AI in Banking: Enhancing Operations & Customer Engagement Course in Paris

Artificial Intelligence Applications in Banking and Financial Services

Artificial intelligence is transforming the banking industry by improving operational efficiency, strengthening risk management, detecting fraudulent activities, and delivering more personalised customer experiences. As financial institutions increasingly adopt AI-driven technologies, banking professionals need the knowledge and practical skills to identify valuable applications, analyse financial data, and implement AI solutions responsibly.

The AI in Banking: Enhancing Operations & Customer Engagement course provides participants with a practical understanding of how artificial intelligence, machine learning, and data analytics support modern banking operations. The programme covers AI applications in credit scoring, fraud detection, customer relationship management, financial forecasting, and automated customer service.

Through Python-based workshops, practical exercises, and banking case studies, participants explore exploratory data analysis, predictive modelling, natural language processing (NLP), and deep learning techniques. The course also addresses model evaluation, data governance, regulatory compliance, and ethical AI deployment, enabling participants to develop practical AI solutions that improve banking performance and customer engagement.

You'll Learn How To

• Understand the role of artificial intelligence in banking and financial services.
• Identify AI applications in credit risk, fraud detection, and customer relationship management.
• Use Python and relevant libraries to analyse banking datasets.
• Conduct exploratory data analysis to identify patterns and business insights.
• Prepare financial data for machine learning and predictive modelling.
• Develop models for loan default prediction, credit risk assessment, and customer churn.
• Apply machine learning techniques to transaction monitoring and fraud detection.
• Evaluate predictive models using appropriate performance metrics.
• Use natural language processing to improve automated banking customer service.
• Explore deep learning techniques for financial and transaction data analysis.
• Address data privacy, algorithmic bias, and regulatory compliance requirements.
• Plan the responsible deployment and monitoring of AI solutions in banking.