The British Academy for Training and Development offers this training program in Financial Risk Management with Artificial Intelligence, aimed at enabling participants to effectively engage with an increasingly complex financial environment driven by smart technologies.

The program focuses on developing a deep understanding of how AI can be used to identify, assess, and mitigate financial risks that institutions and markets may face. It includes data analysis techniques, predictive model building, and advanced methods for detecting financial threats before they occur.

With the growing volatility in global financial markets, institutions must possess intelligent tools capable of early detection and real-time response to risk sources. This program is a practical response to that need, providing participants with the knowledge and skills required to transform big data into precise strategic decisions. It also enhances the participant's ability to align organizational goals with best practices in AI-powered risk management.

Who Should Attend?

• Risk managers in financial and banking institutions.

• Data analysts and professionals in applied artificial intelligence.

• Compliance officers and internal auditors in corporations.

• Financial consultants and decision-makers in both public and private sectors.

Knowledge and Benefits:

After completing the program, participants will be able to master the following:

• Understand the fundamental concepts of financial risk management and the role of AI in its advancement.

• Learn about intelligent tools for monitoring and analyzing risks.

• Develop predictive models that support financial decisions and reduce exposure.

• Use machine learning techniques to enhance early risk detection mechanisms.

• Integrate AI into financial work environments to achieve responsiveness and organizational integration.

• Risk Management Concepts

  • Definition and types of financial risks

  • Components of a risk management system

  • The relationship between risk and financial performance

• Institutional Risk Framework

  • The role of governance in risk control

  • Regulatory bodies and compliance frameworks

  • Integration between strategy and risk management

• Risk Metrics and Assessment

  • Financial risk indicators

  • Probability and impact assessment techniques

  • Quantitative and qualitative classification tools

• Principles of Artificial Intelligence

  • Differences between AI and machine learning

  • AI system lifecycle

  • AI capabilities in data analysis

• Types of Financial Algorithms

  • Predictive and classification algorithms

  • Regression and clustering-based models

  • Anomaly detection algorithms

• Supporting Digital Technologies

  • Role of big data in financial modeling

  • Analysis of unstructured data

  • APIs in banking systems

• Data as a Source of Risk

  • Internal and external data sources

  • Data quality and its impact on outcomes

  • Challenges in organizing financial data

• Descriptive and Exploratory Analysis

  • Historical trend analysis

  • Visualization of financial relationships

  • Identifying recurring patterns

• Designing Smart Databases

  • Structure of financial databases

  • Data integration across systems

  • Use of NoSQL models in financial analysis

• Predictive Risk Models

  • Building bankruptcy prediction models

  • Forecasting market volatility

  • Models for predicting payment defaults

• Machine Learning in Financial Forecasting

  • Supervised and unsupervised learning methods

  • Model accuracy testing

  • Improving models based on performance

• Behavioral Pattern Recognition

  • Analysis of customer and institutional behavior

  • Intelligent credit scoring

  • Crisis prediction through behavioral indicators

• Risk Scenario Analysis

  • Building multi-outcome scenarios

  • Assessing impact of rare probabilities

  • Monte Carlo simulation

• Measuring Risk Value

  • Value at Risk (VaR) model

  • Limitations of relying on normal distribution

  • Comparing quantitative tools

• Financial Dependence Modeling

  • Risk correlations between assets

  • Estimating joint default probabilities

  • Markov chains in risk evaluation

• From Manual to Intelligent Systems

  • Simplifying risk assessment processes

  • Automating reports and reviews

  • Integration with cloud computing systems

• Smart Response Systems

  • Early alerts and automated actions

  • Real-time transaction monitoring

  • Behavioral-based security adjustments

• Analytical Dashboards

  • Building interactive risk dashboards

  • Using performance indicators for monitoring

  • Supporting managerial decisions through data

• Intelligent Compliance Frameworks

  • International risk management standards

  • Automated compliance tools

  • The relationship between AI and regulation

• Information and Privacy Management

  • Protecting financial data

  • Encryption and access management mechanisms

  • GDPR compliance and local regulations

• AI-Specific Risks

  • Algorithmic biases and their impact

  • Loss of transparency in complex models

  • Operational risk management of intelligent systems

• Designing a Comprehensive Risk Management Plan

  • Defining goals and priorities

  • Allocating resources and roles

  • Developing monitoring and evaluation mechanisms

• Integrating AI into Policy

  • Supporting decision-makers with predictive systems

  • Updating policies based on data

  • Strategic alignment with technologies

• Future Vision for Risk Management

  • Global trends in financial technology

  • Institutional readiness for digital transformation

  • Sustainability of smart risk management