Applied Machine Learning in Finance: Transforming Insights into Action

Analytics & Tech
Finance & Investment
Innovation & Business Improvement

Next Intake: 12 Aug 2025 (Tue)

Intermediate
SkillsFuture
Short Courses

This module is conducted in-person.

Next course starts on

12 Aug 2025 (Tue) See Full Schedule

Fee

SGD3,270* (as low as SGD381 after maximum funding) Learn More

Duration

3 Full Day (Weekdays)

Level

Intermediate

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%
+ ETSS Funding 20%)

$981

(After SSG Funding 70%)

Singapore Citizen ≥ 40 years old

$381

(After SSG Funding 70%
+ MCES Funding 20%)

$381

(After SSG Funding 70%
+ ETSS Funding 20%)

$381

(After SSG Funding 70%
+ MCES Funding 20%)

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