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

📢 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 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

💰 Machine Learning is Reshaping Fintech — and we're just getting started.
FinTech ML Labs: https://www.youtube.com/playlist?list=PL0nX4ZoMtjYFuTnUcwv0aFnxN9pEyjVez

Two of the most mission-critical areas where ML is making a real-world impact today are:

1. 🔎 Credit Scoring

Traditional credit scoring often overlooks those without a deep financial history. With ML:

We analyze alternative data (e.g., transaction patterns, mobile usage, utility payments)

Apply classification algorithms to predict creditworthiness

Enable inclusive lending for underbanked populations


✅ Outcome: More accurate risk assessment + financial inclusion.


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2. 🛡️ Fraud Detection

Fraudsters evolve fast. ML evolves faster.

We train models on millions of transactions, identifying subtle anomalies

Use a mix of real-time classification, unsupervised anomaly detection, and behavioral modeling

Continuously improve through feedback loops and active learning


🚨 ML helps flag suspicious activity before it turns into loss.


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🔧 Tech Stack: Python | Scikit-learn | XGBoost | SHAP | FastAPI | Streamlit | AWS

🔄 The future of fintech is predictive, not reactive.

If you’re building intelligent financial systems—whether it’s for lending, fraud prevention, or personalization—let’s connect and exchange notes. 🚀

#Fintech #MachineLearning #CreditScoring #FraudDetection #ArtificialIntelligence #DataScience #FinancialInclusion #ResponsibleAI #Python #MLinFinance

💰 Machine Learning is Reshaping Fintech — and we're just getting started.
FinTech ML Labs: https://www.youtube.com/playlist?list=PL0nX4ZoMtjYFuTnUcwv0aFnxN9pEyjVez

Two of the most mission-critical areas where ML is making a real-world impact today are:

1. 🔎 Credit Scoring

Traditional credit scoring often overlooks those without a deep financial history. With ML:

We analyze alternative data (e.g., transaction patterns, mobile usage, utility payments)

Apply classification algorithms to predict creditworthiness

Enable inclusive lending for underbanked populations


✅ Outcome: More accurate risk assessment + financial inclusion.


---

2. 🛡️ Fraud Detection

Fraudsters evolve fast. ML evolves faster.

We train models on millions of transactions, identifying subtle anomalies

Use a mix of real-time classification, unsupervised anomaly detection, and behavioral modeling

Continuously improve through feedback loops and active learning


🚨 ML helps flag suspicious activity before it turns into loss.


---

🔧 Tech Stack: Python | Scikit-learn | XGBoost | SHAP | FastAPI | Streamlit | AWS

🔄 The future of fintech is predictive, not reactive.

If you’re building intelligent financial systems—whether it’s for lending, fraud prevention, or personalization—let’s connect and exchange notes. 🚀

#Fintech #MachineLearning #CreditScoring #FraudDetection #ArtificialIntelligence #DataScience #FinancialInclusion #ResponsibleAI #Python #MLinFinance