Applied AI in Finances

1.1. Fundamental Analysis

1.1.1. Regression and Equity Analysis, Linear Regression

1.1.2.  AI-powered Financial Analytics

1.2. Regression in Finance

1.2.1.  Machine and Deep Learning as Model Estimation in Finance

1.2.2. Logistic Regression for Modelling Bank Failures 

2.1. Risk management

2.1.1.      Types of Risk in the Banking and Insurance Sector

2.1.2.      Machine Learning and Deep Learning in Risk Assessment

2.1.3.      Risk Management Tools

2.1.4.      Analytics and Big Data Tools in Risk Management

2.2. Fraud Detection

2.2.1.      Fraud and Benefits of Using Artificial Intelligence for Fraud Detection in Banking

2.2.2.      Surveillance of Conduct and Market Abuse in Trading

2.3. Modelling

2.3.1.      Credit Risk and Revenue Modelling

2.4. Ethics

2.4.1.      Ethics and Appliance with General Data Protection Regulation (GDPR)

3.1. Customer Behaviour

3.1.1. AI Customer Behaviour Analytics and Predictive Analytics

3.1.2. Impact of AI on Consumer Buying Behaviour

3.2. Interaction

3.2.1. AI in Customer Interaction

3.2.2. AI powered Virtual Assistants in Banking

4.1. Analysis, Monitoring, and Decisioning

4.1.1. Credit Decisioning, Insurance Decisioning

4.1.2. Monitoring and Collections,

4.1.3. Deepening Relationships,

5.1. Analysis and Predictions

5.1.1. Machine Learning and Deep Learning Predictive Approaches*.

5.1.2. Real-time Financial Time Series Analysis

5.2. Hidden Information Extraction

5.2.1. Feature Extraction in the Potential Market Opportunities

6.1. Operations Automation

6.1.1. Automation of Trade Finance

6.1.2. Automation of Banking Regulatory and Compliance

6.1.3. Automation in Anti-money Laundering (AML) and Sanction Screening

6.2. Workflow Automation

6.2.1. Automation of Cash Management Operations

6.2.2. Automation of Document Workflow and Internal Processes