Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions.

For markets and society to function, individuals and companies need access to credit. That's why one of the most essential tools banks use on a day to day basis and over a large scale are credit scoring algorithms or loan assessment.
A tool which can efficiently and effectively determine whether or not a loan should be granted.

But what if we can have this intelligent activity incorporated into our automation workflow? Possibilities will be endless.

What it does

Credit Grant Assessor

This activity can be used to easily incorporate credit grant assessment into your automation.

Qualifying for the different types of credit hinges largely on your credit history. This activity boasts a pre trained machine learning model which utilizes historical public credit data by world bank.

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Activity Parameters

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How I built it

The project was built using the following technologies.

  • Visual Studio with .NET Framework 4.6.1
  • Azure Machine Learning Studio - Multi-class neural Network

Challenges I ran into

As with most machine learning problems, challenges in cleansing data by far the most common. Also this is really the one of the most crucial part since this will ultimately dictate most of the accuracy and reliability of the trained model. The more data, the more the results are trained properly and the more it can predict the results on the test data. Next in line would be selecting the most optimal algorithm for a particular use case which may vary among different problems.

Skewed or unbalanced data. This also includes making sure that the data is free from bias brought about by unbalanced data.

Also encountered some challenges attaching the drop down list box to the designer canvas and attaching to the activity class model.

Accomplishments that I'm proud of

Of course when I finally reached a beyond acceptable accuracy and precision of the model! That was the Eureka and Voila moment!

What I learned

Dataset resampling to counter unbalanced data. One way to achieve this is by OVER-sampling, which is adding copies of the under-represented class (better when you have little data) Another is UNDER-sampling, which deletes instances from the over-represented class (better when he have lot's of data) More advanced technique includes using SMOTE (Synthetic Minority Oversampling Technique) to increase the number of underrepresented cases in a dataset used for machine learning.

What's next for Intelligent Activities - Document and Text Translation

Ability to do asynchronous retraining on the model with an updated dataset. Bulk and batch transactions More intelligent activities to come!

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