner. However, it is crucial to ensure these tools facilitate the process rather than introduce unnecessary complexity. This approach not only ensures the reliability of the outcomes but also enhances their practical applications in financial decision-making. As I progress with my project, I will regularly update my blog with insights and developments, inviting readers to follow along and learn from my experiences.

References:

Barbaglia, L., Manzan, S. and Tosetti, E. (2021) ‘Forecasting loan default in Europe with machine learning’. Journal of Financial Econometrics, 21(2), pp. 569 – 596

Berrar, D. (2018) Cross-validation. Encyclopedia of Bioinformatics and Computational Biology. 1(2018), pp. 542 – 545

Google. (2024) Google Calendar. Available at: calendar.google.com (Accessed: 26th May 2024)

Google. (2022) Classification: ROC Curve and AUC. Available at: https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc (Accessed: 22th May 2024)

Google. (2022a) Classification: Accuracy. Available at: https://developers.google.com/machine-learning/crash-course/classification/accuracy (Accessed: 22nd May 2024)

Gunnarsson, B., Broucke, S., Baesens, B., Óskarsdóttir, M. and Lamahieu, W. (2021) Deep learning for credit scoring: Do or don’t? European Journal of Operational Research. 295(1), pp. 292-305

Herman, D., Jelinek, T., Reade, W., Demkin, M. and Howard, A. (2024) Home Credit – Credit Risk Model Stability. Kaggle. Available at: https://kaggle.com/competitions/home-credit-credit-risk-model-stability (Accessed: 26th May 2024)

Kaggle. (2024) Kaggle. https://www.kaggle.com (Accessed: 26th May 2024)

Meardon, E. (2024) What are Gantt charts? Available at: https://www.atlassian.com/agile/project-management/gantt-chart#:~:text=a%20Gantt%20chart%3F-,A%20Gantt%20chart%20is%20a%20project%20management%20tool%20that%20illustrates,schedule%20bars%20that%20visualize%20work (Accessed: 26th May 2024)

Rehkopf, M. (2024) What is a kanban board? Available at: https://www.atlassian.com/agile/kanban/boards (Accessed: 26th May 2024)

Stellman, A. and Greene, J. (2014) Learnung Agile: Understanding Scrum, Xp, Lean, and Kanban. California: O’Reilly

Trello. (2024) Trello. Available at: https://www.trello.com (Accessed: 26th May 2024)

Xia, Y., Liu, C., Li, Y. and Lie, N. (2017) ‘A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring’. Expert Systems with Applications, 78 (2017), pp. 225-241

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