AI & ML
According to Mark,“ AI-driven predictive analytics can adjust to these fluctuations by integrating sentiment analysis, macroeconomic indicators and alternative datasets like supply chain disruptions and consumer spending patterns.” This adaptation allows for more accurate forecasting during uncertain times.
Alan adds that models are increasingly using“ smaller historical reference frames as recent trends in many sectors diverge from long-term patterns.” Additionally, forecasting updates have become more frequent,“ moving away from annual model refreshes with monthly runs to hourly, or even more frequent, updates with near real-time execution.”
“ By leveraging deep learning and automated model retraining, institutions can maintain financial stability, optimise risk strategies and make informed decisions in volatile markets,” Mark explains. The agility of modern predictive models enhances resilience, allowing banks to navigate economic uncertainty with greater confidence.
Revolutionising fraud detection with real-time analytics Perhaps one of the most striking applications of predictive analytics is in fraud detection, where traditional rule-based systems are giving way to sophisticated real-time monitoring.“ Fraud detection is becoming increasingly sophisticated as data volume, analysis speed and advanced techniques continue to evolve,” explains Alan.“ What started with simple rules,
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