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Advancing Artificial Intelligence

& Machine Learning 

AI, Meta Data Decision Making

Artificial Intelligence brings significant impacts on current business. Today, it is not just a computer which you used for lab but a resource which is everywhere to solve real-life business problems. AI becomes one of the important competitive advantages which creates new values and enables company to grow.

Machine learning is an area which computers can do self-learning without clear programming.

ex. ​Autonomous Vehicle, Spam filer

Depends on Data training types, Machine learning can be classified as below

Supervised Learning

Task Driven

  • An action which prediction model learns from past-data to predict the right answer

  • Target information based general predictive modeling

Financial Institutions credit prediction : Delinquent characteristics checking machine learning model development

Unsupervised Learning

Data Driven

  • Analysis to find similar group or pattern with no target

  • Customer Segmentation Plan by similarity grouping, clustering and linkage analysis

Classification by customers’ behavior : Customer research to create various segments by clustering analysis

Reinforcement Learning

Algorithm learns to react

To an environment

  • Method which to find the optimum strategy from learning by trial and error

  • Draw optimum strategy by reinforcing from measuring performance

  • Google’s Self-driving car and Limit optimization and etc.

‘Next best offer’ model development for Call-center group

How Predictive Analytics and Machine Learning are related?

  • Development Tool: SAS, Model Builder, Model Development Studio, etc.

  • Self-programming

At development stage, Artificial Intelligence can be applied by machine learning algorithm and retraining automation process

PANIROIS provides data analytic service with machine learning methodology

What is the major supervised learning algorithm for credit assessment model?

Random Forest

The application of collective intelligence

•Developing various models randomly and predicting a target by combining the developed models

•High predictive ability due to various models’ outcomes based on the majority decision or collective intelligence principle

Gradient Boosting Machine

Overcoming of weak prediction models

•Improvement in discrimination by adding relatively weak prediction models to training data

•High discrimination ability due to the growth through overcoming weaknesses of the model

Deep NN

Maximizing the prediction with
a nervous system principle

•Predicts a target by applying complex neural network functions like human brain

•Possible to predict the target with complex and various patterns through elaborate predictions

Contact us below if you have questions about AI or Machine Learning.

Contact Us

4F, Somerset Palace Seoul

7, Yulgok-ro 2-gil, Jongno-gu, Seoul, 03143, Korea

T. 02-739-8325

F. 02-739-8328


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