Thanks to this 100% online program, you will get the most out of Big Data and analyze trends that influence the performance of financial assets” 

According to a study conducted by the International Finance Association, 70% of the entities that implement Artificial Intelligence solutions have managed to improve the accuracy of their economic analysis and optimize the management of their portfolios. Faced with this reality, more and more companies are demanding the incorporation of professionals who skillfully handle emerging tools such as Big Data, Natural Language Processing or Convolutional Neural Networks to make more informed strategic decisions and improve financial risk management. To take advantage of these job opportunities, experts need to have a competitive advantage that differentiates them from other candidates.

With this in mind, TECH is launching a revolutionary program in Artificial Intelligence in the Financial Department. Devised by renowned experts in this field, the academic itinerary will provide professionals with advanced skills to handle advanced tools ranging from Data Mining or Deep Computer Vision to Recurrent Neural Network models. Therefore, graduates will be highly qualified to use predictive models in financial risk management, optimize tedious tasks such as treasury management and even automate other processes such as internal audits. In addition, the didactic materials will delve into the most innovative methods for optimizing various investment portfolios. Also, the syllabus will offer advanced tools for designing complex economic data visualizations using Google Data Studio.

Moreover, the course is based on the revolutionary Relearning methodology promoted by TECH. This is a learning system that consists of the progressive reiteration of key aspects, which ensures that the essential concepts of the syllabus remain in the minds of the graduates. In addition, the syllabus can be planned individually, as there are no preset schedules or evaluation chronograms. Along the same lines, the Virtual Campus will be available 24 hours a day and will allow professionals to download the materials and consult them whenever they wish.

You will reach your full potential in the field of Financial Management with the help of multimedia resources in formats such as interactive summaries, explanatory videos and specialized readings”

This Professional master’s degree in Artificial Intelligence in the Financial Department contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by experts in Artificial Engineering
• The graphic, schematic and practical contents with which it is conceived provide complete and practical information on those disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

Looking to incorporate the most innovative Natural Language Processing techniques into your daily practice? Get it with this university program in less than a year”

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the course. For this purpose, students will be assisted by an innovative interactive video system created by renowned and experienced experts.

You will efficiently train Machine Learning models, which will allow you to foresee various potential financial risks"

You will have access to a learning system based on repetition, with natural and progressive teaching throughout the entire syllabus"

Through this Professional master’s degree, professionals will stand out for their solid knowledge on the implementation of Artificial Intelligence in financial procedures. Similarly, graduates will acquire advanced skills to run predictive models that enable proactive risk management and more accurate financial planning.

Likewise, experts will be able to implement robotic process automation solutions to optimize repetitive tasks such as accounting, treasury management and internal audits. In addition, students will ensure that these technological tools comply with legal regulations, therefore protecting the security of financial data.  

You will master the emerging technique of Data Mining and contribute to evidence-based financial decision making” 

General Objectives

• Apply Artificial Intelligence techniques in financial decision making
• Develop predictive models for financial risk management
• Optimize the allocation of financial resources using AI algorithms
• Automate routine financial processes using machine learning
• Implement natural language processing tools for the analysis of financial data 
• Design recommender systems for the financial sector
• Analyze large volumes of financial data using Big Data techniques
• Evaluate the impact of Artificial Intelligence on companies' profitability
• Improve financial fraud detection with the use of AI
• Create financial asset valuation models using Artificial Intelligence
• Develop financial simulation tools based on AI algorithms
• Apply data mining techniques to identify financial patterns
• Develop optimization models for financial planning
• Use neural networks to improve prediction of market trends
• Develop AI-based solutions for financial product personalization
• Implement AI systems for automated investment decisions
• Develop analytical capabilities for interpreting the results of financial AI models 
• Investigate the use of Artificial Intelligence in financial regulation and compliance
• Develop AI solutions to reduce costs in financial processes
• Identify opportunities for innovation in the financial sector through AI

Specific Objectives

Module 1. Fundamentals of Artificial Intelligence

• Analyze the historical evolution of Artificial Intelligence, from its beginnings to its current state, identifying key milestones and developments
• Understand the functioning of neural networks and their application in learning models in Artificial Intelligence
• Study the principles and applications of genetic algorithms, analyzing their usefulness in solving complex problems
• Analyze the importance of thesauri, vocabularies and taxonomies in the structuring and processing of data for AI systems
• Manage automation solutions using Artificial Intelligence to optimize efficiency in key tasks such as invoice processing, bank reconciliation or inventory management
• Manage tools such as TensorFlow and Scikit-Learn to support strategic decision making
• Develop advanced skills in exploratory financial data analysis and the creation of visualizations through tools such as Google Data Studio
• Lead the digital transformation within financial companies to increase their operational performance and improve the management of risks such as liquidity 

Module 2. Data Types and Data Life Cycle

• Understand the fundamental concepts of statistics and their application in data analysis
• Identify and classify the different types of statistical data, from quantitative to qualitative data
• Analyze the life cycle of data, from generation to disposal, identifying key stages
• Explore the initial stages of the data life cycle, highlighting the importance of data planning and structure
• Study data collection processes, including methodology, tools and collection channels
• Explore the  concept, with emphasis on the elements that comprise it and its design

Module 3. Data in Artificial Intelligence

• Master the fundamentals of data science, covering tools, types and sources for information analysis
• Explore the process of transforming data into information using data mining and visualization techniques
• Study the structure and characteristics of datasets, understanding their importance in the preparation and use of data for Artificial Intelligence models
• Use specific tools and best practices in data handling and processing, ensuring efficiency and quality in the implementation of Artificial Intelligence

Module 4. Data Mining: Selection, Pre-Processing and Transformation

• Master the techniques of statistical inference to understand and apply statistical methods in data mining
• Perform detailed exploratory analysis of data sets to identify relevant patterns, anomalies, and trends
• Develop skills for data preparation, including data cleaning, integration, and formatting for use in data mining
• Implement effective strategies for handling missing values in datasets, applying imputation or elimination methods according to context
• Identify and mitigate noise present in data, using filtering and smoothing techniques to improve the quality of the data set
• Address data preprocessing in Big Data environments

Module 5. Algorithm and Complexity in Artificial Intelligence

• Introduce algorithm design strategies, providing a solid understanding of fundamental approaches to problem solving
• Analyze the efficiency and complexity of algorithms, applying analysis techniques to evaluate performance in terms of time and space
• Study and apply sorting algorithms, understanding their performance and comparing their efficiency in different contexts
• Explore tree-based algorithms, understanding their structure and applications
• Investigate algorithms with Heaps, analyzing their implementation and usefulness in efficient data manipulation
• Analyze graph-based algorithms, exploring their application in the representation and solution of problems involving complex relationships
• Study Greedy algorithms, understanding their logic and applications in solving optimization problems
• Investigate and apply the  technique for systematic problem solving, analyzing its effectiveness in various scenarios

Module 6. Intelligent Systems

• Explore agent theory, understanding the fundamental concepts of its operation and its application in Artificial Intelligence and software engineering
• Study the representation of knowledge, including the analysis of ontologies and their application in the organization of structured information
• Analyze the concept of the semantic web and its impact on the organization and retrieval of information in digital environments
• Evaluate and compare different knowledge representations, integrating these to improve the efficiency and accuracy of intelligent systems

Module 7. Machine Learning and Data Mining

• Introduce the processes of knowledge discovery and the fundamental concepts of machine learning
• Study decision trees as supervised learning models, understanding their structure and applications
• Evaluate classifiers using specific techniques to measure their performance and accuracy in data classification
• Study neural networks, understanding their operation and architecture to solve complex machine learning problems
• Explore Bayesian methods and their application in machine learning, including Bayesian networks and Bayesian classifiers
• Analyze regression and continuous response models for predicting numerical values from data
• Study clustering techniques to identify patterns and structures in unlabeled data sets
• Explore text mining and natural language processing (NLP), understanding how machine learning techniques are applied to analyze and understand text

Module 8. Neural Networks, the Basis of Deep Learning

• Master the fundamentals of Deep Learning, understanding its essential role in Deep Learning
• Explore the fundamental operations in neural networks and understand their application in model building
• Analyze the different layers used in neural networks and learn how to select them appropriately
• Understand the effective linking of layers and operations to design complex and efficient neural network architectures
• Use trainers and optimizers to tune and improve the performance of neural networks
• Explore the connection between biological and artificial neurons for a deeper understanding of model design

Module 9. Deep Neural Networks Training

• Solve gradient-related problems in deep neural network training
• Explore and apply different optimizers to improve the efficiency and convergence of models
• Program the learning rate to dynamically adjust the convergence speed of the model
• Understand and address overfitting through specific strategies during training
• Apply practical guidelines to ensure efficient and effective training of deep neural networks
• Implement Transfer Learning as an advanced technique to improve model performance on specific tasks
• Explore and apply  techniques to enrich datasets and improve model generalization
• Develop practical applications using Transfer Learning to solve real-world problems

Module 10. Model Customization and Training with TensorFlow

• Master the fundamentals of TensorFlow and its integration with NumPy for efficient data management and calculations
• Customize models and training algorithms using the advanced capabilities of TensorFlow
• Explore the tfdata API to efficiently manage and manipulate datasets
• Implement the TFRecord format for storing and accessing large datasets in TensorFlow
• Use Keras preprocessing layers to facilitate the construction of custom models
• Explore the TensorFlow Datasets project to access predefined datasets and improve development efficiency
• Develop a Deep Learning application with TensorFlow, integrating the knowledge acquired in the module
• Apply in a practical way all the concepts learned in building and training custom models with TensorFlow in real-world situations

Module 11. Deep Computer Vision with Convolutional Neural Networks

• Understand the architecture of the visual cortex and its relevance in Deep Computer Vision
• Explore and apply convolutional layers to extract key features from images
• Implement clustering layers and their use in Deep Computer Vision models with Keras
• Analyze various Convolutional Neural Network (CNN) architectures and their applicability in different contexts
• Develop and implement a CNN ResNet using the Keras library to improve model efficiency and performance
• Use pre-trained Keras models to leverage transfer learning for specific tasks
• Apply classification and localization techniques in Deep Computer Vision environments
• Explore object detection and object tracking strategies using Convolutional Neural Networks

Module 12. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention

• Develop skills in text generation using Recurrent Neural Networks (RNN)
• Apply RNNs in opinion classification for sentiment analysis in texts
• Understand and apply attentional mechanisms in natural language processing models
• Analyze and use Transformers models in specific NLP tasks
• Explore the application of Transformers models in the context of image processing and computer vision
• Become familiar with the Hugging Face Transformers library for efficient implementation of advanced models
• Compare different Transformers libraries to evaluate their suitability for specific tasks
• Develop a practical application of NLP that integrates RNN and attention mechanisms to solve real-world problems

Module 13. Autoencoders, GANsand Diffusion Models

• Develop efficient representations of data using  and Diffusion Models
• Perform PCA using an incomplete linear autoencoder to optimize data representation
• Implement and understand the operation of stacked autoencoders
• Explore and apply convolutional autoencoders for efficient visual data representations
• Analyze and apply the effectiveness of sparse automatic encoders in data representation
• Generate fashion images from the MNIST dataset using Autoencoders
• Understand the concept of Generative Adversarial Networks (GANs) and Diffusion Models
• Implement and compare the performance of Diffusion Models and  in data generation

Module 14. Bio-Inspired Computing

• Introduce the fundamental concepts of bio-inspired computing
• Analyze space exploration-exploitation strategies in genetic algorithms
• Examine models of evolutionary computation in the context of optimization
• Continue detailed analysis of evolutionary computation models
• Apply evolutionary programming to specific learning problems
• Address the complexity of multi-objective problems in the framework of bio-inspired computing
• Explore the application of neural networks in the field of bio-inspired computing
• Delve into the implementation and usefulness of neural networks in bio-inspired computing

Module 15. Artificial Intelligence: Strategies and Applications

• Develop strategies for the implementation of artificial intelligence in financial services
• Identify and assess the risks associated with the use of AI in the healthcare field
• Assess the potential risks associated with the use of AI in industry
• Apply artificial intelligence techniques in industry to improve productivity
• Design artificial intelligence solutions to optimize processes in public administration
• Evaluate the implementation of AI technologies in the education sector
• Apply artificial intelligence techniques in forestry and agriculture to improve productivity
• Optimize human resources processes through the strategic use of artificial intelligence

Module 16. Automation of Financial Department Processes with Artificial Intelligence

• Master the automation of financial processes using Robotic Process Automation to optimize accuracy in tasks such as invoice processing
• Apply Deep Learning techniques to improve liquidity and working capital
• Create automated financial reports through Power Bi, increasing the speed of report writing
• Implement systems that minimize human error in the processing of economic data, increasing the reliability of financial information

Module 17. Strategic Planning and Decision Making with Artificial Intelligence

• Use the Scikit-Learn predictive model for strategic planning and informed financial decision making
• Manage TensorFlow to develop market strategies based on Artificial Intelligence, increasing the competitiveness and adaptability of companies in a dynamic financial environment

Module 18. Advanced Financial Optimization Techniques with OR-Tools

• Master investment portfolio optimization techniques using linear, nonlinear and stochastic programming to improve financial portfolios
• Apply genetic algorithms in financial optimization, exploring innovative solutions to complex problems

Module 19. Analysis and Visualization of Financial Data with Plotly and Google Data Studio

• Develop advanced skills to use tools such as Google Data Studio to create interactive visualizations that can be used to analyze and visualize financial data
• Accurately analyze financial time series and detect both historical trends and recurring patterns

Module 20. Artificial Intelligence for Financial Risk Management with TensorFlow and Scikit-Learn

• Implement state-of-the-art credit, market and liquidity risk models using Machine Learning
• Carry out simulation techniques to assess and manage the impact of financial risks in different scenarios

The teaching materials of this program, elaborated by these specialists, have contents that are completely applicable to your professional experiences"