📢Day 7/100: Tackling Data Availability in Ethiopia

Building a credit scoring model in Ethiopia has challenges, especially regarding data. 📊

Key hurdles I’m exploring:

1️⃣ Data scarcity: Formal credit histories are rare, but eCommerce and mobile money data offer untapped potential.

2️⃣ Local partnerships: Collaborating with fintechs to access anonymized transaction data.

3️⃣ Privacy compliance: Ensuring data protection laws are adhered to while innovating responsibly.

💡 Question of the day: Are there alternative sources of financial data that have worked well in other emerging markets?

#DataChallenges #Ethiopia #FintechInnovation #AlternativeData #PrivacyByDesign

📢Day 10/100: Class Imbalance Challenges
Class imbalance is a persistent issue in fraud detection and credit scoring. 🚨

In my dataset:

Fraudulent transactions are rare (<5%), making prediction tricky.
Techniques like SMOTE (Synthetic Minority Oversampling Technique) helped balance the dataset.
💡 Key Insight: Balancing the data improved model precision for rare classes like fraud detection.

💡 Question: What other methods do you use to address class imbalance without oversampling?

#DataChallenges #FraudDetection #CreditScoring #FintechInnovation