In Ghana, banks like Ecobank use ML for efficiency, complying with BoG’s fintech regulations and post-2024 trends in sustainable banking.

a) Four Banking Activities for Machine Learning (10 Marks, 2.5 Each):

1. Fraud Detection: ML algorithms analyze transaction patterns in real-time to flag anomalies, reducing losses as in GCB’s systems.
2. Credit Risk Assessment: Models predict default probabilities using customer data, enhancing lending decisions per Basel III.
3. Customer Segmentation and Personalization: ML clusters clients for targeted marketing, improving cross-selling.
4. Predictive Maintenance for ATMs/IT Infrastructure: Forecasts equipment failures, minimizing downtime.

b) Five Benefits of Implementing ML Solutions (10 Marks, 2 Each):

1. Improved Efficiency and Cost Savings: Automates routine tasks, cutting operational costs amid BoG’s CRD pressures.
2. Enhanced Risk Management: Better accuracy in predictions reduces defaults, aligning with post-cleanup resilience.
3. Personalized Customer Experiences: Boosts satisfaction and retention through tailored services.
4. Competitive Advantage: Faster insights enable innovation, outpacing fintechs under Act 987.
5. Data-Driven Decision Making: Provides actionable analytics for profitability, supporting ethical and compliant practices