CIBC Project: Fraud Analytics
Western’s Data Science program and the CIBC Fraud Analytics team are collaborating to provide Undergraduate students with the unique opportunity to work on a real Data Science problem for course credit. The opportunity is open to any undergraduate student enrolled in any of Western’s (or Affiliates) modular programs. The project is most suitable for advanced (3rd or 4th year) students. Basic expertise in data science and machine learning is required. Prior successful completion of Data Science 3000 (or similar course) is highly recommended.
The Project
The CIBC Fraud Analytics team is comprised of data professionals that use data, machine learning, and computer applications to develop algorithms to detect suspicious activity. The team uses data as a strategic asset to enable a deeper understanding of fraud threats, accelerate the development of fraud mitigation activities, and provide the foundation for effective and timely decision-making.
The problem you will be working on is worthless deposits and fraud. A deposit is made to a deposit account from an account where the check/funds does not exist or are fraudulent. Such deposits may be followed by other fraudulent transactions or account usage. In the project, you will be conducting detailed data mining and data analysis to mitigate this problem and provide meaningful insights and recommendations. The output of the project will be an executive-style presentation as well as supporting analytical details, computer algorithms, data analysis, and a detailed write-up.
This project provides an opportunity to apply your theoretical data and analytics knowledge and skills in a real-world, industry environment.
Core skills and technology/tool requirements to solve problems can consist of the following:
Analytical Thinking
Ability to creatively think about the challenge and develop well thought out solutions guided by data based analytical solutions.
Data Management
Data management/querying and computer programming/software expertise. Ability to work with analytical tools and applications that may include but not limited to SAS, SQL, Python, R, Tableau, and Alteryx.
Communication
Excellent verbal and writing skills to effectively articulate ideas and opinions that involve analysis, interpretation, and assessment.
Giving Meaning to Data
Enjoy investigating complex problems and making sense of information with the ability to communicate detailed information in an impactful way.
How to Apply
Applications need to be submitted by the end of July 28, 2023 by email to mgrkava@uwo.ca. Please submit all applications in a single pdf-file entitled <LastName>_<FirstName>
- A one-pager answering the following question: ‘How can data and analytics be used to mitigate worthless deposits and Fraud’? Please describe your thought processes within the one-pager and include:
- how you would initially explore the data to gain insights into the problem,
- what analytical tools/applications will you be utilizing to conduct the data mining and analysis, and
- how you would create an executive-style presentation and dashboard to present your results.
- A paragraph describing why CIBC’s Fraud Analytics team intrigues you.
- Your resume and unofficial transcript. Please ensure your Full Name, Student ID, Western email, and the degree modules you are enrolled in (and which year) are included.
Application Deadline
Upon Selection
If your application is shortlisted, you will be invited to participate in CIBC’s Dragon’s Den Challenge in August, where you are able to present your one-pager in front of CIBC leadership and clearly articulate what makes this initiative special to you. You will have 3-5 minutes for your presentation and Q&A.
Selected students will be notified following the Dragon’s Den Challenge, and will commence project work with CIBC from September 7 to April 30.
Note: If accepted to the project, you will be enrolled in a special section of the DS4999Z course (0.5 credits). If you are in Data Science, this course can count as your Honour specialization thesis (DS4999Z). If you are in Actuarial Science or Statistical Sciences, it can count as an additional course at the 4000 level towards your module requirement. If you are in Financial Modelling, it can count as your G/F/Z course. If you are in Computer Science, this course can count as a CS Selected Topics course (CS4433-4438 A/B/Y). Since your enrolment in this course will be finalized in September, it is wise to make your course selection for 2023-24 in a way that will provide you with the number and the type of credits that are appropriate for your program. Should you be selected for DS 4999Z, you would be able to drop a 0.5 course if your schedule becomes too busy.
Key Benefits
An opportunity to apply your theoretical data and analytics knowledge and skills in a real-world, industry environment - solving an actual business problem
A chance to work with peers in your own, or related, disciplines in a collaborative, team environment
A unique engagement and relationship opportunity with CIBC analytics employees for future talent opportunities
Development of your personal and professional skills including presentations, networking, data visualization tools, data cleaning capabilities, and more
Any Questions?
Please reach out to Mira Grkavac (mgrkava@uwo.ca) by sending an email with the subject line "CIBC Fraud analytics project."
