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Showing posts with the label AI credit scoring

The Problem with Traditional Credit

Why the Old Credit System Is Failing and What You Can Do About It in 2025 Introduction Traditional credit has long been the backbone of lending and personal finance: credit cards, personal loans, mortgages, auto loans. But in an era of gig-economy incomes , side hustles, digital payments, alternative data and rapidly evolving financial behaviors, the “old way” of assessing creditworthiness is showing serious cracks. In this post you’ll learn why traditional credit scoring and lending is problematic , who gets left behind, how the system perpetuates inequalities, and what practical steps you – whether a consumer, freelancer or creator – can take to work around or improve your credit prospects in 2025. We’ll cover: what “traditional credit” means the core challenges (industry-wide) effects on everyday people why fintech / alternative credit scoring is rising practical tips to improve your credit standing future outlook of credit in a digital world Let’s dive i...

💳 AI and Financial Inclusion: How Credit Scoring with Non-Traditional Data is Transforming Access to Finance

🌍 Introduction: Why AI Matters for Financial Inclusion Across the world, 1.4 billion adults remain unbanked (World Bank, 2023). Traditional credit scoring systems — built on financial history, credit cards, and bank loans — exclude millions of people who have never had access to formal banking. This creates a cycle where those without credit cannot access credit, trapping them outside economic growth. Here’s where artificial intelligence (AI) comes in. By analyzing non-traditional data sources like mobile payments, utility bills, social signals, and online transactions , AI-powered credit scoring models can bring financial inclusion to billions. This blog explores the future of credit scoring and how AI is unlocking new opportunities in fintech, empowering communities, and reshaping global finance. 🏛️ The Traditional Credit Scoring System: A Barrier to Inclusion Traditional credit systems rely on: Credit history (loans, credit cards). Income and employment data. Repayme...