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📢Day 14/100: Next Steps for the Credit Scoring Model With the prototype complete, here’s what’s next: 1️⃣ Testing with real-world data: Partnering with fintechs to validate the model. 2️⃣ Incorporating mobile money data: Adding another dimension to the scoring process. 3️⃣ Monitoring and retraining: Ensuring the model stays relevant as new data comes in. 💡 Takeaway: A successful model is never truly done—it evolves with the market. 💡 Question: What’s your approach to maintaining machine learning models in production? #CreditScoring #MachineLearning #FintechEthiopia #AI

📢Day 13/100: Real-World Prototype Deployment The prototype for my credit scoring model is live! 🚀 Features: 1️⃣ Web dashboard: Enter customer details and get real-time risk classifications. 2️⃣ API integration: Seamless communication between the frontend and back end. 3️⃣ Explainable results: Each score is accompanied by a breakdown of contributing factors. 💡 Takeaway: Deploying a functional prototype provides valuable feedback for real-world usability. 💡 Question: How do you ensure user-friendly designs for fintech tools in emerging markets? #Prototype #AI #FintechEthiopia #CreditScoring

Build a self-evolving Genetic Algorithm https://youtu.be/9M4ETVngWy4

📢Day 12/100: Comparing Machine Learning Models Today, I compared the performance of multiple machine learning models for credit scoring: 1️⃣ Logistic Regression: Simple and interpretable but less effective with complex data. 2️⃣ Random Forest: Excellent for feature importance but slower for large datasets. 3️⃣ Gradient Boosting: Best overall performance with high accuracy and recall. 💡 Finding: Gradient Boosting stood out with an ROC-AUC of 0.97. 💡 Question: Do you prioritize interpretability or accuracy when selecting a model for financial applications? #MachineLearning #ModelSelection #CreditScoring #FintechEthiopia

📢Day 11/100: Integrating AI and ML in Credit Scoring AI and machine learning are at the heart of my credit scoring model, but they require careful application. 🤖 Today’s focus: 1️⃣ Modeling approaches: Exploring supervised learning techniques like Gradient Boosting for risk prediction. 2️⃣ Bias mitigation: Addressing imbalances in transactional data to ensure fair outcomes. 3️⃣ Explainability: Building a model that’s transparent and interpretable to meet regulatory standards. 💡 Coming soon: Detailed performance metrics and insights from my initial experiments with AI-powered credit scoring! #AI #MachineLearning #CreditScoring #ExplainableAI #FintechEthiopia

📢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

📢Day 9/100: Feature Engineering Deep Dive Feature engineering is where raw data turns into actionable insights! 🛠 In my credit scoring project, key features include: 1️⃣ Recency, Frequency, Monetary (RFM): Critical for understanding customer behavior. 2️⃣ Fraud indicators: High-value transactions flagged based on outlier analysis. 3️⃣ Categorical encodings: Using Weight of Evidence (WoE) to transform qualitative data like product categories. 💡 Takeaway: Good features are the foundation of any successful model. They ensure the patterns we observe are meaningful and actionable. 💡 Discussion point: What’s your go-to method for handling highly skewed data in financial datasets? #FeatureEngineering #DataScience #CreditScoring #FintechEthiopia

📢Day 8/100: Sketching My Credit Scoring Workflow Today, I outlined the workflow for my credit scoring model. Here’s what it looks like: 1️⃣ Data collection: Leveraging transaction histories, behavioral metrics, and alternative data sources. 2️⃣ Feature engineering: Creating features like transaction recency, frequency, and value tailored to BNPL behavior. 3️⃣ Model selection: Comparing Gradient Boosting, Random Forest, and Logistic Regression. 4️⃣ Evaluation: Balancing precision, recall, and ROC-AUC to ensure the model is reliable in the Ethiopian context. 💡 Tips needed: What’s your go-to feature engineering strategy for financial datasets? #AI #CreditScoring #ModelDevelopment #BNPL #DataScienceEthiopia

📢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

I am excited to share with you the Python Programming for Beginners roadmap Basic Python Programming: https://youtu.be/ISv6XIl1hn0 Data Structures with Projects full tutorial for beginners https://www.youtube.com/watch?v=lbdKQI8Jsok OOP in Python - beginners Crash Course https://www.youtube.com/watch?v=I7z6i1QTdsw Join #epythonlab https://t.me/epythonlab Join https://t.me/epythonlab for more learning resources

📢Day 6/100: BNPL Risks in Emerging Markets While Buy-Now-Pay-Later (BNPL) services are revolutionizing access to credit, they come with risks—particularly in emerging markets like Ethiopia. ⚠️ Key risks I’m addressing in my project: 1️⃣ Credit risk: Developing a robust scoring system to predict default probabilities. 2️⃣ Behavioral risk: Educating users to avoid overspending, especially first-time borrowers. 3️⃣ Operational challenges: Adapting BNPL models to Ethiopia’s infrastructure and regulatory environment. 💡 Discussion point: How can BNPL providers balance convenience with responsible lending practices? #RiskManagement #BNPL #CreditScoring #FinancialInclusion #EthiopiaFintech

📢Day 5/100: Understanding Ethiopian Fintech Ethiopia's fintech ecosystem is a mix of challenges and opportunities. 📈🌍 From low formal banking penetration to an increasingly digital population, it’s clear that innovation in financial services is critical. Key insights from my research today: 1️⃣ Low banking penetration but high mobile adoption: Over 75% of transactions are cash-based, yet mobile payment systems like Telebirr are gaining traction. 2️⃣ Regulatory frameworks: Ethiopia’s regulatory approach emphasizes financial inclusion but poses innovation challenges, especially for BNPL services. 3️⃣ Unique consumer behaviors: Ethiopians' dominance of informal financial systems and cash reliance shape their engagement with digital financial services. 💡 Question of the day: How can fintech drive financial literacy in Ethiopia to accelerate digital adoption? #FintechAfrica #Ethiopia #BNPL #FinancialLiteracy #DigitalTransformation

📢 Day 4/100: The Role of Data in Credit Scoring Data is the fuel for any credit scoring engine. 🔍 However, in Ethiopia, traditional credit data is scarce. Today, I'll dive into: Types of alternative data (e.g., mobile money, e-commerce behavior). Ethical challenges in data collection. I plan to build a framework that respects privacy while being effective. #DataDriven #CreditScoring #AlternativeData #Fintech #EthicalAI #Ethiopia

📢 Day 3: Using RFM Scoring to Classify Customer Risk 📊 Before diving into machine learning or deep learning models, I started with a simpler yet powerful approach: RFM (Recency, Frequency, Monetary) scoring to classify customers into high-risk or low-risk groups. Why RFM? In the context of Ethiopian BNPL services, where traditional credit histories are scarce, RFM provides a practical starting point by analyzing customer behavior: 1️⃣ Recency: How recently did the customer make a purchase? 2️⃣ Frequency: How often do they shop? 3️⃣ Monetary: How much do they spend? This method helped me: ✅ Identify behavioral patterns to differentiate reliable customers from risky ones. ✅ Create a foundation for more advanced models ✅ Address data scarcity by leveraging transactional and engagement data. RFM scoring is simple and interpretable, making it easier to communicate results to stakeholders early on.Next, I’ll integrate these insights into machine learning models to refine predictions and enhance scalability. 🚀

What’s Next After AI? The Emerging Frontiers of Technology https://medium.com/@epythonlab/whats-next-after-ai-the-emerging-frontiers-of-technology-822c73b9c7c9

📢 Day 2/100: Why BNPL in Ethiopia? BNPL services have huge potential in Ethiopia, a country with growing digital adoption but limited formal credit access. 🛒💳 Key points I'll focus on: 1️⃣ Addressing financial inclusion. 2️⃣ Navigating unique local challenges (e.g., data availability). 3️⃣ Leveraging tech for scalability. What do you think are the biggest opportunities for BNPL in emerging markets? #BNPL #EmergingMarkets #FintechInnovation #DigitalFinance #Ethiopia #FinancialInclusion

Day 1: Introduction to the Challenge 📢 Day 1/100: The Journey Begins! I'm embarking on a 100-day challenge to share insights, progress, and lessons learned as I build a data-driven credit scoring model tailored for Buy-Now-Pay-Later (BNPL) services in Ethiopia's fintech space. 🚀 Why this topic? BNPL is reshaping financial inclusion, and robust credit scoring is the backbone of sustainable lending. Follow along as I explore data, algorithms, and strategies to make this happen! hashtag#Fintech hashtag#DataScience hashtag#CreditScoring hashtag#BNPL hashtag#FinancialInclusion hashtag#Ethiopia hashtag#100DaysChallenge

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