Scrappy Number Two - Automating Loan Approval Processes

[JJonkman]

Team Name	Scrappy Number 2
Track	Banking
Use Case	300+ Retail Branches and 80+ Central Loan Approval Officers work together to underwrite loans being issued to consumers using various requested documents to prove ID, income, residence, etc. These data points support the questions asked on our loan application, which can reach 30 different inquiries. We aim to use a combination of contact data quality processes, OCR and open source capabilities, rapid model of banking transactions, and modeling of verified data allow us to automate or vastly improve the application process via developing services in Intelligent Decisioning.
Technology	SAS ID, Python
Region	Canada
Team Members	Elmar Taghizade - Credit Risk and Analytics Nada El Moghany - Credit Risk and Analytics Dorrian Khouri - Construction and Design, PM Namrata Ail - Operations, Fraud Quality and Loan Contracts Katelyn Paterson - Central Loan Approval/ Underwriting Alicia Liberty - Central Loan Approval/ Underwriting Surya Surya - Credit Risk and Analytics Guilherme Ferreira - Insight Analytics John Jonkman - Credit Risk and Analytics
Is your team interested in participating in an interview?	Yes - happy to, subject to internal approvals
Optional: Expand on your technology expertise	10+ Years in SAS 9.4 3 Years in SAS 3.5 Good understanding of databases and networks Enough Python to be considered cute