Finance Training Courses

Finance Training Courses

Local, instructor-led live Finance training courses demonstrate through interactive discussion and case studies the fundamentals of Finance and Accounting.

Finance training is available as "onsite live training" or "remote live training". Onsite live Finance trainings in Hong Kong can be carried out locally on customer premises or in NobleProg corporate training centers. Remote live training is carried out by way of an interactive, remote desktop.

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Finance Course Outlines

Title
Duration
Overview
Title
Duration
Overview
14 hours
Overview
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
21 hours
Overview
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
35 hours
Overview
Overview

Across the globe regulators are increasingly linking the amount of risk taken by a bank to the amount of capital it is required to hold and banks and financial services are increasingly being managed on risk-based management practices. The banks, their products, the regulations and the global market are becoming increasingly complex, driving ever greater challenges in effective risk management. A key lesson of the banking crisis of the last five years is that risks are highly integrated and to manage them efficiently banks have to understand these interactions.

Key features include:

- the explanation of the current risk-based regulations
- detailed review of the major risks faced by banks
- industry best practices for adopting an enterprise approach to integrating risk management across an entire organisation
- using governance techniques to build a group wide culture to ensure everyone takes an active role in managing risks in line with the banks strategic objectives
- what challenges could be faced by risk managers in the future.

The course will make extensive use of case studies designed to explore, examine and reinforce the concepts and ideas covered over the five days. Historical events at banks will be used throughout the course to highlight how they have failed to manage their risks and actions that could have been taken to prevent loss.

Objectives

The objective of this course is to help bank management deliver an appropriate integrated strategy for managing the complex and changing risks and regulations in today’s international banking environment. Specifically this course aims to give senior level management an understanding of:

- major risk within the financial industry and the major international risk regulations
- how to manage a bank’s assets and liabilities whilst maximising return
- the interaction between risk types and how banks use an integrated approach for their management
- corporate governance and the best practice approaches to managing the diverse interests of the stakeholders
- how to develop a culture of risk governance as a tool for minimising unnecessary risk taking

Who should attend this seminar

This course is intended those who are new to integrated risk management, senior management responsible strategic risk management, or those who wish to further their understanding of enterprise risk management. It will be of use to:

- Board level bank management
- Senior managers
- Senior risk managers and analysts
- Senior directors and risk managers responsible for strategic risk management
- Internal auditors
- Regulatory and compliance personnel
- Treasury professionals
- Asset and liability managers and analysts
- Regulators and supervisory professionals
- Suppliers and consultants to banks and the risk management industry
- Corporate governance and risk governance managers.
21 hours
Overview
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
21 hours
Overview
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
35 hours
Overview
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
21 hours
Overview
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
28 hours
Overview
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
28 hours
Overview
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
21 hours
Overview
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?
- What are embedded derivatives?
- What are exotic options?
- What are the pricing and hedging considerations?
7 hours
Overview
This course is aimed at Project Managers and those interested in Risk Management within Projects.
7 hours
Overview
Why should you attend?

We live and work in an increasingly global market which offers opportunities at every turn which we need to take. However, with those opportunities come increased competition and most of the complexities remain in place. Knowing the best ways to find and select the most suitable and competitive price for your product or services is crucial to the success and growth of any business. The knowledge, techniques and strategies that you gain during our training will enable you to increase profitability and effectiveness of your business.

Turning theory into practice:

The purpose behind this 1-day training course is to simplify and explain the successful strategies of pricing. You’ll leave our event more confident and more positive to continue and expand your business. We will provide you with the strategies and the best practice examples to improve and expand your knowledge about pricing and to make your plans and targets come true.

Who should attend?

This course is designed for sales managers, marketing managers, product managers, account managers, pricing analysts & managing directors. This course is also vital for executives who are new to pricing or who have limited experience of the subject.
35 hours
Overview
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.
21 hours
Overview
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 basics of financial trading as they step through building and implementing basic trading strategies and actions in R using quantstrat.

By the end of this training, participants will be able to:

- Understand the fundamental concepts in trading
- Create and implement their first trading strategy using R
- Analyze the performance of their strategy using R

Audience

- Programmers
- Finance professionals
- IT Professionals

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Overview
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.
21 hours
Overview
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
21 hours
Overview
Strategic Thinking refers to the process of thinking through problems and situations through a big picture lens, considering the implications of various actions, then acting upon the most ideal strategy for overall business success.

Good strategic thinkers look at challenges and opportunities analytically and consider them within the context of a company's vision and goals. Whether used by a strategy manager considering how to allocate an investment or a business developer evaluating the potential of expanding into a new market, strategic thinking is a skill that can be learned, practiced, and refined. Strategic thinking gives managers the clarity and edge needed to make better decisions and continuously move their companies in right direction.

In this instructor-led, live training, we walk new managers through the most important concepts of strategic thinking, putting theory into practice through task-based activities based on real-life cases. Participants get a unique opportunity to learn new ways of thinking, implement key concepts to solve problems, reflect on how these principles apply to their own work, and listen to and share their own perspectives with their instructor and class peers.

Format of the course

- Participants will gain a big-picture understanding of what drives their business and how their role within the organization contributes to the business's goals, vision, and bottom line. The course is interactive and activity-based, with case-studies, team-based tasks and individual presentation and problem-solving making up an important part of the course.
21 hours
Overview
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 advanced programming concepts in R as they walk through coding in R using financial examples.

By the end of this training, participants will be able to:

- Implement advanced R programming techniques
- Use R to manipulate their data to perform more advanced financial operations

Audience

- Programmers
- Finance professionals
- IT Professionals

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Overview
Course goal:

To ensure that an individual has the core understanding of GRC processes and capabilities, and the skills to integrate governance, performance management, risk management, internal control, and compliance activities.

Overview:

- GRC Basic terms and definitions
- Principles of GRC
- Core components, practices and activities
- Relationship of GRC to other disciplines
7 hours
Overview
Chain Core is an open-source platform written in Go that allows finance companies to operate a Blockchain network or connect to other networks for the purpose of transferring financial assets over the Blockchain.

In this instructor-led, live training, participants will learn the core principles behind Blockchain and the distributed ledger as they build a prototype Blockchain network using Chain Core Developer Edition.

By the end of this training, participants will be able to:

- Operate and participate in a permissioned blockchain network
- Understand Chain Core's key concepts, including its cryptographically-secured multi-asset shared ledger
- Issue digital assets directly to custodians, who can then transfer them to each other in real time with no transactional intermediary
- Issue digital assets that represent units of value such as currencies, bonds, securities, IOUs, or loyalty points
- Is Chain's high-level language, Ivy, and Ivy Playground, to express contracts that protect value on a blockchain.

Audience

- Developers

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Overview
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
21 hours
Overview
Audience

All staff who need a working knowledge of Compliance and the Management of Risk for companies doing business in People's Republic of China.

It can be tailored to deal with specific regional laws (e.g. company head-quartered in Germany but operating in China).

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 of the other “hot spots” in Financial Crime – especially as they relate to International Business, Offshore Centres and High-Net-Worth Clients
7 hours
Overview
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.
7 hours
Overview
Audience

All staff needing a working knowledge of Corporate Governance for their organisation

Format of the course

A highly-interactive combination of:

- Facilitated Discussions
- Slide Presentations
- Examples
- Exercises
- Case Studies
21 hours
Overview
Audience

- Newly-appointed Personal Lending Managers
- 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
14 hours
Overview
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 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

Audience

- Financial institution managers
- Regulatory compliance managers and consultants
- Tax administrators
- IT support engineers

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Overview
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
28 hours
Overview
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
7 hours
Overview
GnuCash is an open-source, double-entry financial accounting application.

In this instructor-led, live training, participants will learn how to use GnuCash to manage business accounting and finance projects and tasks.

By the end of this training, participants will be able to:

- Manage financial and accounting tasks including invoicing, billing, payments, VAT, reporting, etc.
- Track bank accounts, stocks, income, and expenses
- Track multiple accounts in a multiple-department business
- Use GnuCash to manage multi-currency business accounts

Audience

- Accountants
- Bookkeepers
- Business owners

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Overview
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

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