Hello!

Welcome to my blog! I am a Mathematics and Marketing graduate from the University of New South Wales with a strong interest in statistics, data science, insurance and, more broadly, understanding how quantitative methods can be used to solve real-world problems.

This blog is my personal learning diary. It is a place where I can look back and see how my understanding has developed over time, but, just as importantly, it is a space where I can share what I learn with others.

Helping people understand difficult ideas has always been something I enjoy, whether through tutoring, working with peers, or breaking down concepts in my own notes. I strongly believe that topics which initially appear intimidating can become intuitive when they are explained carefully and from the right perspective.

Much of what I write here reflects that philosophy. Rather than simply presenting formulas or code, I want to understand where ideas come from, why they work, and how they can be applied.

Passions

Coming from a background in Mathematics and Marketing, I have always been interested in the intersection between quantitative analysis and strategic decision-making.

Mathematics taught me how to approach problems rigorously, while Marketing taught me to think about behaviour, competition and decision-making in a much broader context. Together, they shaped my interest in using data not merely to describe what has happened, but to understand why it happened and what should be done next.

More recently, my interests have increasingly moved towards statistics, statistical inference and data science. I have been developing my skills in R and Python while studying topics including regression, generalized linear models, maximum likelihood estimation, statistical inference and machine learning.

I am particularly interested in understanding the theory behind the models I use. For me, being able to run a model is only the beginning. I want to understand its assumptions, how its estimators are derived, what uncertainty surrounds its predictions, and when a different statistical approach may be more appropriate.

Career

My interest in insurance began after participating in a data science competition organised by Allianz and Atlassian, where the task involved classifying fraudulent insurance claims. A blog post about that project can be found here.

That experience introduced me to an industry where statistics, risk, economics, behavioural decision-making and data intersect in fascinating ways.

I subsequently entered the insurance industry and currently work as a Claims Specialist at Longitude Insurance, managing commercial and residential strata property claims.

Working in claims has given me exposure to the practical side of risk. A claim is rarely just a number in a dataset. It involves policy interpretation, technical reports, competing accounts of events, financial consequences and decisions made under uncertainty.

Some of the most valuable skills I have developed include:

• Analytical reasoning: Interpreting policy wording and assessing complex claims requires synthesising information from engineers, builders, consultants, brokers and insureds before reaching a defensible conclusion.

• Strategic decision-making: Managing claims requires balancing policy coverage, available evidence, commercial considerations and stakeholder expectations while anticipating how a decision may develop over time.

• Communication: Complex technical and contractual information often needs to be translated into clear explanations for people with very different levels of subject-matter knowledge.

My experience in insurance has also strengthened my interest in the quantitative side of the industry. I am particularly interested in how statistical modelling can be applied to problems such as claim frequency, claim severity, fraud detection, pricing and risk prediction.

Learning and Projects

Outside of work, I spend a significant amount of time independently studying statistics, machine learning and data science.

My current learning includes statistical inference, generalized linear models, R and Python, alongside further study in deep learning through the DeepLearning.AI Deep Learning Specialisation.

I also use this blog to document projects and concepts as I work through them. These range from relatively simple statistical models to larger data science projects, including fraudulent insurance claim classification.

The purpose is not to present myself as knowing everything. Quite the opposite. I want this site to capture the process of learning: asking questions, making mistakes, deriving results, writing code and gradually developing a deeper understanding.

Future Aspirations

My long-term interests sit at the intersection of statistics, data science and insurance.

I enjoy problems that require both mathematical reasoning and practical judgement, particularly where data can be used to better understand risk and support decision-making.

Going forward, I want to continue strengthening my foundations in statistical theory while developing the computational skills required to apply those ideas to real-world problems.

Ultimately, I hope this blog becomes a record of that journey and, along the way, a useful resource for anyone else trying to understand the same ideas.