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Kym - GoJek GoPay integration

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Proposal for Kym (offline credit score calculation) integration with GoPay e-wallet (GoJek).

Publicada em: Economia e finanças

Kym - GoJek GoPay integration

  1. 1. Copyright Skyline Labs 2016-17
  2. 2. Copyright Skyline Labs 2016-17 Hackathon 2017
  3. 3. GoPay Credit based e-wallet payment system Copyright Skyline Labs 2016-17
  4. 4. Why this idea? GoHack – GoJek services Current Hyper-local transport & logistics scene in Indonesia GoJek is expert in services it provides Minor changes in logistics or backend not enough To beat the competition – Unique feature Using existing infrastructure Copyright Skyline Labs 2016-17
  5. 5. GoCredit – The idea GoPay : Credit based e-wallet Revolutionary concept for e-wallets Allow transactions without requirement to pay immediately 0 GoPay account balance and still need to use services – Use ‘GoCredits’ for payment (Negative GoPay balance) Payments clear at end of month - Through cash or digital payment Copyright Skyline Labs 2016-17
  6. 6. GoCredit calculation Sources of data for credit score calculation 1. Bank transactions 2. Timely repayment of bank loans 3. Social media profile 4. GoJek app activity 5. Timely repayment of GoCredits Machine learning based determination of max GoCredits allocated Copyright Skyline Labs 2016-17
  7. 7. Data collection What’s unique – No need to approach banks for data! SMS Mining through android app will provide complete profile Research paper on the proposed model accepted to Peer reviewed IEEE International conference Copyright Skyline Labs 2016-17
  8. 8. Data collection Traditional credit score calculation Copyright Skyline Labs 2016-17
  9. 9. Data collection Proposed credit score calculation Copyright Skyline Labs 2016-17
  10. 10. Data mining Keyword extraction based NLP Your SBI account 123456789 has been credited/ Debited with Rs 100 for transaction at Flipkart. Current balance: 56 Example of data captured through SMS Copyright Skyline Labs 2016-17
  11. 11. Solution architecture GoCredit Copyright Skyline Labs 2016-17
  12. 12. Solution architecture R studio – Data pre-processing and processing PHP – Android to MongoDB REST APIs MongoDB Android database NodeJS application for Web-dashboard Apache server for REST APIs Materialize CSS ExpressJS Twitter APIs Android application Application uses Keyword word extraction Based NLP algorithm Copyright Skyline Labs 2016-17
  13. 13. What have we made? Android application 1. Replicates e-wallet transactions 2. SMS data extraction Apache server with PHP 1. Android to MongoDB data collection and extraction MEAN Stack web application 1. Graphical data insights R Server 1. Data pre-processing 2. Twitter data collection and sentiment analysis 3. Credit score calculation Machine learning algorithms Copyright Skyline Labs 2016-17
  14. 14. Future prospects GoCredit system opens up a completely new potential domain For GoJek (PayTM Bank – GoJEK bank) Credit score as a service – Use the calculated credit score for enabling Various services Data collected by the system helps GoJek create detailed model Of every user 1. Use for targeted advertisements 2. Chatbot (Using contextual data collected , NLP algorithm created) 1. Automated helpdesk chatbot 2. Interactive chatbot for using GoJek services Copyright Skyline Labs 2016-17
  15. 15. Machine learning • Data unavailable for accurate prediction as of now • Sample data trained over linear regressions to calculate weightage of parameters • To train the backend – Limited release of GoCredit feature to collect data • Code for calculating weightage using ML is ready to demo Copyright Skyline Labs 2016-17
  16. 16. Privacy issues • Ask user permission to access data – If denied, GoCredits not available • Many applications (Including Google) access SMS data • Hash code the data captured - GoJek provides GoCredit without knowing who the user is. • Don’t send SMS data to server. Offline logic on smartphone will calculate the credit score and send to servers. Copyright Skyline Labs 2016-17
  17. 17. Money security • GoCredit system is created to be profitable – Model assures minimal risk • Maximum credit value allocated will not be high (50,000-60,000 Rupiah) • Credit is given only to the users with average monthly spending of a certain high amount (For example 1,00,000 rupiah) • Legally enforceable credit system – defaulting will lead to legal action • Credit allocation will be based assuming worst case scenarios – The data trained using ML will allocate 50,000 rupiah if credit worth calculated is of 1,00,000 rupiah. • Typical value for customer acquisition - ~30,000-40,000 rupiah. • Risk involved in GoCredit per user – equal to customer acquisition cost Copyright Skyline Labs 2016-17