Thanks to this 100% online Postgraduate diploma, you will master the most cutting-edge Artificial Intelligence techniques to predict market trends and optimize financial processes such as investment portfolio management”

Why Study at TECH?

TECH is the world's largest 100% online business school. It is an elite business school, with a model based on the highest academic standards. A world-class center for intensive managerial skills education.   

TECH is a university at the forefront of technology, and puts all its resources at the student's disposal to help them achieve entrepreneurial success"

At TECH Global University

Innovation

The university offers an online learning model that balances the latest educational technology with the most rigorous teaching methods. A unique method with the highest international recognition that will provide students with the keys to develop in a rapidly-evolving world, where innovation must be every entrepreneur’s focus.

"Microsoft Europe Success Story", for integrating the innovative, interactive multi-video system. 

The Highest Standards

Admissions criteria at TECH are not economic. Students don't need to make a large investment to study at this university. However, in order to obtain a qualification from TECH, the student's intelligence and ability will be tested to their limits. The institution's academic standards are exceptionally high...  

95% of TECH students successfully complete their studies.

Networking

Professionals from countries all over the world attend TECH, allowing students to establish a large network of contacts that may prove useful to them in the future.  

100,000+ executives prepared each year, 200+ different nationalities.

Empowerment

Students will grow hand in hand with the best companies and highly regarded and influential professionals. TECH has developed strategic partnerships and a valuable network of contacts with major economic players in 7 continents.  

500+ collaborative agreements with leading companies.

Talent

This program is a unique initiative to allow students to showcase their talent in the business world. An opportunity that will allow them to voice their concerns and share their business vision. 

After completing this program, TECH helps students show the world their talent.

Multicultural Context

While studying at TECH, students will enjoy a unique experience. Study in a multicultural context. In a program with a global vision, through which students can learn about the operating methods in different parts of the world, and gather the latest information that best adapts to their business idea.

TECH students represent more than 200 different nationalities.  

Learn with the best

In the classroom, TECH’s teaching staff discuss how they have achieved success in their companies, working in a real, lively, and dynamic context. Teachers who are fully committed to offering a quality specialization that will allow students to advance in their career and stand out in the business world. 

Teachers representing 20 different nationalities. 

TECH strives for excellence and, to this end, boasts a series of characteristics that make this university unique:   

Analysis

TECH explores the student’s critical side, their ability to question things, their problem-solving skills, as well as their interpersonal skills.  

Academic Excellence

TECH offers students the best online learning methodology. The university combines the Relearning method (postgraduate learning methodology with the best international valuation) with the Case Study. Tradition and vanguard in a difficult balance, and in the context of the most demanding educational itinerary. 

TECH is the world’s largest online university. It currently boasts a portfolio of more than 10,000 university postgraduate programs. And in today's new economy, volume + technology = a ground-breaking price. This way, TECH ensures that studying is not as expensive for students as it would be at another university.   

At TECH, you will have access to the most rigorous and up-to-date case analyses in academia” 

The Postgraduate diploma in Advanced Financial Management with Artificial Intelligence is a program that is delivered in a convenient 100% online mode, which gives you the opportunity to carry it out while you develop your professional work full time. Over the course of 6 months, graduates will be prepared to overcome any economic risk by implementing advanced technologies such as Machine Learning.

You will apply predictive models based on Machine Learning to anticipate market trends and predict risks in investment portfolios”

Syllabus

Through this Postgraduate diploma in Advanced Financial Management with Artificial Intelligence, professionals will lead the digital transformation in their companies and implement algorithms to optimize strategic decision making.

Designed by references in the application of Artificial Intelligence in the financial sector, the itinerary will delve into the most sophisticated techniques to automate complex tasks such as invoice processing or detection of economic fraud.

Students will acquire skills to design and implement Deep Learning models that accurately manage cash flows. They will also handle TensorFlow to predict asset price fluctuations and stock market movements.

This Postgraduate diploma in Advanced Financial Management with Artificial Intelligence is developed over 6 months and is divided into 3 modules:

Module 1. Automation of Financial Department Processes with Artificial Intelligence
Module 2. Strategic Planning and Decision Making with Artificial Intelligence
Module 3. Advanced Financial Optimization Techniques with OR-Tools

Where, When and How is it Taught?

TECH offers the possibility of developing this Postgraduate diploma in Advanced Financial Management with Artificial Intelligence completely online. Throughout the 6 months of the educational program, the students will be able to access all the contents of this program at any time, allowing them to self-manage their study time.

Module 1. Automation of Financial Department Processes with Artificial Intelligence

1.1. Automation of Financial Processes with Artificial Intelligence and Robotic Process Automation (RPA)

1.1.1. AI and RPA for Process Automation and Robotization
1.1.2. RPA Platforms for Financial Processes: UiPath, Blue Prism, and Automation Anywhere
1.1.3. Evaluation of RPA Use Cases in Finance and Expected ROI

1.2. Automated Invoice Processing with AI with Kofax

1.2.1. Configuration of AI Solutions for Invoice Processing with Kofax
1.2.2. Application of Machine Learning Techniques for Invoice Classification
1.2.3.  Automation of the Accounts Payable Cycle with AI Technologies

1.3. Payment Automation with AI Platforms

1.3.1. Implementing Automated Payment Systems with Stripe Radar and AI
1.3.2. Use of Predictive AI Models for Efficient Cash Management 
1.3.3. Security in Automated Payment Systems: Fraud Prevention with AI

1.4. Bank Reconciliation with AI and Machine Learning

1.4.1. Automation of Bank Reconciliation Using AI with Platforms Such as Xero
1.4.2. Implementation of Machine Learning Algorithms to Improve Accuracy
1.4.3. Case Studies: Efficiency Improvements and Error Reduction

1.5. Cash Flow Management with Deep Learning and TensorFlow

1.5.1. Predictive Cash Flow Modeling with LSTM Networks Using TensorFlow
1.5.2. Implementation of LSTM Models in Python for Financial Forecasting
1.5.3. Integration of Predictive Models in Financial Planning Tools

1.6. Inventory Automation with Predictive Analytics

1.6.1. Use of Predictive Techniques to Optimize Inventory Management
1.6.2. Apply Predictive Models with Microsoft Azure Machine Learning
1.6.3. Integration of Inventory Management Systems with ERP

1.7. Creation of Automated Financial Reports with Power BI

1.7.1. Automation of Financial Reporting using Power BI
1.7.2. Developing Dynamic Dashboards for Real-Time Financial Analysis
1.7.3. Case Studies of Improvements in Financial Decision Making with Automated Reports

1.8. Purchasing Optimization with IBM Watson

1.8.1. Predictive Analytics for Purchasing Optimization with IBM Watson
1.8.2. AI Models for Negotiations and Pricing
1.8.3. Integration of AI Recommendations in Purchasing Platforms 

1.9. Customer Support with Financial Chatbots and Google DialogFlow

1.9.1. Implementation of Financial Chatbots with Google Dialogflow 
1.9.2. Integration of Chatbots in CRM Platforms for Financial Support 
1.9.3. Continuous Improvement of Chatbots Based on User Feedback 

1.10. AI-Assisted Financial Auditing 

1.10.1. AI Applications in Internal Audits: Transaction Analysis 
1.10.2. Implementation of AI for Compliance Auditing and Discrepancy Detection
1.10.3. Improvement of Audit Efficiency with AI Technologies

Module 2. Strategic Planning and Decision Making with Artificial Intelligence

2.1. Predictive Modeling for Strategic Planning with Scikit-Learn

2.1.1. Building Predictive Models with Python and Scikit-Learn 
2.1.2. Application of Regression Analysis in Project Evaluation 
2.1.3. Validation of Predictive Models Using Cross-Validation Techniques in Python

2.2. Scenario Analysis with Monte Carlo Simulations 

2.2.1. Implementation of Monte Carlo Simulations with Python for Risk Analysis 
2.2.2. Use of AI for the Automation and Improvement of Scenario Simulations
2.2.3. Interpretation and Application of Results for Strategic Decision Making

2.3. Investment Appraisal using AI

2.3.1. AI Techniques for the Valuation of Assets and Companies
2.3.2. Machine Learning Models for Value Estimation with Python
2.3.3. Case Analysis:  Use of AI in the Valuation of Technology Startups

2.4. Optimization of Mergers and Acquisitions with Machine Learning and TensorFlow

2.4.1. Predictive Modeling to Evaluate M&A Synergies with TensorFlow
2.4.2. Simulation of Post-M&A Integrations with AI Models
2.4.3. Use of NLP for Automated due Diligence Analysis

2.5. Portfolio Management with Genetic Algorithms

2.5.1. Use of Genetic Algorithms for Portfolio Optimization
2.5.2. Implementation of Selection and Allocation Strategies with Python 
2.5.3. Analyzing the Effectiveness of Portfolios Optimized by AI

2.6. Artificial Intelligence for Succession Planning

2.6.1. Use of AI for Talent Identification and Development 
2.6.2. Predictive Modeling for Succession Planning using Python
2.6.3. Improvements in Change Management using AI Integration

2.7. Market Strategy Development with AI and TensorFlow

2.7.1. Application of Deep Learning Techniques for Market Analysis
2.7.2. Use of TensorFlow and Keras for Market Trend Modeling 
2.7.3. Development of Market Entry Strategies Based on AI Insights

2.8. Competitiveness and Competitive Analysis with AI and IBM Watson 

2.8.1. Competitor Monitoring using NLP and Machine Learning
2.8.2. Automated Competitive Analysis with IBM Watson 
2.8.3. Implementation of Competitive Strategies Derived from AI Analysis 

2.9. AI-Assisted Strategic Negotiations 

2.9.1. Application of AI Models in the Preparation of Negotiations
2.9.2. Use of AI-Based Negotiation Simulators for Training Purposes 
2.9.3. Evaluation of the Impact of AI on Negotiation Results 

2.10. Implementation of AI Projects in Financial Strategy

2.10.1. Planning and Management of AI Projects 
2.10.2. Use of Project Management Tools Such as Microsoft Project
2.10.3. Presentation of Case Studies and Analysis of Success and Learning

Module 3. Advanced Financial Optimization Techniques with OR-Tools

3.1. Introduction to Financial Optimization

3.1.1. Basic Optimization Concepts
3.1.2. Optimization Tools and Techniques in Finance
3.1.3. Applications of Optimization in Finance

3.2. Investment Portfolio Optimization

3.2.1. Markowitz Models for Portfolio Optimization
3.2.3. Portfolio Optimization with Constraints
3.2.4. Implementation of Optimization Models with OR-Tools in Python 

3.3. Genetic Algorithms in Finance

3.3.1. Introduction to Genetic Algorithms 
3.3.2. Application of Genetic Algorithms in Financial Optimization
3.3.3. Practical Examples and Case Studies 

3.4. Linear and Non-linear Programming in Finance

3.4.1. Fundamentals of Linear and Nonlinear Programming
3.4.2. Applications in Portfolio Management and Resource Optimization
3.4.3. Tools for Solving Linear Programming Problems 

3.5. Stochastic Optimization in Finance 

3.5.1. Concepts of Stochastic Optimization 
3.5.2. Applications in Risk Management and Financial Derivatives
3.5.3. Stochastic Optimization Models and Techniques 

3.6. Robust Optimization and its Application in Finance 

3.6.1. Fundamentals of Robust Optimization 
3.6.2. Applications in Uncertain Financial Environments 
3.6.3. Case Studies and Examples of Robust Optimization

3.7. Multi-Objective Optimization in Finance

3.7.1. Introduction to Multiobjective Optimization 
3.7.2. Applications in Diversification and Asset Allocation
3.7.3. Techniques and Tools for Multiobjective Optimization 

3.8. Machine Learning for Financial Optimization 

3.8.1. Application of Machine Learning Techniques in Optimization 
3.8.2. Optimization Algorithms Based on Machine Learning
3.8.3. Implementation and Case Studies 

3.9. Optimization Tools in Python and OR-Tools 

3.9.1. Python Optimization Libraries and Tools (SciPy, OR-Tools)
3.9.2. Practical Implementation of Optimization Problems
3.9.3. Examples of Financial Applications

3.10. Projects and Practical Applications of Financial Optimization

3.10.1. Development of Financial Optimization Projects
3.10.2. Implementation of Optimization Solutions in the Financial Sector
3.10.3. Evaluation and Presentation of Project Results

A flexible syllabus, with no fixed schedules and teaching materials available 24 hours a day. What are you waiting for to enroll?"