Open Bank Project for Developers Training Course

This instructor-led, live training in Estonia (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.

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:

• Facilitated Discussions
• Slide Presentations
• Examples
• Exercises
• Case Studies

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

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

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.

This instructor-led, live training in Estonia (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

1. What are structured products?
2. Why issue them?
3. How do issuers and investors benefit?
4. How do you structure and price a range of derivative products?
5. What are the risks and costs of producing structured financial products?
6. What are embedded derivatives?
7. What are exotic options?
8. What are the pricing and hedging considerations?

This instructor-led, live training in Estonia (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.

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

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