Operational Finance and Lending Dynamics
Research Projects
Credit Risk
Project Title: Portfolio Credit Analytics
Principal Investigator: Associate Professor Huang Ke-Wei
Summary: Current fintech lending primarily focuses on leveraging IT technologies to set up new platforms and distribution channels. Data analytics is traditionally applied in sorting out good from bad credits but credit rating/scoring alone is definitely insufficient to meet the expectations of modern credit analysis in this digital era. The shortcomings are evident in at least two aspects – (1) dynamic credit portfolio perspective of institutional lending, and (2) heterogeneous recovery rates across obligors and/or instrument types. This project addresses the former through developing new big-data deep credit analytics constructed for dynamic credit environments and designed for consistent aggregation of individual credit exposures into a portfolio perspective that can evolve in a point-in-time manner for various application horizons of interest.
Project Title: Credit Recovery Efficiency Analytics
Principal Investigator: Associate Professor Ying Chen
Summary: This upstream research project, "Smart Credit Analytics," aims to develop a new methodology for modeling recovery rates for various debt obligations using modern big-data analytics. Current industry practices often rely on simplified assumptions or discretionary judgment, such as fixed or uniformly distributed recovery rates. This project addresses the industry's recognized deficiencies by focusing on two key challenges: the scarcity of recovery rate observations and the need for non-standard distributional modeling for bimodal recovery rates. The research involves extending modern variable selection techniques to develop a scientifically rooted and robust recovery rate prediction system.
Supply Chain Finance in Agriculture
Project Title: Supply Chain Finance in Agriculture
Principal Investigator: Associate Professor Johan Sulaeman
Summary: The project focuses on addressing barriers in providing digital financing services to smallholder agribusinesses. Firstly, it aims to identify and document these barriers among all relevant actors. Secondly, it seeks to create a repository of scientifically valid surveys to assess the readiness of different stakeholders to engage in digital finance. Thirdly, it aims to conduct extensive data collection across Southeast Asian countries to gauge the readiness of various actors for digital finance. Lastly, to publish and maintain a benchmarking platform online, providing access to survey materials, raw data, and insights for researchers and financial institutions to utilize in their own research or business endeavors.
Digital Privacy and Its Impact on Fintech Lending and Borrower Behavior
Project Title: Unveiling the Implications: Investigating the Impact of Digital Private Information Exposure on Fintech Lending and Borrower Behaviors
Principal Investigator: Professor Sumit Agarwal
Summary: The emergence of online intermediaries and Fintech companies has revolutionized lending through technological innovation. The ascent of Fintech has brought forth renewed prospects for enhancing efficiency, but it has also raised fresh concerns about shifting systemic risk to less-regulated and more-fragile financial intermediaries. To gain a comprehensive understanding of the impact of Fintech lending on the financial market, our research intends to investigate the impact of digital private information exposure on Fintech lending and borrower behaviors by analyzing a customer private data regulation shock in Aug 2019.
In August 2019, the Google Developer Policy Center implemented a new privacy regulation, stipulating that apps without default SMS, phone, or assistant handler capability could no longer collect call log and SMS information of customers. Consequently, our researching Fintech lending firm, CASHe has been unable to access this personal information since the regulation came into effect. After the shock, CASHe started to collect borrower information by asking for five contacts and their relationship to call in case of default.