Through this 100% online Postgraduate diploma, you will handle the tools of Artificial Intelligence to automate Financial Processes and manage investment Risks” 

A new report prepared by the World Bank reflects that Artificial Intelligence technologies are driving a profound transformation in the way financial organizations operate, offering solutions that improve efficiency, accuracy and adaptability in the face of an ever-changing global economic environment. Faced with this reality, professionals need to manage the use of advanced algorithms and Machine Learning to identify patterns and anomalies in financial data, in order to identify potential risks. 

In this framework, TECH launches a revolutionary program in Financial Process Automation and Risk Management with Artificial Intelligence. The academic itinerary will delve into areas ranging from robotic automation of processes in financial operations or the implementation of automated payment systems using Stripe Radar to cash flow management using Deep Learning algorithms. In addition, the syllabus will address in detail the advanced techniques of financial data analysis using Google Data Studio, providing students with the skills to interpret large volumes of data efficiently. In addition, the program will provide various Machine Learning strategies for quantitative credit risk assessment, allowing a more accurate identification and mitigation of financial risks through sophisticated predictive models. 

On the other hand, the methodology of this program reinforces its innovative character. To this end, it employs the Relearning methodology, based on the repetition of key concepts to fix knowledge and facilitate learning. In this way, the combination of flexibility and a robust pedagogical approach makes it highly accessible. In addition, experts will have access to a didactic library with a variety of multimedia resources in different formats such as interactive summaries, explanatory videos and infographics. The specialists will also be specialized in simulated learning environments to extract valuable lessons that will be applied in their work practice. 

An academic experience with no set schedule and which you can access from any device with an Internet connection. Even from your cell phone!"  

This Postgraduate diploma in Financial Process Automation and Risk Management with Artificial Intelligence contains the most complete and up-to-date program on the market. The most important features include:

• Development of practical cases presented by experts in Artificial Intelligence 
• The graphic, schematic, and practical contents with which they are created, provide practical information on the disciplines that are essential for professional practice 
• Practical exercises where self-assessment can be used 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 

You'll use data analytics to support strategic decisions in areas such as investments, financing and portfolio management” 

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.   

 Looking to apply predictive models for financial risk assessment? Achieve it with this university program in only 3 months"

The Relearning system applied by TECH in its programs reduces the long hours of study so frequent in other teaching methods. You will enjoy a natural and progressive learning process!"

This university program has been designed by recognized experts in Financial Process Automation and Risk Management with Artificial Intelligence. The study plan will delve into issues ranging from robotic automation of financial processes or implementation of automatic payment systems with Stripe Radar to cash flow management with Deep Learning. At the same time, the syllabus will delve into the most advanced techniques for analyzing financial data with Google Data Studio. In addition, the program will offer the most effective Machine Learning strategies to evaluate credit risk.  

You will implement Artificial Intelligence solutions to automate routine financial tasks such as bank reconciliation, accounts receivable management and reporting”

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

Module 2. Analysis and Visualization of Financial Data 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. Network and Graph Analysis in Finance 

2.7.1. Fundamentals of Network Analysis 
2.7.2. Applications of Network Analysis in Finance 
2.7.3. Network Analysis Tools (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 Networks 
2.8.3. Tools and Techniques for Text Analysis 

2.9. Financial Data Analysis and Visualization Tools with AI 

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

2.10. Practical Analysis and Visualization Projects and Applications 

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. Risk Management Basics 
3.1.2. Types of Financial Risks 
3.1.3. Importance of Risk Management in Finance 

3.2. Credit Risk Models with AI 

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

3.3. Market Risk Models with AI 

3.3.1. Market Risk Analysis and Management 
3.3.2. Application of Predictive Market Risk Models 
3.3.3. Implementation of Market Risk Models 

3.4. Operational Risk and its Management with AI 

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

3.5. Liquidity Risk Models with AI 

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 AI 

3.6.1. Systemic Risk Concepts 
3.6.2. Applications of AI in the Evaluation of Systemic Risk 
3.6.3. Case Studies and Practical Examples 

3.7. Portfolio Optimization with Risk Considerations 

3.7.1. Portfolio Optimization Techniques 
3.7.2. Incorporation of Risk Measures in Optimization 
3.7.3. Portfolio Optimization Tools 

3.8. Simulation of Financial Risks 

3.8.1. Simulation Methods for Risk Management 
3.8.2. Application of Monte Carlo Simulations in Finance 
3.8.3. Implementation of Simulations with Python 

3.9. Continuous Risk Assessment and Monitoring 

3.9.1. Continuous Risk Assessment Techniques 
3.9.2. Risk Monitoring and Reporting Tools 
3.9.3. Implementation of 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 benefit from an enjoyable learning experience through the didactic formats offered by this program, such as the explanatory video or the interactive summary”