Dr Anthony Bellotti
Associate Professor in Computer Science
The second Credit Scoring and Credit Rating Conference will be held this year at the University of Nottingham Ningbo China between 14 and 16 October, with the chosen theme being “green finance and smart credit”. This conference will be a showcase of state-of-the-art credit risk analysis both in China and internationally. Dr Anthony Bellotti, who has 16 years of experience studying statistical models and machine learning in consumer credit risk, is one of the leading members of the conference’s academic committee.
Dr Anthony Bellotti first joined the University of Nottingham Ningbo China in 2019 as an Associate Professor in Computer Science. He received his PhD in Machine Learning from Royal Holloway, University of London in 2006 with a focus on its medical application for diagnosis of leukemia subtypes.
His research into credit risk models dates back to early in his career working at the Credit Research Centre, based at the University of Edinburgh. At that time, his work on machine learning and statistical modelling in credit scoring was advanced. After this, he became a senior lecturer at Imperial College London until 2019 where he taught quantitative methods in retail finance.
Since 2021, Dr Anthony Bellotti has been leading a team of five academic staff from FoSE Department of Computer Science and one member of staff from the Faculty of Business, working collaboratively on the credit risk assessment and decision support for smart finance in Ningbo. The project has received RMB one million funding from the Ningbo Municipal Government and was awarded the Ningbo 3315 Innovation Team award in November 2021.
Anthony and his team members are working with the Ningbo Financial Regulatory Bureau on the data collected by the Ningbo Big Data Bureau from multiple municipal government departments and are developing a model for smart credit scoring and detecting fraud in financial transactions.
Credit scoring is a statistical analysis performed by lenders and financial institutions to determine the creditworthiness of a person or a small, owner-operated business. Credit scoring is used by lenders to help decide whether to extend or deny credit. Through this, local financial institutions, especially these small credit facilities, are able to reduce their risk in lending loans and become more profitable. But more importantly, Anthony emphasised that this area of research will improve local financial inclusion. He gave an example to further illustrate this.
“When low-income people need funding for their own business, they may face multiple rejections because the bank may think it risky to give a loan to a person on a low income. However, they may still be good people. If you check their records for paying bills you will see that they are reliable people. But the computer system in big banks may not take this into account. Therefore, we would like to build a better model can take into account more data, for example, water repayments and other utility bills.”
The idea is to use people’s repayment history for other products or utilities is a way of assessing individual. This is an international area of research, and many countries are investigating this use of new data sources. “We have the data provided by the bureau and we can already start to work with it” said Dr Anthony Bellotti. His team use the federated semi-supervised learning method to analyse and process the data. Through reinforcement learning, the model is able to perform self-renovation and detect a user’s abnormal behaviour.
Anthony also told us that traditionally the assessment for a loan can be quite precise and inflexible. Only recently have financial institutions and researchers started to look into the broader picture, such as how people behave in terms of paying their electricity or water bills , and this has been made possible because through the advent of Big Data.
This research project is still in its early stages, but Dr Anthony Bellotti believes that in the future it will help to build a sustainable and effective way for individuals and small businesses to have access to useful and affordable financial products and services that meet their needs, whilst maintaining prudent risk control in financial institutions. This project will also be of great benefit to the citizens of Ningbo City, supporting development and inclusivity.