Not provided
Participation Type
Individual
Max Talent Participants
Unlimited
Organiser
Beehive
Location
Online
Registration Closes
Sep 7, 2026
Submission Deadline
Sep 9, 2026
Status
Concluded
Job Opportunity
No
Description
Analyze Transaction Patterns to Uncover Fraud or Usage Insights.
Teams will analyze a dataset of anonymized financial transactions to:
- Detect fraudulent activity (suspicious or abnormal transactions).
- Extract usage insights that inform customer segmentation, risk management, and engagement strategies.
- The challenge simulates a real-world financial data analytics task where precision, storytelling, and business impact matter as much as technical performance.
General Track
Total Cash Prize
₦0
🥇
1st place
₦0
🥈
2nd place
₦0
🥉
3rd place
₦0
Timeline
Registration Opens
Sep 2, 2026. 12:00 [UTC]
Registration Closes
Sep 7, 2026. 12:00 [UTC]
Submission Deadline
Sep 9, 2026. 12:00 [UTC]
Winners Announced
Sep 22, 2026. 12:00 [UTC]
This hackathon has concluded.
Rules & Eligibility
- Transaction ID
- Timestamp
- Amount
- Merchant ID / Category
- Payment Method
- Customer ID (anonymized)
- Location (if available)
- Label (Fraud/Non-Fraud)
Submission Requirements
- Python / R (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch optional)
- SQL for querying and aggregation
- Visualization: Matplotlib, Seaborn, Power BI, Tableau, or equivalent
- Collaboration: GitHub / GitLab
Judging Criteria
- Review the dataset and challenge statement.
- Identify possible fraud patterns and behavior hypotheses.