Minitab Webinar - Application of Machine Learning in the Financial Industry

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 25^th 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

PDF notes - https://www.sfi-minitab.com.tw/uploadedFiles/webinar/PresentationAUG25.pdf

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