Applied Machine Learning in Finance: Transforming Insights into Action
- Analytics & Tech
- Finance & Investment
- Innovation & Business Improvement
This module is conducted in-person.
3 Full Day (Weekdays)
Who Should Attend
- Portfolio Managers, Data Scientists in Finance, Investment Professionals and Data Analysts.
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, such as interest, compounding, and investment strategies, is recommended to understand the financial applications of machine learning.
- Familiarity with data handling, particularly using tools like Excel or Python for basic data processing.
Overview
Machine learning is revolutionising financial services, offering unprecedented opportunities for innovation, efficiency, and decision-making. From fraud detection and credit scoring to algorithmic trading and personalised customer experiences, machine learning is reshaping the way financial institutions operate. By leveraging advanced algorithms and data-driven insights, financial organisations can uncover patterns, predict trends, and optimise processes with precision and speed. This course dives into the transformative potential of machine learning, equipping participants with the skills to apply these techniques to real-world financial challenges. Combining foundational concepts with practical exercises, it prepares participants to harness machine learning for sharper analytics, smarter risk evaluation, and a competitive edge in the rapidly evolving financial landscape.
Learning Objectives
At the end of the 3-day course, participants will be able to:
- Understand the underlying mechanisms of various machine learning models
- Apply machine learning techniques, such as regression, clustering, and neural networks, to solve practical financial problems
- Evaluate and compare different machine learning models to select the most appropriate for specific financial applications
- Extract insights from machine learning models and effectively communicate these insights to non-technical stakeholders
- Use popular machine learning tools and packages to develop predictive models and uncover hidden patterns in financial data
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)
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