Thanks to this Postgraduate diploma, 100% online, you will handle both Google Data Studio and Plotly to manage large volumes of Financial Data and create the most comprehensive interactive Visualizations”

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” 

This program has been designed by real experts in Artificial Intelligence. Therefore, the curriculum will delve into issues such as the implementation of automatic payment systems using Stripe Radar, bank reconciliation with Machine Learning and the application of predictive models with Power BI. In this way, students will obtain advanced skills to automate financial processes through cutting-edge Deep Learning techniques. In addition, the teaching contents will delve into a variety of simulation methods for risk management in order for graduates to model different economic scenarios to analyze the performance of investments or assets.

You will be highly qualified to model financial risks by applying state-of-the-art Machine Learning and predictive analytics models”

Syllabus

The Postgraduate diploma in Financial Process Automation and Risk Management with Artificial Intelligence of TECH Global University is a high intensity program that prepares you to face economic challenges and strategic decisions in the field of Financial Advisory.

The syllabus will delve into issues such as the implementation of automatic payment systems using Stripe Radar, bank reconciliation with Machine Learning and the application of predictive models with Power BI.

In this way, students will obtain advanced technical skills to carry out simulations that model different economic scenarios to analyze the return on investments.

This Postgraduate Diploma takes place over 3 months and is divided into 3 modules: 

Module 1. Automation of Financial Department Processes with Artificial Intelligence
Module 2. Financial Data Analysis and Visualization with Plotly and Google Data Studio
Module 3. Artificial Intelligence for Financial Risk Management with TensorFlow and Scikit-learn

Where, When and How is it Taught?

TECH offers the possibility of developing this Postgraduate diploma in Financial Process Automation and Risk Management with Artificial Intelligence completely online. Throughout the 3 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. Applying Machine Learning Techniques for Invoice Classification 
1.2.3. Automation of the Accounts Payable Cycle with Artificial Intelligence Technologies

1.3. Payment Automation with Artificial Intelligence Platforms 

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

1.4. Bank Reconciliation with Artificial Intelligence and Machine Learning

1.4.1. Automation of Bank Reconciliation Using AI with Platforms such as Xero 
1.4.2. Implementing 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. Implementing LSTM Models in Python for Financial Forecasting  
1.5.3. Integrating Predictive Models into Financial Planning Tools 

1.6. Inventory Automation with Predictive Analytics

1.6.1. Using Predictive Techniques to Optimize Inventory Management 
1.6.2. Applying Predictive Modeling with Microsoft Azure Machine Learning 
1.6.3. Integrating Inventory Management Systems with ERP 

1.7. Creating Automated Financial Reports with Power BI 

1.7.1. Automating the Generation of Financial Reports 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 Reporting 

1.8. Purchasing Optimization with IBM Watson 

1.8.1. Predictive Analytics for Purchase Optimization with IBM Watson 
1.8.2. AI Models for Negotiations and Pricing 
1.8.3. Integrating AI Recommendations into Purchasing Platforms 

1.9. Customer Service with Financial Chatbots and Google DialogFlow

1.9.1. Implementing Financial Chatbots with Google DialogFlow
1.9.2. Integrating Chatbots in CRM Platforms for Financial Support 
1.9.3. Continuous Improvement of Chatbots Based on User Feedback 

1.10. Artificial Intelligence-Assisted Financial Auditing

1.10.1. AI Applications in Internal Audits: Transaction Analysis 
1.10.2. Implementing AI for Compliance Auditing and Discrepancy Detection 
1.10.3. Improving Audit Efficiency with Artificial Intelligence Technologies 

Module 2. Financial Data Analysis and Visualization with Plotly and Google Data Studio

2.1. Fundamentals of Financial Data Analysis 

2.1.1. Introduction to Data Analysis 
2.1.2. Tools and Techniques for Financial Data Analysis 
2.1.3. Importance of Data Analysis in Finance

2.2. Techniques for Exploratory Analysis of Financial Data

2.2.1. Descriptive Analysis of Financial Data 
2.2.2. Visualization of Financial Data with Python and R 
2.2.3. Identifying Patterns and Trends in Financial Data 

2.3. Financial Time Series Analysis

2.3.1. Fundamentals of Time Series 
2.3.2. Time Series Models for Financial Data 
2.3.3. Time Series Analysis and Forecasting 

2.4. Correlation and Causality Analysis in Finance

2.4.1. Correlation Analysis Methods 
2.4.2. Techniques for Identifying Causal Relationships 
2.4.3. Applications in Financial Analysis 

2.5. Advanced Visualization of Financial Data

2.5.1. Advanced Data Visualization Techniques  
2.5.2. Tools for Interactive Visualization (Plotly, Dash) 
2.5.3. Use Cases and Practical Examples

2.6. Cluster Analysis in Financial Data

2.6.1. Introduction to Cluster Analysis 
2.6.2. Applications in Market and Customer Segmentation 
2.6.3. Tools and Techniques for Cluster Analysis

2.7. Networks and Graph Analysis in Finance 

2.7.1. Fundamentals of Network Analysis 
2.7.2. Applications of Graph Analysis in Finance 
2.7.3. Tools for Network Analysis (NetworkX, Gephi) 

2.8. Text and Sentiment Analysis in Finance

2.8.1. Natural Language Processing (NLP) in Finance 
2.8.2. Sentiment Analysis in News and Social Media 
2.8.3. Tools and Techniques for Text Analysis 

2.9. Financial Data Visualization and Analysis Tools with AI 

2.9.1. Python Data Analysis Libraries (Pandas, NumPy) 
2.9.2. Visualization Tools in R (ggplot2, Shiny) 
2.9.3. Practical Implementation of Analysis and Visualization 

2.10. Projects and Practical Applications of Analysis and Visualization

2.10.1. Development of Financial Data Analysis Projects
2.10.2. Implementation of Interactive Visualization Solutions
2.10.3. Evaluation and Presentation of Project Results 

Module 3. Artificial Intelligence for Financial Risk Management with TensorFlow and Scikit-learn

3.1. Fundamentals of Financial Risk Management

3.1.1. Basic Concepts of Risk Management
3.1.2. Types of Financial Risks
3.1.3. Importance of Risk Management in Finance

3.2. Credit Risk Models with Artificial Intelligence

3.2.1. Machine Learning Techniques for Credit Risk Assessment
3.2.2. Credit Scoring Models (Scikit-Learn)
3.2.3. Implementing Credit Risk Models with Python

3.3. Market Risk Models with Artificial Intelligence

3.3.1. Market Risk Analysis and Management
3.3.2. Applying Predictive Models for Market Risk
3.3.3. Implementing Market Risk Models 

3.4. Operational Risk and Its Management with Artificial Intelligence

3.4.1. Concepts and Types of Operational Risk 
3.4.2. Applying AI Techniques to Operational Risk Management  
3.4.3. Tools and Practical Examples 

3.5. Liquidity Risk Models with Artificial Intelligence

3.5.1. Fundamentals of Liquidity Risk 
3.5.2. Machine Learning Techniques for Liquidity Risk Analysis 
3.5.3. Practical Implementation of Liquidity Risk Models 

3.6. Systemic Risk Analysis with Artificial Intelligence

3.6.1. Systemic Risk Concepts 
3.6.2. AI Applications in Systemic Risk Assessment 
3.6.3. Case Studies and Practical Examples

3.7. Portfolio Optimization with Risk Considerations

3.7.1. Portfolio Optimization Techniques 
3.7.2. Incorporating Risk Measures in the Optimization 
3.7.3. Portfolio Optimization Tools 

3.8. Simulation of Financial Risks

3.8.1. Simulation Methods for Risk Management 
3.8.2. Applying Monte Carlo Simulations in Finance 
3.8.3. Implementing Simulations with Python 

3.9. Risk Assessment and Continuous Risk Monitoring

3.9.1. Continuous Risk Assessment Techniques 
3.9.2. Risk Monitoring and Reporting Tools 
3.9.3. Implementing Continuous Monitoring Systems 

3.10. Projects and Practical Applications in Risk Management 

3.10.1. Development of Financial Risk Management Projects 
3.10.2. Implementation of AI Solutions for Risk Management 
3.10.3. Evaluation and Presentation of Project Results 

You will have a library of teaching resources available 24 hours a day and with material that stands out for its quality. What are you waiting for to enroll?"