Regtech in Fintech + QuSandbox Demo

Location:

QuantUniversity Meetup

8/10/2017

Regtech 101 + QuSandbox Demo

2016 Copyright QuantUniversity LLC.

Presented By:

Sri Krishnamurthy, CFA, CAP

sri@quantuniversity.com

www.analyticscertificate.com

2

Slides will be available at: http://www.analyticscertificate.com/fintech

• Founder of QuantUniversity LLC. and www.analyticscertificate.com

• Advisory and Consultancy for Financial Analytics• Prior Experience at MathWorks, Citigroup and

Endeca and 25+ financial services and energy customers.

• Regular Columnist for the Wilmott Magazine• Author of forthcoming book

“Financial Modeling: A case study approach”published by Wiley

• Charted Financial Analyst and Certified Analytics Professional

• Teaches Analytics in the Babson College MBA program and at Northeastern University, Boston

Sri KrishnamurthyFounder and CEO

3

4

Quantitative Analytics and Big Data Analytics Onboarding

• Trained more than 1000 students in Quantitative methods, Data Science and Big Data Technologies using MATLAB, Python and R

• Launching ▫ Analytics Certificate Program (Spring

2018)

▫ Fintech Certification program (Fall 2017)

6

• August 2017▫ Machine Learning models for Credit Risk – August 13th ARPM NYC▫ Fintech Certificate Program(www.analyticscertificate.com/fintech ) Open

house – August 17th Boston

• September 2017▫ Creating Credit Risk models with Alternate data – September 26th

• October 2017▫ Fintech PRMIA event – Boston – Oct 3rd

▫ Big Data Bootcamp – Boston▫ Fintech Certificate Program – Boston – Launch!

• November 2017▫ ODSC West

Events of Interest

7

8

• Boston

• New York

• Chicago

• Washington DC

• San Francisco

QuantUniversity meetups

9

10

• According to the IOSCO Research Report on Financial Technologies(Fintech):

“The term Financial Technologies or “Fintech” is used to describe a variety of innovative business models and emerging technologies that have the potential to transform the financial services industry ”

What is Fintech?

https://www.iosco.org/library/pubdocs/pdf/IOSCOPD554.pdf

11

• Offer one or more specific financial products or services in an automated fashion through the use of the internet.

• Unbundle the different financial services traditionally offered by service providers -- incumbent banks, brokers or investment managers.

For example:

• Equity crowdfunding platforms intermediate share placements

• Peer-to-peer lending platforms intermediate or sell loans

• Robo-advisers provide automated investment advice

• Social trading platforms offer brokerage and investing services

Innovative Fintech business models

Ref: https://www.iosco.org/library/pubdocs/pdf/IOSCOPD554.pdf

12

Fintech being noticed by Regulators

13

• Technologies like:▫ Cognitive computing

▫ Machine learning

▫ Artificial intelligence

▫ Distributed ledger technologies (DLT)

can be used to supplement both Fintech new entrants and traditional incumbents, and carry the potential to materially change the financial services industry.

Emerging technologies

https://www.iosco.org/library/pubdocs/pdf/IOSCOPD554.pdf

14

http://www.analyticscertificate.com/fintech/

15

http://www.analyticscertificate.com/fintech/

16

http://www.analyticscertificate.com/fintech/

17

http://www.analyticscertificate.com/fintech/

18

Technology enabling the creation or transformation of business models for reporting, monitoring & compliance in highly regulated industries

OR

Delivering regulatory compliance through technology improving upon current and traditional ways

What is Regtech?

19

•Scenario analysis, modeling and forecasting

•AML, Fraud detection

•Monitoring payments and transactions

•Trading analytics

•Regulatory compliance and tracking model changes

•Model risk, Stress testing etc.

Opportunities for companies

20

Companies in this space

Source: https://letstalkpayments.com/regtech-companies-in-us-driving-down-compliance-costs-innovation/

21

• The regulatory sandbox allows businesses to test innovative products, services, business models and delivery mechanisms in the real market, with real consumers.

• The sandbox is a supervised space, open to both authorized and unauthorized firms, that provides firms with:▫ reduced time-to-market at potentially lower cost▫ appropriate consumer protection safeguards built in to new products and

services▫ better access to finance

• https://www.fca.org.uk/firms/regulatory-sandbox

Regulatory Sandboxes

22

Who the sandbox is for:• Businesses seeking authorization▫ The sandbox may be useful for firms that need to become authorised

before testing their innovation in a live environment.

• Authorized businesses▫ The sandbox may be useful for authorized firms looking for clarity

about rules before testing an idea that doesn’t easily fit into the existing regulatory framework.

• Technology businesses supporting financial services firms▫ Technology businesses that want to provide services to our regulated

firms (eg: through outsourcing agreements) can also apply for the sandbox if they need clarity about rules before testing.

Regulatory Sandboxes

23

US Regulators catching up

24

• Creating internal labs or innovation houses▫ Manulife - LOFT

▫ DCU – Fintech Innovation center

• Partnering or prototyping Fintech solutions▫ Fidelity promoting Fintech Sandbox

• Internal Innovation to replicate Fintech business models▫ Fidelity Go

What are companies doing?

25

Model Validation

• “Model risk is the potential for adverse consequences from decisions based on incorrect or misused model outputs and reports. “ [1]

• “Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses. ” [1]

• Ref:• [1] . Supervisory Letter SR 11-7 on guidance on Model Risk

27

Popularity of Open-source software in the enterprise increasing

28

• Financial Services customers like Capital One, FINRA, and Pacific Life are moving critical workloads to AWS

Cloud maturing

29

• Versions and packages

Challenges in adopting Open-source software in the enterprise

30

• Difficulty in replicating and reconciling differences in environments

Challenges in adopting Open-source software in the enterprise

31

• Deploying models built by Data Scientists still a problem

Challenges in adopting Open-source software in the enterprise

Data Scientists Enterprise IT

32

• The Try before adopt model is difficult with unproven open-source solutions

Challenges in adopting Open-source software in the enterprise

33

www.QuSandbox.com

34

Quant/Enterprise use cases• Create an environment that can support multiple platforms and

programming languages• Enable remote running of applications• Ability to try out a Github submission/ someone else’s code• Facilitate creation of Docker images to create replicable containers• Create prototyping environments for Data Science/Quant teams• Enable Data scientists/Quants to deploy their solutions• Enable running multiple tasks and jobs• Enable concurrent running of multiple experiments• Integrate seamlessly with the cloud to scale up computations

Use cases

35

Fintech use cases

• To demonstrate solutions to enterprises

• Create customized enterprise trials for companies that don’t permit installation of vendor software prior to procurement

• To manage quick updates

• Enable effective integration and hosting of services (REST APIs)

Use cases

36

Academic use cases

• Enable creation of course material and exercises that could be shared

• Enable students and workshop participants to focus on the data science experiments rather than environment setting

Use cases

37

Creating replicable environments

Creating and manage replicable environments (Code + software + data) in a single portal

38

Creating replicable environments

Create replicable environments (Code + software + data) through a easy point & click tool and publish to Dockerhub or manage internallyShare it with target users

39

User portal

• Run multiple experiments in pre-created environments (Code + software + data)• Deploy your own solutions• Run any Docker image or Github submission on the cloud

40

Run Jupyter notebooks and prototype applications

41

Run Rstudio and Shiny applications

42

Run any Docker application

43

Manage tasks and errors

44

User portal

• Dockerize and deploy applications on AWS in just a few steps

45

Deploy applications with ease

46

Open source project

47

www.QuSandbox.com

48

Thank you!Checkout our programs at:

www.analyticscertificate.com/fintechwww.qusandbox.com

Sri Krishnamurthy, CFA, CAPFounder and CEO

QuantUniversity LLC.

srikrishnamurthy

www.QuantUniversity.comInformation, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be

distributed or used in any other publication without the prior written consent of QuantUniversity LLC.

49