SCIENTIFIC PROGRAMS AND ACTIVITIES

August 29, 2015

THE FIELDS INSTITUTE FOR RESEARCH IN MATHEMATICAL SCIENCES

May 20-21, 2015

Workshop on Big Data in Commercial and Retail Banking
at the Fields Institute, Toronto

 

Organizing Committee:

Matt Davison, Western University
Adam Metzler, Wilfrid Laurier University
Mark Reesor, Western University

Registration has closed.

We regret that we can not accept any on-site registrations as the workshop has reached capacity.

 

Executive Summary

Modern banks such as the Canadian "Big Six" have been "power users" of modern methods in mathematics, statistics, and computing in their financial markets arms for many decades now to the point where at least Master's level training in a quantitative discipline has become essential for many working in trading and risk management. But perhaps less known is the trend towards highly quantitative work in the less glamorous but highly profitable commercial and retail banking arms of banks.

Commercial bankers must decide to whom to lend, in what amount, and on what terms, both quickly and accurately. Not everyone gets a mortgage from a bank, and of those who do, not everyone pays the same mortgage rate. These decisions are made with a tap of a few keys in a retail banker's office. How are these decisions made? With Big Data and the big analytics that go with it.

For example, every time you use your credit card, the bank records when, where, how much, and to whom you are paying. Some of us have had the experience of getting a call from a bank, concerned that our recent use of a card in an unaccustomed way or in a remote place is evidence that it has been stolen. What is less known is the use to which this huge set of credit card data can be used in finding patterns that suggest that someone's spending is calling into question their ability to repay their loans.

Even the marketing group of the bank's credit card division gets into the action. Many consumers hit the mall armed with a small arsenal of credit cards. What few of us know is the degree of concern that banks devote to keeping the card they have issued "top of wallet": the first card you reach for at the point of sale. Banks can assemble, query, and analyze the Big Data in their databases and use it to decide both how to shed unprofitable but also how to retain profitable customers, by tying it with various marketing techniques.

 

 

Program Outline

This 2-day program will consist of a 1.5-day workshop on data analytics in banking, capped off by a 1-hour research talk. The workshop leader is Professor Cristián Bravo, Department of Industrial Engineering, Universidad de Talca, Chile. The workshop deals with modern credit scoring and credit risk techniques and includes coverage of probability of default (PD), loss given default (LGD) and exposure at default (EAD). Case studies may include social network analysis for credit card fraud, micro-credit, and an application of semi-supervised learning. Participants in the workshop should have a basic knowledge of data mining and an understanding of how to create and use statistical models. Previous knowledge of predictive analytics used in this application area is not required. A 1-hour research talk entitled "State-Dependent Correlations and PD-LGD Correlation" given by Dr. Adam Metzler, Department of Mathematics, Wilfrid Laurier University, will conclude the program.

 

 

Online Registration

Online registration has now closed. We regret that we can not accept any on-site registrations as the workshop has reached capacity.

Workshop fees: $200 regular rate, $150 faculty rate, $75 student and postdoctoral fellow rate.

These fees include workshop participation, lunch and break catering both days, and reception on Wednesday evening (cash bar).

Funding support: Graduate Students and Post-doctoral Fellows interested in being considered for a travel award should select and complete the "funding application form" option when registering. Awards will be given post-workshop.

 

 

Workshop Leader

Cristián Bravo is Instructor Professor at the Universidad de Talca, Chile. He is an Industrial Engineer, has a Master in Operations Research and a PhD in Engineering Systems from the University of Chile. He previously served as the Research Director of the Finance Center at the Department of Industrial Engineering, Universidad de Chile, and worked as a Research Fellow at KU Leuven, Belgium.

He has been published in several Data Mining and Operations Research journals, and edited a special issue at Intelligent Data Analysis (2014). His research interests cover Credit Risk, specially applied to micro, small, and medium enterprises, and the development and application of data mining and big data models in this area.

 

 

Schedule

Wednesday May 20

8:30-8:45 Welcome
8:45-10:30 Workshop
10:30-11:00 Coffee break
11:00-12:30 Workshop
12:30-1:50 Onsite lunch
1:50-3:30 Workshop
3:30-4:00 Coffee break
4:00-5:30 Workshop
5:30-6:30 Reception at Fields

Thursday May 21

8:30-10:30 Workshop
10:30-11:00 Coffee break
11:00-12:30 Workshop
12:30-1:50 Onsite lunch
1:50-2:50 Talk by Adam Metzler
2:50-3:00 Closing remarks


 

Talks

Adam Metzler, Wilfrid Laurier University

State-Dependent Correlations and PD-LGD Correlation - Modeling and Computation

It is an empirical fact that (i) correlations tend to rise during adverse economic scenarios and (ii) default rates are strongly positively correlated with loss-given-default. Unfortunately many of the most popular models used in risk management applications do not incorporate these phenomena, and the result is overly optimistic predictions. This talk will consist of two parts. In the first part we present an empirically motivated model that allows for state-dependent correlations in linear factor models and contains both the simple mixture and so-called Random Factor Loading models as special cases. We derive a number of tractable asymptotic approximations and illustrate that state-dependence in correlations effectively precludes moderate default rates, exacerbating both good times and bad. In the second part we discuss efficient Monte Carlo methods for computing risk measures in a PD-LGD correlation model developed by Miu and Ozdemir (2006).

Adam Metzler received his M.Math (2004) and Ph.D. (2008) from the Department of Statistics and Actuarial Science at the University of Waterloo. He was an assistant professor in the Department of Applied Mathematics at the University of Western Ontario from 2008-2012 before accepting his current position as assistant professor in the Mathematics Department at Wilfrid Laurier, where he is currently Co-ordinator of Financial Mathematics Programs. He maintains an active research program in quantitative finance, with an emphasis on problems related to credit risk.

 

 

 

 

 

 

 

 

 

 

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