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CSBS 2023 Annual Data Analytics Competition
 

Prompt

What will be the potential impact on banks (earnings, deposits, asset quality, etc.) from the recent rapidly rising interest rate environment? And how should banks prepare?

This competition is open to teams of 3-5 undergraduate and/or graduate students working with a faculty advisor.

Background

Banks of all sizes and complexities must be able to manage their balance-sheet risks. At the most basic level, a bank should measure its interest rate risk exposure to understand the potential impact of significant interest rate movements on the bank’s earnings, asset quality, liquidity, and capital. In 2022, the Federal Reserve started to significantly raise its target on the Fed Funds Rate, which has had a big impact on most market-driven interest rates. 

Known as asset/liability management (ALM), a bank’s potential short-term and long-term earnings must be balanced with adequate liquidity and capital. Bankers use ALM models to help them understand the potential impact from various risks (e.g., interest rate risk, liquidity risk, credit risk, market risk). 

See the appendices for more details.

Assignment

While ALM models are specific for individual banks (because it is important to know the composition of its loan portfolio and mix of securities), what can be learned about bank risks from the Call Report data (FDIC and FFIEC) and any other publicly available data sources? Are there differences between banks of different sizes? Are there differences between banks operating in different locations or lending environments? Are there differences between banks with different asset structures or charters?

What will be the potential impact on banks (earnings, deposits, asset quality, etc.) from the recent rapidly rising interest rate environment? And how should banks prepare?

CSBS invites small teams of three to five undergraduate and graduate students at U.S. universities (working with a faculty advisor) to help scholars, policymakers, and the public better understand the potential impact on banks (earnings, deposits, asset quality, etc.) from the recent rapidly rising interest rate environment, and to comment on how banks of different sizes and complexities should prepare.

Using bank Call Report data (FDIC and FFIEC) and any other publicly available data sources, teams should develop a hypothesis about the impact banks will have from a rapidly rising interest rate environment, create a data analytics model to test their hypothesis, and draw data-driven insights from their research.  

For selected proposals (see competition phases below), the final research project should consist of a written report that includes at least one of the following supporting data analytics components: 

  1. Statistical model 
  2. Optimization model 
  3. Business Intelligence tool/dashboard 
  4. Other data analytics component 

 

Judging

The winning teams will be selected by a panel of judges, with an emphasis on the following (see Appendix 1 for the scoring rubric): 

  1. Use of data and analytical methods/models 
  2. Creativity and innovative thinking 
  3. Persuasion through data-driven arguments 

 

Competition Phases 

  • Phase I – Proposal Submission: Teams should submit a detailed project proposal by January 29, 2023, for eligibility to advance to Phase II. A proposal template is included for your reference in Appendix 2. Limit your proposal to two pages. Proposals must have a faculty sponsor. Send proposals to: [email protected]  
  • Phase II - Proposals Reviewed by CSBS: Up to eight teams will be selected to advance to Phase III. Selected teams will be notified in early February to submit final reports by mid-April. Proposals will be evaluated on your research idea (hypotheses), expected methodology and model(s), and presumed insights from your analyses. 
  • Phase III - Eight Teams Work on Competition: The eight selected teams must submit their written research reports and data analytics components by April 23, 2023. Each team should email their reports/components to [email protected]
  • Phase IV – Four Finalist Teams Make Oral Presentations: Four (finalist) teams from Phase III will make oral presentations (20 minutes, plus 10 minutes Q&A) to a panel of CSBS judges on May 4, 2023, to determine first through fourth place winners. (The winning team may also be invited to present their findings at the annual CSBS State and Federal Supervisory Forum in late May.) 

 

Eligible Participants and Prizes

The CSBS Data Analytics Competition is open to all undergraduate and graduate students at U.S. universities. Teams of three to five college students are required to be sponsored by a research faculty advisor at their university. Prize money will be awarded to the top four finalists as follows: $5,000 to the first-place team; $2,500 to the second-place team; $1,500 to the third-place Team; $1,000 to the fourth-place Team. 

 

Suggested Data Sources

 


2022 Competition Archive

Final Presentations & Winners

Overview

A three-member team of students from the William & Mary won the CSBS 2022 Data Analytics Competition. This year’s competition challenged students to develop a data analytics model that demonstrates the role community banks played during the pandemic using data from the Paycheck Protection Program (PPP).

“We started the CSBS Data Analytics Competition in 2021 to give college students an opportunity to explore data analytics solutions to real-world banking questions, and to provide CSBS and its members with creative ideas and valuable solutions to interesting and important bank-related data analytics questions,” said Emil Phillips, CSBS Senior Vice President over Research and Analytics.

CSBS selected William & Mary students Gio DeFrank, Junghee Mun, and Kristina Posner as winners of the CSBS 2022 Data Analytics Competition. Led by faculty advisor Dr. Joseph Wilck, the William & Mary team developed and tested the following hypothesis: Were community banks who partnered with financial technology (fintech) firms more successful in distributing PPP loans to small businesses with racial minorities and underbanked populations? 

To complement PPP loan data from the Small Business Administration’s database that CSBS matched to most U.S. commercial banks, the William & Mary team added the FDIC’s community bank financial data and information from the FDIC’s annual “How America Banks” survey. But because of many missing observations, the team created a classification neural network model to predict the probability of a business being minority owned.

Once the team finished scrubbing and cleaning the data, they used logistic regression models and random forest classification models to test their hypothesis. The team found that fintech partnerships with community banks was not a factor in predicting whether a PPP borrower was a minority or lived in a low or moderate income (LMI) area and did not significantly contribute to boosting minority or LMI outreach. The team concluded that fintech partnerships could help better diversify the banking ecosystem and encouraged further research in this area and for better data collection to help determine outcomes.

“We have all been extremely impressed with the creativity and rigorous technical skills from all the students involved in the CSBS 2022 Data Analytics Competition,” said Tom Siems, CSBS chief economist and creator of the competition. He added, “Indeed, the Data Analytics Competition is one of the ways we engage with the academic community as it effectively combines both research on banking with data analytics, and helps us be part of building the workforce of tomorrow. We hope all participants are inspired to continue their academic studies in economics, finance, data science, accounting, or other fields that offer a valuable perspective on community banking and financial sector supervision.”

The other finalists in the CSBS 2022 Data Analytics Competition include teams of students from the University of California at Irvine (second place), Carnegie Mellon University (third place), and Southern Methodist University (fourth place).

The University of California at Irvine team was led by faculty advisor Dr. Gary Richardson. The five-member team included Abhishek Karimpuzha Devarajan, Qi Wang, Qingqing Yu, Wanjing Xu, and Yixuan Cai.  The team created several multiple regression models and concluded that PPP lending via community banks appears to have reduced county-level unemployment during the pandemic more than PPP lending via other institutions, particularly larger banks. They found that $1 billion in PPP lending via community banks reduced unemployment by about 0.6%, whereas $1 billion in PPP lending via other institutions reduced unemployment by about 0.1%.

The three-student Carnegie Mellon University team was led by Dr. Gabriela Gongora-Svartzman and comprised of students Thomas Yu Chow Tam, Shun Tomita, and Jamie (Jeong Yeon) Lim. Using multivariate regression models, the team concluded that community banks seem to have helped local communities and local employees mainly in counties in the Midwest and Great Plains states, and their results were inconclusive regarding their hypothesis that communities with higher proportions of community bank-originated PPP loans have lower bankruptcy rates.

The three-student SMU team was led by faculty advisor Dr. Eli Olinick. Melissa Serrano, Annalise Sumpon, and Madi Tedrow used regression and statistical models that found statistically significant differences in the business type and business ownership (e.g., racial minority or gender) for businesses that received PPP loans from community banks and/or non-community banks.

As the first-place winners of the 2022 CSBS Data Analytics Competition, the William & Mary team will collect $5,000. The second-place team from the University of California at Irvine will collect $2,500, while the third-place team from Carnegie Mellon got $1,500 and the fourth finalist team from SMU got $1,000.
CSBS’ Data Analytics Task Force provides oversight and direction for the annual CSBS Data Analytics Competitions. 

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