Evaluation of Artificial Intelligence and Machine Learning in Auto Finance

The INTER-INSTITUTIONAL DEVELOPMENT SCHEME (IIDS) Seminars are fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (RGC Ref. No.: UGC/IIDS15/B01/19

PPT

Where are we Headed? The Future of FinTech

Increasing Regulation of Platforms

Principles‐based compliance
Automated/remote compliance
ZeroBias/Fairness demonstration

Changing Customer Exectations and GenZ

Automated Instant Decisions (a la Kabbage)
- Fraud
- Credit
- Authorization
Personalization
- Smooth robotic interface
- Customized everything
Risk evaluation machines ubiquitous!

Data Assets are Central

Creation of “alternative databases”
- – i.e. all real estate transactions are recorded, but it
- requires some “sweat of the brow” to create clean
- and integrate noisy/incomplete historical data
  - Naming mismatches
  - Errors in translation of physical to electronic records
  - New players like SnowFlake are addressing this space
Big‐Tech making major inroads into these spaces via new kinds of data and better machine learning methods
Will they remain “tools providers” or eat the lunch of existing players?

Aug, 2020Jun, 2022

Blockchain and Smart Contracts

PPT

Baseline model
- Credit market competition for borrowers with private types
- Lenders (bank and ntech) with asymmetric screening technologies
Open banking and data sharing
- Borrowers can voluntarily share their own data resided in bank
- But what kind of data? Welfare implications?
- Endogenous credit quality inference: adverse selection as the backbone of credit market
Open banking: Credit information sharing
- Potentially perverse eect of open banking
Open banking: privacy and targeted loans
- Endogenous sign-up population and information externalities

Paper

Open banking facilitates data sharing consented by customers who generate the data, with a regulatory goal of promoting competition between traditional banks and challenger fintech entrants. Open banking could make the entire financial industry better off yet leave all borrowers worse off, even if borrowers could choose whether to share their data. The importance of equilibrium credit quality inference from borrowers’ endogenous sign-up decisions. When data sharing triggers privacy concerns by facilitating exploitative targeted loans, the equilibrium sign-up population can grow with the degree of privacy concerns.

I would like to express my heartfelt thanks to Prof HE to bring us the academic model. The model points out that open banking could make the entire financial industry better off yet leave all borrowers worse off, even if borrowers could choose whether to share their data. Since the degree of privacy concern is an important welfare issue. Perhaps, one possible way to relax this concern is to use BlockChain Technology and Smart Contracts. The blockchain network serves as a secure ledger of transactions and information sharing thus are remodelling the traditional banking environment

Aug, 2020May, 2022

FinTech Seminars (Zoom) in Oct 2020

I would like to express my heartfelt thanks to Prof Vasant DHAR and Prof Zhiguo HE for being the speaker for the IIDS FinTech seminar. There are over 150 registrations (including colleagues from oversea and local universities such as HKU, HKBU, HKCityU, HKLU, OUHK and UOWCHK) for the seminars. Their presentations are extremely informative, and the active participation illustrated the importance of the topic to our colleagues and students.

The INTER-INSTITUTIONAL DEVELOPMENT SCHEME (IIDS) Seminars are fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (RGC Ref. No.: UGC/IIDS15/B01/19

IIDS Zoom Seminar: Evaluation of Artificial Intelligence and Machine Learning in Finance	IIDS Zoom Seminar: BlockChain Technology and Smart Contracts
Time: 9:00 am to 10:15am	Time: 10:30am to 11:45am
Date: Wed, 7 OCT, 2020	Date: Wed, 7 OCT, 2020
Speaker: Prof Vasant DHAR Stern School of Business and the Center for Data Science at New York University	Speaker: *Prof Zhiguo HE* Fuji Bank and Heller Professor of Finance at the University of Chicago Booth School of Business

Venue for walk-in: RLG502 (Computer Room)

Schedule

Date: Wed, 7^th OCT 2020 (HK); Tue, 6^th OCT 2020 (New York, Chicago)

Welcome: 09:00 to 09:05 (HK); 21:00 to 21:05 (New York)

Presentation: 09:05 to 10:00 (HK); 21:05 to 22:00 (New York)

Prof Vasant DHAR

Q&A: 10:00 to 10:15 (HK); 22:00 to 22:15 (New York)

Break: 10:15 to 10:30 (HK)

Welcome: 10:30 to 10:35(HK); 21:30 to 21:35 (Chicago)

Presentation: 10:35 to 11:30 (HK); 21:35 to 22:30 (Chicago)

Prof Zhiguo HE

Q&A: 11:30 to 11:45(HK); 22:30 to 22:45(Chicago)

END: 11:45(HK)

Aug, 2020Aug, 2020

Honourable Speakers

I would like to express my heartfelt thanks to Prof Vasant DHAR (the Stern School of Business and the Center for Data Science at New York University) and Prof Zhiguo HE ( Fuji Bank and Heller Professor of Finance at the University of Chicago Booth School of Business, the Director of Becker Friedman Institute-China and Co-Director of the Fama-Miller Center) for accepting our invitation to be the speaker of the IIDs FinTech Seminars.

Prof Vasant DHAR
Stern School of Business and the Center for Data Science at New York University

Prof Zhiguo HE
Fuji Bank and Heller Professor of Finance at the University of Chicago Booth School of Business

Aug, 2020Oct, 2020

Prof DHAR Vasant

Prof Vasant Dhar is a professor at the Stern School of Business and the Center for Data Science at New York University where he is the Director of the PhD program. He is also the founder of SCT Capital Management, one of the first systematic machine-learning-based hedge funds, with a track record of over 20 years.

Dhar’s research answers two related questions: (1) when should we trust AI machines that learn from data, and (2) how should we design machines in different domains to be sufficiently trustworthy for decision making?

His research has addressed these questions in a number of areas, most notably, in financial markets.Dhar has authored over 100 research papers, as well as articles for publications such as the Financial Times, Wall Street Journal, Forbes, Wired, and the Harvard Business Review. He has appeared on CNBC, Bloomberg TV, National Public Radio and other media.

Jul, 2020Aug, 2020

FinTech

Open banking is expected to be one of the most important FinTech development and is going to remodel the traditional banking and business environment. Facing the wave of the FinTech revolution, it is important to understand how the decentralization of blockchain technology improves and reshapes the business model with smart contracts to handle the traditional issue of information asymmetry. To enhance the financial inclusion of online and mobile payment service, banks are encouraged ( or required) to share data with third parties through an application programming interface (API). This concept of sharing of data upon different actors in the banking sector through blockchain network is often referred as an important part of Open banking. To catch up with the FinTech development, it is important that we understand the mechanism that customers manipulate their business in multiple places with multiple sources using open banking service.

Artificial intelligence (AI) and machine learning are widely used to handle massive amounts of data in financial markets. In recent years, AI and machine learning has played a significant role in the FinTech landscape, such as in auto loans, auto financial evaluation, auto financial risk analysis, auto financial information exchange, and auto investment. As AI and machine learning are making more and more automatic financial decisions (for example, programme trading), there has been increasing discussion on the role of human judgment . To catch up with the FinTech development, it is important that we have the knowledge to evaluate the financial predictions of artificial intelligence and machine learning so that humans can control the rapid advances in FinTech.

The seminars will provide participants with the knowledge of open banking and the financial predictions of artificial intelligence and machine learning so that we can catch up with the rapid advances FinTech.

Jul, 2020Aug, 2020

Professor Zhiguo HE

Booth’s Zhiguo He, September 6, 2016. (Photo by Jean Lachat)

Prof Zhiguo HE is a Chinese financial economist serving as the Fuji Bank and Heller Professor of Finance at the University of Chicago Booth School of Business, where he has taught since 2008. He serves as the Director of Becker Friedman Institute-China and Co-Director of the Fama-Miller Center. He is also a research associate at the National Bureau of Economic Research, member of the academic committee at the Luohan Academy, and special-term Alibaba Foundation Professor of Finance at Tsinghua University. He earned his Ph.D. from the Kellogg School of Management at Northwestern University.

His work has been published in leading academic journals, including American Economic Review, Econometrica, Review of Economic Studies, Journal of Finance, Review of Financial Studies, and Journal of Financial Economics. He is serving as Associate Editor for both the Journal of Finance and the Review of Financial Studies. He has been named a 2014 Alfred P. Sloan Research Fellow and has won numerous awards for his outstanding scholastic record, including the Lehman Brothers Fellowship for Research Excellence in Finance in 2007, the Swiss Finance Institute Outstanding Paper Award in 2012, the Smith-Breeden First Prize in 2012 and the Brattle Group First Prize in 2014.