WELLS FARGO Campus Analytics Challenge
- Deadline to Submit: 13 October 2021
- Virtual Challenge
Campus Analytics Challenge with Wells Fargo: Students can compete individually (preferred) or submit as a team for a chance to win a significant cash prize as well as develop skills around a real-world application.
DEADLINE TO SUBMIT: 13 October 2021 at 12:00 pm EST
At Wells Fargo, our data scientists play a key role in driving innovative and meaningful insights that enable our lines of business to provide a world-class experience to our stakeholders. The Campus Analytics Challenge 2021 (“Challenge”) puts you in the role of a data scientist and calls you to develop a machine learning model. The dataset is small enough that you should be able to work with it on a standard laptop. To help get your creative juices flowing, we encourage you to explore machine learning research literature and beyond, as you may find a creative approach in other sub-fields of machine learning.
Challenge Background: Banks are required to report suspected vulnerable (elder and dependent adult) financial exploitation. Today, much of this activity is limited to human interaction (bankers working with customers on the phone or in person), through which bankers may pick up queues, or red flags or customers self-reporting scams or financial abuse to their financial institution. Digital payments have a degree of reported fraud and claims, with the assumption that much more unreported losses occur, especially perpetrated against older adults (60 years of age or older).
As digital payments continue to expand across all demographics, research shows that older adults are showing the biggest uptick in adoption during the 2020/21 period due to the pandemic. Currently, digital payment data is not analyzed specifically under the vulnerable (elder and dependent adult) financial exploitation lens. Banks are required to report elder financial abuse but, unless a customer reports fraud and files a claim, financial abuse can go undetected and repeated fraud via digital payments can continue without the banks’ knowledge. Without detection models, a large amount of fraud is left unreported by consumers and elder and vulnerable adult populations will be at greater risk of being targeted and losing savings to fraudulent payments.
Banks need better methods to help protect elder and vulnerable adults against fraud in the digital payments landscape. Predictive modeling may also be applied in some form to alert consumers and bankers in advance of a fraud attempt and potentially pre-empt certain transactions and monetary losses. As the older adult segment continues to adopt digital technology, including digital payments, banks need better ways to predict and analyze transaction data to detect high risk payment patterns or transaction attributes that signal high risk for fraud, especially for older and vulnerable adult customers, which could be targeted by scammers.
Challenge Objective: The Challenge is for you to develop a machine learning model to predict suspected elder fraud in the digital payments space as described in the rules document on the website.
This challenge will likely be best suited for those pursuing degrees in computer science, information science, data analytics, mathematics, or engineering. The challenge is open to undergrads, masters, and Ph.D. programs.
Please note: To be eligible to receive any prize, potential winners must have a valid U.S. tax identification number and meet all the eligibility requirements at the time the prize is awarded.