by Riddhi Mittal (@riddhimittal) on Saturday, 30 April 2016

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Section
Full talk

Technical level
Beginner

Media

Abstract

I lead Finomena, which uses the power of big-data, AI and ML in every imaginable way (information retrieval, NLP, deep learning, social network analysis, fraud detection and prevention, image recognition (even from videos), speech to text transcription and analysis, reinforcement learning) on a daily basis to provide access to credit to people in the long tail in India - over 60 crore people who would otherwise be rejected by any Bank or Financial Institution.

This talk will describe the massive access to credit problem in India, (or why fin-tech is the hottest kid on the block today), and open the audience’s minds up to using their ML knowledge towards this cause which is improving how people live their lives everyday. We can enable them a better quality of life, all while appreciating their unique differences to personalise the risk-assessment and risk-pricing, and still being able to scale up the technology using ML.

“Small tenure, small ticket-size” loans is a genre which cannot be solved by traditional ways of risk-assessment in banking. The cost of sourcing, analysing, approving and servicing is way too high for a request for a Rs. 60,000 laptop. And if that request is from a student, it will be rejected outright due to no income and no credit history. So the entire process has to be re-thought end-to-end from first-principles to be non-traditional and technology-first. Technology has to deeply disrupt every stage, so that disbursing loans at scale for such small amounts becomes viable.

As the former Economic Advisor to the Government of India C. Rangarajan recently said, “There are two aspects to financial inclusion: one is bank accounts and the second is access to credit. The scheme announced by the prime minister addresses the first problem. The issue of making credit available to small borrowers remains.”

ML in fin-tech is helping bring Financial Inclusion in India at the biggest scale seen in history so far. Character evaluation and risk-assessment still remain extremely complex areas as they are about evaluating how people might behave. There is plenty of behavioral psychology to take advantage of as well. We capture 20,000+ data points during the application process, and use those to varying degrees in the evaluation.

Outline

  • Proof that Banking is the most data-intensive business in the world!
  • How will big-data help increase penetration of credit in India
    • How to increase penetration of credit in India
    • The Long tail
    • Using big-data to personalise
    • The future of Holistic Risk-assessment and Differentiated risk-pricing
  • A sneak-peak into our Credit-scoring-engine architecture
  • Examples of how to use AI and ML in every imaginable way to provide access to credit to people in the long tail in India - over 60 crore people who would otherwise be rejected by any Bank or Financial Institution
    • E.g. image recognition
    • information retrieval and NLP
    • deep learning
    • social network analysis
    • fraud detection and prevention
    • The importance and impact of Re-inforcement learning
  • Examples of why ML is harder on these ever-evolving dynamic datasets.

Requirements

An open mind, and excitement! :)

Speaker bio

  • I studied BS and MS at Stanford University in Computer Science, built GraphSearch with the creator of Google Maps Lars while working as a Facebook engineer, was a VC associate at MDV ($700M fund) post that, and the youngest PM at Microsoft HoloLens post that, among other experiences. My two specialisations at Stanford were Systems and AI. I also enjoy behavioral psychology and we’re employing it in our product, design, and risk-assessment algorithms.
  • I am co-leading the largest consumer-lending fin-tech company in India:
    • Fin-tech in India is a trillion$ market because of India’s size. Today we have 25 crore people online and in the next 5 years that number is poised to grow to around 70-80 crore. This kind of a relative and absolute growth will not be witnessed again in the world
    • India has ~53% of its population under 25 years of age. It is a country of millennials, for whom, banking and lending have to be re-imagined.
    • India is mobile-first.
  • Finomena, the company I co-lead, is bringing Financial Inclusion to a country of 1.25 Billion people through focusing on the holy-grail of “small tenure, small ticket size” lending which can only be done in a tech-first way to be effective.
  • Finomena is built on the foundation provided by Aadhaar (the world’s largest biometric fingerprint and identity platform of it’s kind with 1 Billion people already registered on it). The Aadhaar platform will be as revolutionary over the next 5 years as the smartphone platform was from 2010-2015 in India.
  • I am a contributor to the “India-Stack” discussions - this refers to the most advanced fin-tech infrastructure in the world being developed by the Indian govt and several leading private sector thought-leaders in India
  • India is it’s own beast (because of it’s scale, diversity and chaos), and requires it’s own innovations, and I am in the rare position to compare and contrast the happenings in India with those in US (because I spent 7 years there) and/or China today.