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Identifying Credit Risk Using Machine Learning

Date

17 November 2017

Time

9.00 am to 5.00 pm

Venue

Data Analytics Consulting Centre
Department of Statistics and Applied Probability
Computer Laboratory 2 (S16-05-102)
Faculty of Science
6 Science Drive 2
Singapore 117546

Overview

With the advancement of technology and Big Data, businesses are building more complex techniques and algorithms to identify credit risk. Using machine learning techniques, businesses are now able to build credit risk models which enable them to identify customers who are likely to be a credit risk. They can therefore make smarter credit decisions in a timely manner.

Who Should Attend?

If you are required to undertake business analytics projects or apply analytics and machine learning techniques for improved business processes and decision-making, this course is for you!


• Credit risk modelling analysts 
• Data analysts 
• Data engineers 
• Data managers 
• Financial analysts
• Machine learning scientists 
• Statisticians
• Technology professionals


This hands-on course uses Python programming for building credit risk models. Participants should be familiar with computers and basic statistics.

Outcomes

Participants will acquire the following skills at the end of the course:

Applying machine learning techniques to identify high credit risk customers

Determining the probability of a customer being a high credit risk

Running a machine learning algorithm using Python Programming

Understanding the factors that may influence credit risk

Understanding the power of machine learning and its impact on business

Understanding how to evaluate and interpret credit risk model outcomes

COURSE FEES

Participants will enjoy an introductory course fee of $700 (excluding GST). 
This includes course materials, lunch and tea breaks.

Early bird

$ 630
(excluding GST) 
  • Before 17 October 2017

Participants who complete the workshop will be awarded a Certificate of Participation.

NUS Faculty of Science reserves the right to postpone or cancel the workshop by giving seven (7) days notice prior to the start of the workshop.

If the Faculty cancels the course, you will be notified and refunded your course fees in full.

PROGRAMME

The course will cover the following topics:

• Overview of machine learning
• Importance of machine learning
• Impact of machine learning on businesses
• Types of machine learning techniques
• Overview of credit risk
• Business impact of the credit risk model
• Linear Regression method 
• Decision Tree method  


The course will include hands-on practicals on the application of Regression Models and Decision Trees.

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