What if data upskilling could help investment bankers better identify high-salary, low-risk clients?
Large Retail and Investment Bank
Banking and Finance
A retail and investment bank with more than 30,000 employees had previously setup an in-house “quants academy” facilitated by its own subject matter experts, but it could not deliver all the skills discovered in a gap analysis. Essentially, there were not sufficient skills in-house to deliver more advanced data science training.
At the time, the bank’s training consisted of staff teaching themselves ad-hoc on Udemy and EdX. They sought a more formal, thought-through solution for developing these critical skills in-house.
The project consisted of upskilling dozens of internal staff in data-related skills over a 9-month period. Apart from the coursework, which is hands-on and broadly practical from the very first week, delegates worked towards a number of capstone projects.
Capstone projects included, and involved delegates building and deploying solutions in an App Fraud Model, a Twitter Retrenchment Sentiment NLP model and Fake News Detector, a Payment Plan Classifier, a Physical Valuation Classification, and an Auto-Approve ML Model. Additional deliverables spanned an Attrition Forecasting Engine for people analytics, a Restructures PD model for PD, an Impairments ML model, an RPC model, and a Competitor Take-up Propensity Model.
Upon the upskilling project’s completion, the large retail and investment bank had: