Financial Risk Management with Machine Learning
- Analytics & Tech
- Finance & Investment
- Innovation & Business Improvement
This module is conducted in-person.
3 Full Day (Weekdays)
Who Should Attend
- Risk Managers, Quantitative Analysts, Portfolio Managers, Data Scientists in Finance, Traders and Hedge Fund Managers
PREREQUISITES
- Participants are encouraged to have a fundamental understanding of Python or R, and those without programming experience should consider taking introductory online courses.
- Basic knowledge of financial concepts, including familiarity with financial instruments, credit, and market risk, is recommended.
- Fundamental understanding of statistical concepts like mean, variance, and correlation is essential for implementing risk models and machine learning algorithms.
Overview
Amidst the challenges of a rapidly evolving financial landscape, effective financial risk management has become essential for organisations seeking to navigate uncertainties and safeguard their assets against credit and market risks. This course aims to equip participants with a comprehensive understanding of financial risk management, focusing on credit and market risks, while incorporating machine learning techniques to enhance the modeling and management of these risks. By combining foundational risk management principles with hands-on applications using Python, the course provides a balance between academic theory and practical implementation.
Learning Objectives
At the end of the 3-day course, participants will be able to:
- Understand the fundamentals of financial instruments, risk-return tradeoffs, and portfolio theory
- Measure and manage credit and market risks using traditional models and advanced machine learning techniques
- Apply machine learning models, such as regression, clustering, and neural networks, to improve risk prediction, credit scoring, and portfolio optimisation
- Implement Value at Risk (VaR), Expected Shortfall (ES), and other risk measures using Python
- Use Python for portfolio optimisation, risk modeling, and stress testing, incorporating modern risk-adjusted performance measures
- Leverage machine learning for risk-aware decision-making
Assessment
As part of the requirement for SkillsFuture Singapore, there will be an assessment conducted at the end of the course. The mode of assessment, which is up to the trainer’s discretion, may be an online quiz, a presentation or based on classroom exercises.
Participants are required to attain a minimum of 75% attendance and pass the associated assessment in order to receive a digital Certificate of Completion issued by Singapore Management University.
Calculate Programme Fee
Fee Table
| COMPANY-SPONSORED | |||
|
PARTICIPANT PROFILE |
SELF-SPONSORED |
SME |
NON-SME |
|
Singapore Citizen < 40 years old Permanent Resident LTVP+
|
$981 (After SSG Funding 70%) |
$381 (After SSG Funding 70% |
$981 (After SSG Funding 70%) |
|
Singapore Citizen ≥ 40 years old |
$381 (After SSG Funding 70% |
$381 (After SSG Funding 70% |
$381 (After SSG Funding 70% |
|
International Participant |
$3,270 (No Funding) |
$3,270 (No Funding) |
$3,270 (No Funding) |
All prices include 9% GST
Please note that the programme fees are subject to change without prior notice.
Post Secondary Education Account (PSEA)
PSEA can be utilised for subsidised programmes eligible for SkillsFuture Credit support. Click here to find out more.
Self Sponsored
SkillsFuture Credit
Singapore Citizens aged 25 and above may use their SkillsFuture Credits to pay for the course fees. The credits may be used on top of existing course fee funding.
This is only applicable to self-sponsored participants. Application to utilise SkillsFuture Credits can be submitted when making payment for the course via the SMU Academy TMS Portal, and can only be made within 60 days of course start date.
Please click here for more information on the SkillsFuture Credit. For help in submitting an SFC claim, you may wish to refer to our step-by-step guide on claiming SkillsFuture Credits (Individual).Workfare Skills Support Scheme
From 1 July 2023, the Workfare Skills Support (WSS) scheme has been enhanced. Please click here for more details.
Company Sponsored
Enhanced Training Support for SMEs (ETSS)
- Organisation must be registered or incorporated in Singapore
- Employment size of not more than 200 or with annual sales turnover of not more than $100 million
- Trainees must be hired in accordance with the Employment Act and fully sponsored by their employers for the course
- Trainees must be Singapore Citizens or Singapore Permanent Residents
- Trainees must not be a full-time national serviceman
- Trainees are eligible for ETSS funding only if their company's SME status is approved prior to the course commencement date. To verify your SME's status, please click here.
Please click here for more information on ETSS.
Absentee Payroll
Companies who sponsor their employees for the course may apply for Absentee Payroll here. For more information, please refer to:
AP Guide (Non-SME Companies)
Declaration Guide (SME Companies)
Loading schedule information...
