Online or onsite, instructor-led live Finance training courses demonstrate through interactive discussion and case studies the fundamentals of Finance and Accounting.
Finance training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Oregon onsite live Finance trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
OR, Portland - World Trade Center
21 One World Trade Center, 121 Southwest Salmon Street, Portland, united states, 97204
The venue is located in the World Trade Center across the street from Banner Bank.
This instructor-led, live training in Oregon (online or onsite) is aimed at beginner-level banking professionals who wish to gain in-depth knowledge and skills in using the SWIFT network for handling international bank transfers.By the end of this training, participants will be able to:
Understand the SWIFT network and decode SWIFT codes.
Navigate SWIFT messages and execute international transfers.
Learn about the potential risks associated with international transfers and how to mitigate these risks.
Explore advanced SWIFT services and understand how they improve the speed, transparency, and traceability of cross-border payments.
This instructor-led, live training in Oregon (online or onsite) is aimed at finance and legal professionals who wish to learn the principles and practices of extrajudicial and judicial collection, enabling them to effectively manage and resolve collection issues.By the end of this training, participants will be able to:
Understand the advantages and disadvantages of extrajudicial and judicial collection.
Apply the main methods and techniques of extrajudicial and judicial collection.
Evaluate the effectiveness and efficiency of extrajudicial and judicial collection.
Deal with the legal and ethical issues involved in extrajudicial and judicial collection.
Integrate extrajudicial and judicial collection in a comprehensive and coherent way.
During the training, will present the issues of financial analysis using the advanced features of Excel.
This course is intended for financial analysts, accountants and all those who want to expand their skills spreadsheet with issues of financial analysis.
R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems.
By the end of this training, participants will be able to:
Understand the fundamentals of the R programming language
Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
Build applications that solve problems related to asset allocation, risk analysis, investment performance and more
Troubleshoot, integrate deploy and optimize an R application
Audience
Developers
Analysts
Quants
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Note
This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.
Python is a programming language that has gained huge popularity in the financial industry. Adopted by the largest investment banks and hedge funds, it is being used to build a wide range of financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to use Python to develop practical applications for solving a number of specific finance related problems.
By the end of this training, participants will be able to:
Understand the fundamentals of the Python programming language
Download, install and maintain the best development tools for creating financial applications in Python
Select and utilize the most suitable Python packages and programming techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
Build applications that solve problems related to asset allocation, risk analysis, investment performance and more
Troubleshoot, integrate, deploy, and optimize a Python application
Audience
Developers
Analysts
Quants
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Note
This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.
kdb+ is an in-memory, column-oriented database and q is its built-in, interpreted vector-based language. In kdb+, tables are columns of vectors and q is used to perform operations on the table data as if it was a list. kdb+ and q are commonly used in high frequency trading and are popular with the major financial institutions, including Goldman Sachs, Morgan Stanley, Merrill Lynch, JP Morgan, etc.
In this instructor-led, live training, participants will learn how to create a time series data application using kdb+ and q.
By the end of this training, participants will be able to:
Understand the difference between a row-oriented database and a column-oriented database
Select data, write scripts and create functions to carry out advanced analytics
Analyze time series data such as stock and commodity exchange data
Use kdb+'s in-memory capabilities to store, analyze, process and retrieve large data sets at high speed
Think of functions and data at a higher level than the standard function(arguments) approach common in non-vector languages
Explore other time-sensitive applications for kdb+, including energy trading, telecommunications, sensor data, log data, and machine and network usage monitoring
Audience
Developers
Database engineers
Data scientists
Data analysts
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
The Compliance and MLRO Refresher Programme examines the key risk management issues and topics that are of vital importance in today’s highly-regulated environment. As well as being targeted at Compliance Officers, MLROs, MLCOs and other risk management professionals it is also aimed at members of senior management and board members keen to know more about what to expect from the risk control functions within their organisations. The Programme is lectured by subject-matter-expert from the UK.
The key learning objective of the Programme is to equip attendees with sufficient knowledge to assess objectively the adequacy of their organisation’s existing risk management controls and practices and to make appropriate enhancements.
Audience
All staff needing a working knowledge of Corporate Governance for their organisation
Format of the course
A highly-interactive combination of:
Support staff responsible for gathering and interpreting information for the lending managers
Staff responsible for the management of bad and doubtful debts who need a working knowledge of the decision-making process which led to the lending being made
Lending to Personal Customers – Consumer Lending – demands a high-level of skill in the assessment of individual lending proposals.
In many cases it has none of the sources of financial information traditionally associated with Corporate Lending – Balance Sheets, Profit & Loss Accounts etc. – and relies more on the trust and rapport built up between the customer and the lender.
By the end of this course lenders to Personal Customers will be able to:
Understand the process for assessing lending propositions from Personal Customers
Utilise that process to come to a logical decision to agree to the loan or to decline it with robust reasons
Manage and control a Personal Lending portfolio to ensure, as far as possible, that all loans are repaid in full. (Remembering that there’s no completely risk-free lending…!)
Build rapport with customers to (try to!) ensure that all their loans are fully repaid
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model.
By the end of this training, participants will be able to:
Understand the fundamental concepts of deep learning
Learn the applications and uses of deep learning in finance
Use R to create deep learning models for finance
Build their own deep learning stock price prediction model using R
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.
In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model.
By the end of this training, participants will be able to:
Understand the fundamental concepts of deep learning
Learn the applications and uses of deep learning in banking
Use Python, Keras, and TensorFlow to create deep learning models for banking
Build their own deep learning credit risk model using Python
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model.
By the end of this training, participants will be able to:
Understand the fundamental concepts of deep learning
Learn the applications and uses of deep learning in banking
Use R to create deep learning models for banking
Build their own deep learning credit risk model using R
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.
In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model.
By the end of this training, participants will be able to:
Understand the fundamental concepts of deep learning
Learn the applications and uses of deep learning in finance
Use Python, Keras, and TensorFlow to create deep learning models for finance
Build their own deep learning stock price prediction model using Python
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training in Oregon (online or onsite) is aimed at financial professionals in US and non-US institutions who wish to understand the legal, compliance and enforcement aspects of FATCA so as to maintain compliance with US tax authorities (IRS).
By the end of this training, participants will be able to understand:
FATCA's impact on global tax compliance and financial transparency.
FATCA's due diligence and reporting requirements.
FATCA's most important legal aspects and how to ensure compliance.
This introductory course will provide participants with a first class and detailed working knowledge of the key financial markets, their purpose, function, main activities and their regulation. It is intended to be part refresher, part educational and part challenging so that all delegates will derive the maximum benefit from it. Feedback and discussion will be actively encouraged throughout the sessions which are intended to be interactive not just reactive and factual.
The primary function is to ensure that by completion, all course delegates will be much better equipped to deal with clients and their ongoing needs and to put into context the services and markets in which they are trading and participating.
Introduction to Structured Products
The purpose of the course is to provide delegates with an introduction to the Structured Products used in investment banking. On completion of the course all delegates will have a working knowledge of the subject and will be able answer
What are structured products?
Why issue them?
How do issuers and investors benefit?
How do you structure and price a range of derivative products?
What are the risks and costs of producing structured financial products?
This instructor-led, live training in Oregon (online or onsite) is aimed at managers who wish to gain a working understanding the IFRS 17 standard.
By the end of this training, participants will be able to:
Identify the key requirements of IFRS 17.
Know the differences between IFRS 17 and IFRS 4.
Understand IFRS rules.
Understand financial models and their numbers.
Dissect and understand different types of insurance contracts and accounting models.
Prepare a well-informed transition plan and schedule.
Implement IFRS 17 within an organization.
Identify and measure insurance contract performance.
Audiance
All staff who need a working knowledge of Compliance and the Management of Risk
Format of the course
A combination of:
Facilitated Discussions
Slide Presentations
Case Studies
Examples
Course Objectives
By the end of this course, delegates will be able to:
Understand the major facets of Compliance and the national and international efforts being made to manage the risk related to it
Define the ways in which a company and its staff might set up a Compliance Risk Management Framework
Detail the roles of Compliance Officer and Money Laundering Reporting Officer and how they should be integrated into a business
Understand some other “hot spots” in Financial Crime – especially as they relate to International Business, Offshore Centres and High-Net-Worth Clients
This course provides a comprehensive introduction to the MATLAB technical computing environment + an introduction to using MATLAB for financial applications. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include:
Working with the MATLAB user interface
Entering commands and creating variables
Analyzing vectors and matrices
Visualizing vector and matrix data
Working with data files
Working with data types
Automating commands with scripts
Writing programs with logic and flow control
Writing functions
Using the Financial Toolbox for quantitative analysis
Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. R will be used as the programming language.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects.
Audience
Developers
Data scientists
Banking professionals with a technical background
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
By the end of this training, participants will be able to:
Understand the fundamental concepts in machine learning
Learn the applications and uses of machine learning in finance
Develop their own algorithmic trading strategy using machine learning with Python
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
By the end of this training, participants will be able to:
Understand the fundamental concepts in machine learning
Learn the applications and uses of machine learning in finance
Develop their own algorithmic trading strategy using machine learning with R
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training in Oregon (online or onsite) is aimed at finance managers, controllers, and accountants who wish to explore QAD's advanced features to manage and report financials.
By the end of this training, participants will be able to manage, report, consolidate, and streamline manufacturing accounts and financial data.
R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn the fundamentals of R programming as they walk through coding in R using financial examples.
By the end of this training, participants will be able to:
Understand the basics of R programming
Use R to manipulate their data to perform basic financial operations
Audience
Programmers
Finance professionals
IT Professionals
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training in Oregon (online or onsite) is aimed at developers who wish to monetize a website or web application using the Stripe API.
By the end of this training, participants will be able to:
Set up the necessary development environment to start developing.
Build an application that integrates payment processing features such as Checkout, Payment Intents, and Billing.
Audience
All Senior Management who need a working knowledge of AML / CTF and their prevention – and an awareness of the other relevant and current Financial Crime issues;
Format of the course
A combination of:
Facilitated Discussions
Slide Presentations
Case Studies
Examples
Course Objectives
By the end of this course, delegates will be able to:
Explain how AML and CTF might be prevented
Understand the major facets of AML and CTF as they apply to their companies and the national and international efforts being made to combat them
Define the ways in which a company and its staff should protect themselves against the risks of Money Laundering and Terrorist Financing
Detail how a company might become a target for Money Laundering and Terrorist Financing: and explain which “red flags” might help them to identify, prevent and report any (suspicious or actual) criminal activity
Understand some of the other “hot spots” in Financial Crime
The Common Reporting Standard (CRS) is an OECD standard which calls on international jurisdictions to obtain information from their financial institutions and automatically exchange that information with other jurisdictions on an annual basis. The Standard requires all reports to be sent electronically in a format known as CRS XML.
To resolve errors related to file preparation and incomplete or inaccurate record information, the CRS Status Message XML Schema was created to check for file and record errors in the CRS XML Schema file.
In this instructor-led, live training, participants will learn:
the basic structure of the CRS XML Schema and CRS Status Message XML Schema.
steps for processing these files.
steps for converting Excel files to XML schema files.
steps for filing with their respective jurisdiction.
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training (online or onsite) is aimed at managers who wish to identify and minimize the risk of fraud faced by eCommerce merchants.
By the end of this training, participants will be able to:
Understand how eCommerce fraud occurs.
Analyze vulnerabilities in their company's eCommerce platform and processes.
Assess a company's 'readiness' for adopting new anti-fraud measures
Communicate and incorporate appropriate anti-fraud policies.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
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Testimonials (7)
Instructor interest in our dashboard to provide suggestions.
ELLEN CAROLINE SANTOS RIBEIRO - Aché Laboratório Farmacêuticos S.a
Course - FinOps
Machine Translated
The pricing strategies. Need to have more real case examples on the strategies and the pricing methods.
Ruziham A Razak - Telekom Malaysia Berhad
Course - A Practical Guide to Successful Pricing Strategies
The trainer was very knowledgeable and well prepared. I think we was very well capable to prepare a training that was suitable for our needs.
Sophia van den Broek - Triple Jump
Course - Corporate Governance
Personal service and orientated to my needs
ANN - New Vitality Clinic
Course - GnuCash for Business Accounting
I generally enjoyed the activity after each topic.
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