The Webinar
named “Application of machine learning in the financial industry (機器學習在金融業的應用)” was organized by Minitab on 25th
Aug 2020. In the beginning, Mr. Chai Lei (柴磊) was
the speaker.
In
the beginning, Mr. Chai Lei introduced the evolution of Bank from 1.0 to
4.0. In Covid-19 period, Bank 4.0 was
enhanced and accelerated such as customer contact.
Risk
control was accepted significantly. Risk
control was based on rules in the past. Mr. Chai explain how to use big data to
reduce loss and increase customer.
Then
he compared the traditional statistical method and the new method using machine
learning (ML). ML could solve high
dimensions problem, non-linear model problem and complex human behavior problem.
After
that Mr. Chai pointed out three critical factors for implementation of Machine
Learning. Firstly, it needs scenario.
Secondly, structural history data should be available. Thirdly, talents and technical tools should
be in place.
Finally, Mr. Chai introduced some case studies and the first one was Visa Card Behavioral Scorecard. He showed the raw data and explain the scenario.
Then he demonstrated how to build model using financial data (~12000 customers) in Salford Predictive Modeler (SPM). CART Decision Tree was employed for analysis.
Green
line is recommended by SPM and the CART Decision Tree is suitable for analysis.
After
that Mr. Chai showed the simple one in the beginning of the tree point.
TreeNet
Gradient Boosting was then introduced.
TreeNet (ROC ~ 0.92) is more accuracy than CART Decision Tree (ROC ~
0.86). TreeNet studied 44 variable and reduced
to 16 variable that were significant to risk.
Lastly,
Mr. Chai performed the second case study about Bank Cross-sell with 40700 data. He said 0 and 1 should be identified for the
machine learning. TreeNet was employed for this case again.
Reference:
Minitab
– www.minitab.com
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