AdvancedBusiness trackCA-CP-29

Interac e-Transfer Fraud Detection Model

Internship role: Data Science Intern (Banking). Detect scam-driven transfers in a synthetic dataset with class imbalance, explain decisions with SHAP and estimate losses prevented at a given false-positive budget.

10 Guided Steps240h Internship TierSubmit: GitHub repo or evidence pack + live demo / report + README + milestone logbookVerified CertificateAI Rubric Graded

Program Overview

What you submit

GitHub repo or evidence pack + live demo / report + README + milestone logbook

Stack: Python, imbalanced learning, SHAP, synthetic transactions

How it is graded

Signals: Milestone gates met; measurable result reported; documentation; deployment or evidence reachable; reflective log

Functionality & requirements30
Technical quality & architecture25
Documentation15
Deployment & evidence15
Innovation10
Presentation5

Pass 60% · Merit 75% · Distinction 90%

Learning Outcomes

240-hour Canada programme. Canadian banks face rising authorised-push-payment fraud and hire analytics interns through co-op.

Target Audience

Engineering Students

Diploma, B.Tech, B.E., BCA, MCA, or M.Tech students seeking practical experience.

Aspiring Developers

Developers who want to build proof-of-work capstone projects for tech resumes.

Job Seekers

Graduates looking for AI-evaluated code review & verified industry credentials.