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BusinessFinance and Investment

Executive Certificate in Financial Intelligence from Big Data
行政人員證書《智能金融大數據》

New Course
Course Code
EP074A
Application Code
1490-EP074A (Jun intake)
1535-EP074A (Jul intake)
1540-EP074A (Aug intake)... more Application Code
Study mode
Part-time
Start Date
03 Jun 2017 (Sat)
Next intake(s)
July 2017
Duration
4 months
Language
English
Course Fee
HK$8400 per programme. Course fees are non-refundable and subject to change without prior notice.
Apply Now
Deadline on 23 May 2017 (Tue)
Enquiries
2520 4612
2858 4750
騰訊主席馬化騰︰「未來就是在雲端用人工智能處理大數據」. 他指出,目前數碼經濟規模多達23萬億元人民幣,預料數碼經濟佔整體經濟增長的31%

Even CFA exams tests you Big Data and Artificial Intelligence, what are you waiting for?

This programme aims to provide students with market knowledge in Big Data for Banking and Financial Services and to develop students’ skill sets in setting up hypothesis models in Predictive Analytics. The programme contents cover challenges and limitations in using Big Data in Banking and Financial Services.

Whitepaper - Tips to succeed with Big Data by Tableau

Bloomberg: CFA Exam to Add Big Data, Artificial Intelligence as Topics (May 10, 2017)

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SFC: Fintech Contact Point

Big Data in Financial Services

Big data describe a situation where the dataset being so big that cannot be processed with traditional methods for accurate and timely decision making, it is characterized by 5 Vs: the volume, velocity, variety, veracity and variability. Big Data is about data management where enterprises can be proactively using predictive approach to extract business value.  Banking and Financial Services are amongst the most data driven industries where they can grow their comparative advantage with the potential of big data.

Under continuous competition, Financial Services set their business objective to seek ways to improve operation efficiency and business performance, through better risk management and customer relationships to gain revenue growth and higher margins. The risks facing by the financial services industry are the increased regulatory requirements, compliance issue and the cyber security threat.  Hence, it is important for Banking and Financial Services to take a proactive approach to gain better insight into their enterprise information to drive business decisions, improve efficiency, mitigate risk and derive value from their enterprises’ big data.

On completion of the programme, students should be able to:

1.      apply the Big Data concept and data scientist skill sets to enhance business value; 

2.      explore how predictive models reduce business risk and improve business profitability and efficiency; 

3.      examine the Big Data handling process with ethical issues of Big Data collection and privacy protection within the financial services; 

4.      access Big Data resources for building and implementing strategic decisions with predictive analytics. 

Duration
  • 15 hours per module
  • 2 meeting(s)
  • 7.5 hours per meeting
Venue Tutor
  • Mr.  Lo is currently an investment manager for a Chinese-based fund management firm in Hong Kong. He has more than 20 years of experience in the financial industry. He is also a CFA charteredholder.  Deep in his heart, he is a Big Data enthusiast. Currently, he is leading Big Data effort in his firm's Hong Kong office. He is a graduate of the Northwestern University's Master of Analytics program, one of the premier master’s program in Big Data.   His past projects include improving DVD rental performance for the largest DVD rental kiosks company in the US, creating new credit card default model based on more than 2,000 factors.
  • Dr. Ng had been senior executive of Digital MNC, including the first online Agency in the States. Launched the first broadband e-commerce, 4GLte and o2o Apps in Asia, the first mobile eShop, and also launched other brands included Apple iphone and other luxury brands. Dr. Ng engaged as guest lecturer and consultant for various domains, including local governments, NGO, corporates and startups in the region over 12 years.
  • Mr. Hung, Data Analytics Manager APAC at an international insurance firm, is a seasoned Data Analytics professional with more than 10 years of project experience in data analytics (Fraud Risk Analytics / Customer Analytics / Performance Management / Balance Scorecard / Business Intelligence / Data mining / Big data) solution design and development. He has in-depth knowledge and experience in the Banking and Insurance industries and proven track record of shaping and delivery complex large-scale data analytics projects. Mr. Hung received an MBA from CUHK and is a Certified Project Management Professional (PMP).

1. Big Data for Financial Services

The objective of this module is to introduce students to the real world use of Big Data in banking and financial services. Topics include introduction to Big Data, challenges and opportunities in Big Data for Financial Services.

Content:

A. Defining Big Data – volume; velocity; variety; variability and veracity
B. Big Data landscape
C. Business implications of Big Data
D. Technology implications of Big Data
E. Management and Storage of Big Data – cloud storage
F. Business Challenges and Big Data Opportunities in Financial Services and Banking

 

2. Fraud Prevention, Governance and Compliance in Big Data

The objective of this module is to provide an overview of Big Data potential in fraud detection and prevention. Key concepts include Big Data governance and compliance regulatory requirement, best practices in data protection and risk management; case studies will be used to explain the importance of fraud detection and prevention in reducing company losses.

Content:

A. Understanding Open Nature of Internet architecture design and Security exposures
B. Strategic Strategy on Cyber Crime protection : From What, how they will steal?  To Ultimate Prevention Strategy
C.  Real defenses from attackers mindset
D. The latest fraud detection and prevention technologies: Latest Cost effective measures in defending  vulnerable breaches (possible industry panels with key security players)
E. Devils on hands: Case Study on how personal Digital practices impact on last America Presidential election: Trump vs Hillary
F. Compliances, Codes of Ethics and Risk-based Mindset trainings

G. Case Study: Apple vs FBI on Ethical privacy vs Cybercrime footprints

 

3. Predictive Analytics for Financial Services

The objective of this module is to provide an overview of data mining process and to pinpoint the potential and limitations of predictive analytics within a business setting.  Hands on computer laboratory experience will be provided.

Content:

A. Overview in predictive analytics
B. Analytics framework and tools
C. Data Modeling methodology
D. Business Application of analytics 

 

Assessment:
Continuous Assessment: In-class exercise (30%)
Final Assessment: Assignment/Case Study (70%)
Grade:PASS/FAIL

Big Data for Financial Services (M64649)

Lecture 1  3 June, 2017 (Sat)     10:00am - 1:30pm
2:30pm - 6:30pm
Lecture 2 10 June, 2017 (Sat)     10:00am - 1:30pm
2:30pm - 6:30pm

Remarks: Tentative timetable is subject to change and course commencement is subject to sufficient enrollment numbers.

 

Fraud Prevention, Governance and Compliance in Big Data (M64652)

Lecture 1  8 Jul, 2017 (Sat)     10:00am - 1:30pm
2:30pm - 6:30pm
Lecture 2 15 Jul, 2017 (Sat)     10:00am - 1:30pm
2:30pm - 6:30pm

Remarks: Tentative timetable is subject to change and course commencement is subject to sufficient enrollment numbers.

 

Predictive Analytics for Financial Services (W06421)

Lecture 1  5 Aug, 2017 (Sat)     10:00am - 1:30pm
2:30pm - 6:30pm
Lecture 2 12 Aug, 2017 (Sat)     10:00am - 1:30pm
2:30pm - 6:30pm

Remarks: Tentative timetable is subject to change and course commencement is subject to sufficient enrollment numbers.

Applicants shall hold:

a)    a bachelor’s degree awarded by a recognized University or equivalent; or

b)   an Associate Degree/ a Higher Diploma or equivalent, and have at least 2 years of working experience; or

c)    a recognized professional qualification.

Applicants with other qualification and substantial senior level work experience will be considered on individual merit.

Application Fee

HK$150 (Non-refundable)

Course Fee
  • HK$8400 per programme. Course fees are non-refundable and subject to change without prior notice.

Application Form Application Form

Enrolment Method

Application Procedure
Applicant should submit the following:
1. Application form
2. Application fee HK$150 (non-refundable)
3. Certified true copy of academic/ professional qualification (Applicant can bring along his/her original and copy of supporting documents to enrolment counter for certifying true copy).
4. HKID copy

Admission is on a first-come, first-served basis. Enrolment will be confirmed once you have made the payment. You will receive a payment confirmation after payment has been made processed successfully. You are advised to keep your payment confirmation for future enquiries.

Upon successful admission into the programme, student is require to follow enrolment procedure:
1.    Enrolment form
2.    Enrolment fee

Late applications /enrolments are subject to program leader’s approval and possible late payment charges.

Payment Method

Payment can be paid by cheque payable to HKU SPACE or by credit card pay at enrollment counter.