Graduate of the MSc in Global Central Banking and Financial Regulation, June Otigba, reflects on her experience and learnings.
MSc Global Central Banking and Financial Regulation participant, Jun Anthony Garcia, shares his experience on the Behavioural Finance and Big Data module. Currently working for an investment bank in London as the VP in the compliance and risk area of the bank, Jun explores how this module has supported his current role and career.
What has been your experience of the Behavioural Finance and Big Data module?
The module itself is relevant to what I do and I've decided to write my dissertation about big databases, specifically exploring machine learning and its application in risk and compliance. So much of the content on this module has supported my research. It's a practical module and is really cutting-edge in terms of knowledge and insights. Plus, I have found the combination of behavioural finance and big data really interesting given the intersection of topics and the potential for using big data analytics on insights gained from behavioural finance.
The two aspects of this module complement and interact with each other as the exercises in the big data content is related to theories or topics that you've covered in the behavioural finance content.
Behavioural finance is more theoretical in terms of the approach, the teaching staff were really approachable and easy to speak to. If you had a question, you could also raise that through the my.WBS platform, which also included a complete reading list of reference materials.
The big data aspect of the module is much more practical. This could make it more difficult as you do need to spend time working through the coding exercises. Each section of the module involves completing a sort of mini-coding project, which is time-consuming. However, there are support materials available and a reference book. Neil Stewart, our instructor for big data content, was also helpful and approachable. Although the initial process of setting up your machine and then understanding the language syntax was difficult, if you do invest time working on each of the sections, you do learn effectively how to code which is a good skill to have. Whilst I don’t code in my day-to-day role, having a good understanding of the various machine learning algorithms helps in my interaction with colleagues in the bank since these big data tools are currently being deployed across the organisation.
How much have you been able to use what you learned from the module in your current role?
I initially proposed my research topic internally at work, but I have expanded the question to cover a broader subject area so that it is not too specific to what I do, as the application is actually broader than what I currently work on now.
The coding part is essential to my research, as around 90% of my research relies on the code and implementing what I've learned on the big data module. In this way, the module content has been useful as I have been applying that knowledge in my research and then eventually I hope that it will be implemented within my role at the bank too.
How was the module assessed?
An essay assessed the behavioural finance content. The big data part of the module was more like a project, as you would be expected to code as part of that assessment, and develop an essay that explains what the project is and how you have applied concepts explored in the module. My project focussed on sentiment analysis of central bank speeches and how it is being applied in finance. In particular, potential insights for market participants ahead of formal monetary policy announcements.
What was the highlight of the module for you?
I think the main highlight is the new skills that you pick up. When I first joined the course, I wasn't expecting to be picking up a coding skill or master machine learning algorithms, so for me, that is probably the highlight of the module. Coding is only one of the many skills that you pick up when you take the modules in the course, but I think that it's an invaluable skill that you may not necessarily use directly but you can't avoid dealing with big data/ machine learning in our current environment. Understanding the models and how the algorithms work, their applications and most importantly their limitations, is a critical skill to have in practice especially for risk and compliance functions within banks when you're dealing with machine learning or big data-driven processes.
What’s the next step for you?
My dissertation for the MSc focused on machine learning applications for risk and compliance functions at banks. My paper bridged a gap in existing academic and industry papers and demonstrated a proof-of-concept for a novel use case of big data/machine learning at financial institutions.
I shared a copy of the final paper internally at the bank and have been soliciting support from my management team to consider implementing the project. I also intend to share the paper more broadly given the potential for the concepts presented in my paper to be applied by central banks, regulators and professional services firms. Future research can also build upon my work and deploy the code I wrote in generating synthetic data to explore high-impact use cases in risk or compliance.
I have also leveraged the networks I gained during the course to introduce experts in behavioural finance from WBS to my senior management to kick off discussions on how we can apply behavioural insights successfully at the bank.
All the modules I elected to complete as part of the MSc have been useful in terms of complementing the practical experience I gained in the industry, reinforcing my existing quantitative expertise in finance and introducing me to new subject areas (e.g. monetary policy, central banking and behavioural finance/big data) that are in the midst of disruptive innovation. I intend to gain further expertise in big data and machine learning by getting involved in projects and initiatives internally and be recognised as a machine learning/big data subject matter expert within the risk and compliance function at the bank.