Artificial Intelligence in Banking

Artificial intelligence has been successfully applied to various fields to create quantum-leap improvements across the entire supply and value chains. Artificial intelligence applications include recommender systems, smart assistants, chatbots, classifiers, and predictive engines. Recommender systems are currently able to recommend the right product to the right person at the right time. Smart assistants are part of everyday life. Chatbots are becoming more and more common in customer service applications. Classifiers can detect fraud, and predictive engines can predict credit defaults. 

With the ever-increasing use of social networks by customers, organizations are more inclined to analyze trends and sentiments. Natural Language Processing provides suitable solutions to this problem. Moreover, data visualization tools are becoming crucial to gain insights from the huge amount of data available to organizations.

The participant to this Artificial Intelligence in Banking course will learn how to apply artificial intelligence to the banking sector. In particular, the participant will learn how recommender systems, chatbots, classifiers, and predictive engines can provide value in the banking sector.

Duration

5 Days

Who Should Attend

• Risk Managers
• Marketing Managers and Professionals in the Banking sector
• Computer Programmers who intend to understand the applications of Artificial Intelligence in Banking
• Technologists and Researchers interested in Banking and Artificial Intelligence
• Customer Service Managers and Professionals in the Banking sector
• Senior Corporate Leaders, Managers, and Department Heads in the Banking sector

Module 1: Artificial Intelligence Basics

• Artificial Intelligence and Machine Learning
• Typical applications
• The Architecture of a System
• Software tools: Python
• Software tools: R
• Software tools: WEKA

Module 2: Data Analytics and Visualization

• Data Gathering
• Feature Engineering
• Statistical Analysis
• Data Visualization
• Dimensionality Reduction

Module 3: Unsupervised and Supervised Learning

• Similarity estimation
• Clustering
• Association rules
• Recommender systems
• K-Nearest Neighbors
• Decision Trees
• Naïve Bayes
• Artificial Neural Networks

Module 4: Natural Language Processing

• Extracting structure from raw text
• Regular expressions
• Word features and semantics
• Text classification
• Information extraction
• Question answering systems

Module 5: Building a Chatbot 

• Extracting information from conversations
• Chatbot as a search engine
• Natural Language Understanding
• Natural Language Generation
• Building a System