Banking machine learning in iot

The intersection of banking, machine learning (ML), and the Internet of Things (IoT) opens up a range of innovative applications that are transforming how financial services are delivered and secured. Here's a breakdown of how these technologies interact, along with real-world use cases and research directions:

🔁 How They Work Together

  • IoT devices (e.g., ATMs, mobile phones, smart cards, wearables) collect large volumes of real-time data.

  • Machine Learning algorithms process and analyze this data to:

    • Detect anomalies

    • Personalize services

    • Automate decisions

  • Banking systems leverage this to improve customer experience, security, and operational efficiency.



📌 Use Cases

1. Fraud Detection

  • IoT sensors in ATMs or POS machines track physical behavior.

  • ML analyzes behavioral patterns (e.g., how a card is swiped, location of usage) to flag anomalies.

  • E.g., sudden withdrawal from a different country while your phone is still in your home country.

2. Personalized Banking

  • IoT wearables (like smartwatches) feed real-time data into ML models.

  • Banks can offer tailored financial advice, loan offers, or spending alerts based on lifestyle habits.

3. Smart ATMs

  • ML optimizes ATM cash replenishment schedules using usage data from IoT sensors.

  • Biometric-enabled ATMs with facial/voice recognition enhance security and convenience.

4. Credit Scoring

  • ML models can use alternative data from IoT sources (e.g., utility payments via smart meters) to assess creditworthiness, especially for the underbanked.

5. Predictive Maintenance

  • IoT sensors in banking hardware (ATMs, kiosks) monitor performance.

  • ML predicts when maintenance is needed to prevent breakdowns.

🔐 Security Applications

  • Continuous authentication: IoT devices monitor user's environment and activity; ML confirms identity without constant logins.

  • Intrusion detection systems: ML detects suspicious access patterns across IoT-enabled banking devices.

📚 Research Directions

  • Federated learning: Training models across decentralized IoT devices without sharing raw data—great for privacy in banking.

  • Adversarial ML in IoT: Studying how to defend against manipulated inputs in smart banking devices.

  • Explainable AI (XAI): Making ML decisions in finance transparent for regulatory compliance.

⚙️ Technologies Involved

  • IoT Platforms: AWS IoT, Azure IoT Hub

  • ML Frameworks: TensorFlow, PyTorch, Scikit-learn

  • Protocols: MQTT, CoAP for low-power data transfer

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Hello and welcome to my blog! I’m Srishti, a passionate and curious student currently pursuing my  Bachelor of Computer Applications (BCA) . Ever since I was introduced to the world of computers, I knew this was my calling. From exploring basic coding in school to diving deep into the fundamentals of programming, databases, and web development in college, my journey has been nothing short of exciting. 📚 Academic Journey I’ve always believed in the power of learning and consistently worked hard to maintain good academic performance throughout my life. Here’s a quick snapshot of my educational background: High School (10th Grade)  - 2021 📊 Score: 85% This was the phase where I first developed an interest in technology and computers. Higher Secondary (12th Grade)  -2023 🎓 Stream: Science with computer science  📊 Score: 70% This is where I took my first steps into programming, which played a major role in choosing BCA. Bachelor of Computer Applications (BCA)  – ...
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