Building a Scorecard using Python

schedule Aug 7th 11:45 AM - 01:15 PM place Neptune people 10 Interested

Financial Scorecards are used widely in all financial organizations for different kinds of ratings. This workshop will take you through the building and validation process of a financial scorecard using data.Financial Scorecards are used by banking organizations to judge the financial stability of their portfolio and take business decisions. These scorecards help in tracking and collections.

This workshop is designed for audience to take them through the process of developing a scorecard using Python. The workshop will guide you through the EDA process using Python and will demonstrate the different kind of visualizations that can enable better data understanding. We would cover basics of EDA and how python visualizations can support us in data mining. We aim to cover step by step process of building a scorecard and Use of different Machine Learning algorithms to build a better scorecard by comparing the outputs of different algorithms. We will demonstrate 3 different Machine learning algorithms Random Forest , Support Vector Machine and Gradient Boosting and their outcomes while building this scorecard.

Along the workshop we would introduce you to Python libraries that can be used to build these scorecards with more efficacy.

The key python libraries that we will be using will be Pandas , Numpy ,Scipy , Matplotlib and seaborn. We would demonstrate functions of these libraries used in building scorecards.

This will be a hands on session and attendees can come with their laptops for better understanding and follow up of session.

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Outline/Structure of the Workshop

Introduction to application of Financial Scorecard

What business problem does it solve

Data Wrangling using Python

Building the scorecard using Machine Learning algorithms

Comparing Scorecards and selecting the best

Scorecard Validation

Learning Outcome

Can build a scorecard from scratch using data science algorithms

Use of financial scorecards

Target Audience

Machine Learning Practitioners , Aspirational Data Scientists , Data Science Enthusiast, Python Programmers , Financial Experts , Banking Professionals

Prerequisites for Attendees

Basics of Scorecard

Basic Knowledge of Data Science

schedule Submitted 3 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Naresh Jain
    By Naresh Jain  ~  1 month ago
    reply Reply

    Hi Kavita, Nirav,

    Can you please help me understand, while creating a scorecard, what specific problem are you trying to solve using ML?

    • Kavita Dwivedi
      By Kavita Dwivedi  ~  4 weeks ago
      reply Reply

      Hi Naresh ,

      Thanks for reviewing the proposal. In this talk we would be discussing the creation of a scorecard to measure the credit worthiness of a customer.based on his application and behaviour data of his all banking products. This will help us measure the riskiness of the customer and also on aggregation will help in loss forecasting of a portfolio. These scorecards can be used during entire life cycle of a customer right from application , behaviour and collection portfolios. These scorecards help banks take decision whether to accept an application , restructure a loan or go for early collections etc.





  • Usha Rengaraju
    By Usha Rengaraju  ~  1 month ago
    reply Reply

    Kavita and Nirav,

    Kindly mention the python libraries which will be covered in the workshop . Does your workshop require prior programming experience ?

    Thanks and Regards,

    Usha Rengaraju


    • Kavita Dwivedi
      By Kavita Dwivedi  ~  1 month ago
      reply Reply

      Thanks Usha for your thoughts. We have updated the proposal with required details on python libraries. Basic Python /Programming experience is good to have, but our tutorial will start from the initial step so even someone new will be able to pick it up.



  • Dr. Vikas Agrawal
    By Dr. Vikas Agrawal  ~  1 month ago
    reply Reply

    Dear Nirav: Please add in the description that this will be a hands-on tutorial/workshop, and please mention the algorithms and libraries you plan to use. Perhaps information on what needs to be installed by attendees will be good to have as well. Warm Regards, Vikas

    • Kavita Dwivedi
      By Kavita Dwivedi  ~  1 month ago
      reply Reply

      Thanks Dr. Vikas for your time to review the proposal and give valuable suggestions. We have updated the proposal with required details. 



  • Ashay Tamhane
    By Ashay Tamhane  ~  1 month ago
    reply Reply

    Thanks for the proposal. Could you kindly elaborate on which ML algorithms will be demonstrated?

    • Kavita Dwivedi
      By Kavita Dwivedi  ~  1 month ago
      reply Reply

      Hi Ashay,


      Thanks for reviewing the proposal . We are largely looking at discussing Random Forest , Gradient Boosting and Support Vector Machines.




  • Anoop Kulkarni
    By Anoop Kulkarni  ~  3 months ago
    reply Reply

    Thanks for your proposal. This is a great area to be working on and looking forward to your talk. I have no specific questions at this time, your description is fairly clear. Thank you!