Integrating Machine Learning (ML) On Mobile Applications Using Local Data To Personalize The Experience For The Customer

Abstract

Mobile banking apps are getting harder to use as banks keep adding more features, making it difficult for customers to find what they need quickly. Most current solutions send your data to the cloud for processing, which raises privacy concerns, slows things down, and isn't great for the environment. Running machine learning directly on your phone offers a better approach - it learns from your habits without your data ever leaving your device. This system uses your phone's built-in AI capabilities to make personalized suggestions while keeping all your data right on your device. It watches how you use the app - what features you access, when you use them, and what tasks you complete - to build a private dataset that never leaves your phone. Smart data processing turns your usage patterns into useful information, while efficient AI models provide accurate recommendations without slowing down your device. Testing shows this approach is much faster than cloud-based systems and works whether you're online or offline. Users get four main benefits: better privacy since data never leaves their phone, faster performance with instant responses, longer battery life from less network usage, and a smaller environmental impact from reduced cloud computing. This technology isn't just for banking - it could improve healthcare apps, educational software, and business productivity tools too.