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Let's first understand what fintech is<\/strong><\/p>\n <\/p>\n Fintech is an abbreviation for Financial Technology<\/strong>which refers to the use of modern technologies\u2014such as smartphone applications, cloud computing, artificial intelligence, and APIs\u2014to deliver financial services faster cheaper, and more easily than traditional methods<\/strong> From traditional methods.<\/p>\n <\/p>\n <\/p>\n Digital payments and wallets<\/strong>Mobile payments or QR-code transactions instead of cash or plastic cards.<\/p>\n <\/p>\n Instant money transfer<\/strong>Instant money transfers: local or international transfers completed within seconds, with instant tracking.<\/p>\n <\/p>\n Buy now and pay later (BNPL)<\/strong>: Short-term installments with simplified procedures within the application.<\/p>\n <\/p>\n <\/p>\n <\/p>\n <\/p>\n As part of the digital transformation process in the Kingdom and Vision 2030, it has become data analysis has become the backbone of modern financial services<\/strong> The backbone of modern financial services. Banks no longer rely on human expertise alone, but mix it with artificial intelligence and big data capabilities to make more accurate decisions in predicting defaults, detecting fraud, and designing innovative financial products.<\/strong>.<\/p>\n <\/p>\n <\/p>\n The idea:<\/strong> before and during the approval of financing, the models predict the probability of a client's default, adjusting prices, limits and policies according to actual risks.<\/p>\n <\/p>\n How is it done<\/strong><\/p>\n <\/p>\n Combining traditional variables (income, liabilities, credit history) with Behavioral variables<\/strong> (Spending pattern, regularity of payment, balance change).<\/p>\n <\/p>\n Use machine learning algorithms to periodically update the forecast with each new transaction.<\/p>\n <\/p>\n Reliance on interpretable models<\/strong> to explain acceptance, rejection, and pricing decisions<\/p>\n <\/p>\n Interest:<\/strong><\/p>\n <\/p>\n More accurate risk pricing<\/strong> and a reduced default rate<\/p>\n <\/p>\n Early warnings<\/strong> and proactive treatment plans (extensions, rescheduling, consolidation offers)<\/p>\n <\/p>\n Wider financial inclusion<\/strong> With a fairer assessment than traditional models.<\/p>\n <\/p>\n <\/p>\n The idea:<\/strong> Assess the risk of each transaction in milliseconds to stop fraud before it happens\u2014while maintaining a seamless customer experience.<\/p>\n <\/p>\n How is it done<\/strong><\/p>\n <\/p>\n Instant risk recording<\/strong> for each transaction based on location, device, time, amount, and user<\/p>\n <\/p>\n Network analysis (Graph Analytics)<\/strong> To discover hidden interconnections between accounts, devices and addresses.<\/p>\n <\/p>\n Unsupervised learning<\/strong> to identify new patterns that have not yet been classified<\/p>\n <\/p>\n Interest:<\/strong><\/p>\n <\/p>\n Reducing losses and minimizing disputed claims.<\/p>\n <\/p>\n Reduce false alarms, which means Higher security with a better experience<\/strong>.<\/p>\n <\/p>\n Simplified example:<\/strong> if a card is used in a country the customer has never visited, in a high-risk store, and at a late hour, the system raises the risk score and requests verification or blocks the transaction<\/p>\n <\/p>\n <\/p>\n The idea:<\/strong> A more accurate understanding of the client's needs and behavior allows building personalized offers and experiences that increase the benefit for both parties.<\/p>\n <\/p>\n How is it done<\/strong><\/p>\n <\/p>\n Segmenting customers into behavioral groups<\/strong> (Saver, traveler, online shopper...).<\/p>\n <\/p>\n Recommending products, limits, and interest rates based on actual behavior rather than generic expectations.<\/p>\n <\/p>\n A\/B tests<\/strong> Continuous improvement of messages and offers.<\/p>\n <\/p>\n Interest:<\/strong><\/p>\n <\/p>\n Increased adoption, satisfaction, and revenue, with controlled risk<\/strong>.<\/p>\n <\/p>\n A practical example:<\/strong> A user who travels a lot and spends on restaurants-the application suggests a travel card<\/strong> with low exchange fees and travel insurance, or a BNPL<\/strong> plan for regular purchases<\/p>\n <\/p>\n <\/p>\n Faster and more accurate decisions<\/strong> compared to relying on experience alone<\/p>\n <\/p>\n Superior customer experience<\/strong>: Faster approvals, less annoying verification, and actually useful offers.<\/p>\n <\/p>\n Lower risk and reduced operating costs<\/strong> thanks to automation and intelligent systems<\/p>\n <\/p>\n Data analysis is the real engine of the fintech revolution in the kingdom. By predicting defaults, detecting fraud, and designing customized products, the banking sector is being reshaped into more efficient, safer, and more tailored to customer needs.<\/strong>. With the continued support of the legislative system and innovation, the kingdom's position as a regional and global financial center is strengthening.<\/p>\nDirect examples of fintech<\/strong><\/h4>\n
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The role of data analysis in art<\/strong><\/h3>\n
1) predicting payment defaults: from intuition to modeling<\/strong><\/h5>\n
2) fraud detection: real-time monitoring and no-down Protection<\/h5>\n
3) innovative and customized financial products: offers \"on size\"<\/strong><\/h5>\n
Why is this important<\/strong><\/h5>\n