PhD Application in Data Science and Analytics: Exploring OpRisk with a Focus on Advanced Measurement Approach

As a junior high student, I actively participated in various mathematical and statistical competitions, winning numerous first prizes and developing a keen interest in mathematics. In today's world, Data Science and Analytics have become increasingly significant and have penetrated every aspect of people's lives. Given my personal interests and strengths, I pursued a major in Statistics at the Central University of Finance and Economics (CUFE), where I was the 300th (top 0.1%) examinee of the College Entrance Examination.

During my college years, in addition to focusing on my studies, I also participated in the college debate team and won the 2012 'Zhongcai Cup' Debate Competition as the best debater. This experience trained me in excellent understanding, information integration, and logical thinking abilities. As an international exchange student at Örebro University in Sweden from 2012 to 2013, I experienced the differences between Chinese and Western educational systems and concepts, as well as the collision of different diets, habits, ideas, and cultures. The Swedish education system was different from the exam-oriented education in China, as the teacher focused on teaching the underlying logic of subjects and problem-solving methods. The diversified and relaxed academic environment gave me opportunities to cultivate a personality of independent thinking, disassemble and analyze problems, and master the data analysis tools needed to solve problems through self-study. This experience instilled in me a desire to apply for a Ph.D. for further study.

After graduating from CUFE in 2014, I pursued a master's degree in Statistics at the University of Hong Kong. It was during this time that I discovered the laws and logic behind human behavior in data, which I found incredibly meaningful. I took the lecture on Artificial Intelligence and chose the cat and dog recognition project, which I thought would play a necessary role in the fields of autonomous driving, security, and face recognition. At first, the results were not satisfactory, but through discussion with my tutor and self-study, I increased the image recognition success rate to 90%. However, I still believe that the application range of convolutional neural networks is narrow, and such an excellent algorithm should have more room for imagination. I look forward to gaining more inspiration through research in Machine Learning, Artificial Intelligence, and Algorithms from the doctoral program at the University of Hong Kong.

After graduation, I gained professional experience in the world's top 100 insurance, banking, securities, and fund companies, focusing on practical exploration in the fields of data governance, data modeling, data mining, and risk quantification. This experience provided me with good adaptability and fast learning abilities, and helped me master the mainstream data analysis and visualization tools in the market. I have specialized in data collection, data warehouse, data modeling, algorithm optimization, data visualization, and commercial promotion in the field of financial technology, such as digital empowerment marketing, big data intelligent risk control, and digital anti-fraud. My professional experience has given me a solid foundation in data manipulation, modeling, and visualization, as well as a global view of data analysis applications.

In recent years, with the vigorous development of China's Internet industry, daily EB-level data has provided data engineers with a wonderful scientific research soil, and data science has also flourished. My team has been continuously exploring the commercial application promotion of models and the construction of corporate data culture. Through graph computing and research on relational networks, we have achieved fruitful breakthroughs in the practice of big data anti-fraud. I hope to enrich myself through rigorous and systematic Ph.D. study and enhance my depth in the field of data analysis.

Compared with the application of relatively complete data analysis technology to the development of credit risk and market risk measurement, OpRisk that lacks sufficient data for risk measurement is also an area I want to explore. The advanced measurement approach (AMA) is the one I want to spend most of my time working on, which is an advanced form of the internal measurement approach. I hope to study and research the internal measurement approach (IMA), scorecard approach (SCA), and loss distribution approach (LDA) systematically under the supervision of my instructor.

Based on the extensive use of the Monte Carlo method during my study in HKU, I am extremely excited to explore the rules of some mainstream closed form LDA models such as the Poisson α-stable LDA analytic one further. Furthermore, I will use the knowledge of risk quantification, analysis, and management that I have learned before to conduct in-depth research in the fields of intelligent risk control, digital anti-fraud, and risk quantification so as to realize the organic combination of 'breadth' and 'depth' of knowledge through my Ph.D. project.

I have challenged myself time and time again and look forward to reopening the hard mode of life after working in the industry for 6 years. I strongly believe that the exposure of this project will enrich and perfect myself academically, and build a new starting point and height for my academic career. I am confident that the University of Hong Kong, with its top reputation and research strength, will provide me with the opportunity to achieve my academic goals and make a significant contribution to the field of data analysis.