SCET Financial Inclusion Collider Challenge: Saad Hirani | Wing Vasiksiri | Wyckliffe Aluga

Convenient Financial Access for All

2

Agenda1. The Problem With Financial Inclusion in Pakistan2. Our Proposed Solution3. Our Product & How It Works4. Product Wireframes & Screenshots5. Value Proposition6. Makeup of Market Opportunity7. Market Validation & Research8. Revenue & Business Model9. Competitive Analysis 10.Risks, Threats 11.Timeline12.Team, What We Need13.Appendices

3Pakistan’s Inclusion Issue

Distance to BankLack of Financial InfrastructureRestrictive Regulations

Governance FailuresLack of Suitable Products

13%

Adults with Bank Account

6%

Adults with Saving Accounts

4Our Solution Concept

Focus on increasing use of mobile payment +Game Changing Hybrid Cost Structure

Affordable, Bank-lessFinancial Services

Afford the Lower Income Classes a Chance Through a Tailored Adaptable Product

6Chance

Send money to anyone anytime

All that by simply texting

Open an Account at Significantly

Low Cost

Deposit Money or Have Money

Transferred to You

Save money & Be More Aware of

Finances and Liquidity

Withdraw cash anywhere anytime

7Wire Frames [Sending]

SECURITY / IDENTITY VERIFICATION

Transaction Confirmation & Record

Interface through

Text with Multiple Options

8

Trains and distributes e-cash to the master agentsChance Sales Team

Supervise and distribute e-cash to local Retail Agents

Master Agents

Allow customers to deposit and withdraw cash

Retail Agents

Customers can send and receive the e-cash

Customers

Distribution Model

9Creative Cloud Technology

Task ManagerLoad BalancerSMS Bot API

DatabaseUser mobile phone

Independent, secure technology that is non-dependent on internet connectivity

10Value Proposition

Safe Storage Mechanism to

Increase Savings

Improves Allocation and Deepens P2P

Lending / Formalizes Credit Market

Allows Risk Sharing and Expansion of

Geographic Networks

Makes it Easier to Pay for, Receive

Payments for Goods and Services

11Market Make-Up

100m

92%

80%

$2py

Unbanked Population in Pakistan

Adult Cell Phone UsersGet paid in cash by factories

Income Under $10k / Year

Avg. Revenue / Customer

Total AddressableMarket: $147m

Conservative Estimate

2018 Target Market: $1.5m

12Market Validation & ResearchInterviewed lower-middle income workers paid daily cash wages in 4 different factories to gauge if they would use product, as well as the viability of product to such customers

• Average Income of $1250/Yr (Average GDP)

• Typical Blue Collar Workers (90M, 40F)

• Everyone had access to own cell phone for personal use

• Cash based Income

• 27/130 said that they do send money

• ~22% people saved (80% of these < Rs 500)

Haier8%

LG17%

Motorolla11%

Nokia15%

Q-Mobile20%

Samsung17%

Sony12%

Mobile Device owned

Jazz / Mobilink15%

Telenor35%

Ufone12%

Warid6%

Zong33%

Count of Service Provider

64 of 130 people said that they would trust sending or receiving money via phones on

text

All people had texting capability in Urdu or

English: 40 Frequent, 18 Usual and 72 Occasional

‘Texters’

13Business Model & Revenue

Withdrawal Fee CutReinvestment of E-

Cash ReceiptsLicensing Fee

Data Sales to Banks, Research etc.

14Competitive Analysis

Accessibility for consumers

Scalability

Cater to Banked Consumers Limiting TAM

Most Firms Cater to One Bank/ One Service Provider

15Risks & Challenges

Minimum deposit requirements, taxation on every transaction fee

Regulation

A significant number of people are happy to operate on cash for the sake of evading taxation

Taxes & The Want for a Cash Economy

Convincing grocery stores etc. to act as agents, banks & microfinance inst. to act as master agents

Adaption & Introduction of Scale

Record rates of corruption in PakistanUnpredictability, Corruption, Fraud

16Project Timeline

- Continue negotiations with financial institutions

- Hire developers, team on ground in Pakistan

Now

- Build MVP of product- Begin training master agents- Acquire pilot license- Acquire agents in dense

population area for B-Testing

Three months

Six months

One year

- Launch 6 month pilot program with 200 Known Customers

- Iterate product from pilot data

- Acquire full operation license

17Our Team

Wyckliffe Aluga(CEO)

Wing Vasiksiri(COO)

Saad Hirani(CFO)

• Product Development for 7 Startups

• Experience in MF in Kenya

• Experience in Operations & PM

• Significant Experience in Data Analysis @ TALA

• On Ground Connections, Experience, Partnerships

• Financial Modeling, Growth Fin-Tech Background

18What We’re Looking For

Connections with Fin-Tech Industry Leaders, Banks, LawyersPossible Executive Board Members

Mentorship, Feedback

Need developers, on-ground team (sales & marketing) Workers / Executors ‘The Ninjas’

To create relationship for deposits, master agentsPossible Data Customers in the Future

Partnerships with Banks and MF Institutions

To Develop Beta Product, Hire & Expand Team ($100k)To Front Minimum Reserve Requirements - $2m (IRR +)

Funding (Seed Equity)

Appendix

20Appendix 1: Regulatory Aspects

Minimum Capital Requirements

Application for Permission

Operation Regulations

Security and Confidentiality Laws

• Unable to perform banking functions or act as custodians

Solution: Partnership with banks / MF banks

• Must acquire pilot license followed by operating license

Solution: Operate a Beta with one master agent using pilot license

• Required minimum capital of 200 million rupees

Solution: Partner with microfinance institutions and banks, along with fundraising

• Must abide by state bank confidentiality standards

Solution: Hire a lawyer to ensure we comply with all standards

21Appendix 2 - Market Validation & ResearchInterviewed lower-middle income workers paid daily cash wages in 4 different factories to gauge if they would use product, as well as the viability of product to such customers

Low-income workers from 4 factories in Karachi earning an average income of Rs 12,000 / month i.e. $1350 per year – all unbanked

Participants worked in factories as packers, security guards, supervisors, cleaners, operators, drivers, gate-keepers, helpers etc.

All received income in form of monthly cash based payments from supervisors at factories, had cell phones for personal use

103 people said they do not send money, 27 said that they do send money (mostly via friends, by themselves in lump-sum, via cell phone credit)

~22% people saved (80% of these < Rs 500) for causes like children (education), marriage, miscellaneous and healthcare

Haier8%

LG17%

Motorolla11%

Nokia15%

Q-Mobile20%

Samsung17%

Sony12%

Mobile Device owned

Jazz / Mobilink15%

Telenor35%

Ufone12%

Warid6%

Zong33%

Share of Service Provider

64 of 130 people said that they would trust sending or receiving money via phones on

text

All people had texting capability in Urdu or

English: 40 Frequent, 18 Usual and 72 Occasional

‘Texters’

22Appendix 3: Fraud Prevention

Put checks on the identity and legitimacy of customers, especially new new customers and those acting on behalf of others. Have a transaction limit

Record keeping and established systems of identifying and reporting unusual or suspicious transactions

Train our agents to spot activities that raise a suspicion of money laundering, and to put clear processes in place for reporting back to the us for crosschecking.

23Appendix 4: Distribution Model

24Appendix 5: Pricing Model & ExampleCategory Withdrawal Amount Transaction Fee

Charged (Rs)Taxation Master Agent Agent CHANCE

1 0-200 Rs 5.00 Rs 0.8 Rs 1.0 Rs 2.5 Rs 0.72 201-500 Rs 7.00 Rs 1.2 Rs 1.4 Rs 3.5 Rs 0.93 501-1000 Rs 10.00 Rs 1.7 Rs 2.0 Rs 5.0 Rs 1.34 1001-5000 Rs 15.00 Rs 2.5 Rs 3.0 Rs 7.5 Rs 2.05 5001-10000 Rs 20.00 Rs 3.3 Rs 4.0 Rs 10.0 Rs 2.76 10001-25000 Rs 50.00 Rs 8.3 Rs 10.0 Rs 25.0 Rs 6.77 25000-50000 Rs 100.00 Rs 16.6 Rs 20.0 Rs 50.0 Rs 13.4

Agents Master Agents CHANCEPer Transaction Total Per Transaction Total Per Transaction Total

Category 1 Rs 2.5 Rs 2.50 Category 1 Rs 1.0 Rs 1.00 Category 1 Rs 0.67 Rs 0.67Category 2 Rs 3.5 Rs 14.0 Category 2 Rs 1.4 Rs 5.6 Category 2 Rs 0.94 Rs 3.75Category 3 Rs 5.0 Rs 15.0 Category 3 Rs 2.0 Rs 6.0 Category 3 Rs 1.34 Rs 4.02Category 4 Rs 7.5 Rs 7.50 Category 4 Rs 3.0 Rs 3.00 Category 4 Rs 2.01 Rs 2.01Category 5 Rs 10.0 Rs 10.0 Category 5 Rs 4.0 Rs 4.0 Category 5 Rs 2.68 Rs 2.68Total Per Customer Rs 49.00 Total Per Customer Rs 19.60Grand Total Rs 4,900.00 Total Per Agent Rs 1,960.00 Total Per Customer Rs 13.13

Grand Total Rs 29,400.00 Grand Total Rs 19,698.00

1 Master Agent, 15 Agent, 100 Customer, 10 Transaction / Customer Model

25Appendix 6: Financial Projections2017 2018 2019 2020 2021

Master Agents 1 20 50 100 150Agents 10 300 750 2000 3000Customers 1000 60,000 250,000 800,000 1,400,000Transactions 10,000 300,000 750,000 2,000,000 3,000,000

Revenue Per Customer 13.1 7.5 5.1 4.7 5

Revenue [Withdrawals] 157,200 5,400,000 15,300,000 45,120,000 84,000,000 Licensing Revenue - - 3,750,000 10,000,000 15,000,000 Reinvestment of E-Cash - 4,800,000 12,000,000 32,000,000 48,000,000 Data Sales - - 10,000,000 20,000,000 60,000,000

Total Revenues (Rs) 157,200 10,200,000 41,050,000 107,120,000 207,000,000 Total Revenues ($) 1,497 97,143 390,952 1,020,190 1,971,429

26Appendix 7: Partners, Traction

• Conversations with Habib Bank Limited (Largest bank in Pakistan) on relationship banking / master agent

• Conversations with Akhuwat Microfinance & Aga Khan Agency for Microfinance to act as Master Agents

• Interest from CEO of 21C Girls to Develop Product Technology and Join as Mentor / Board Member

Market Indicators of Industry Health

45%Partnerships, Conversations

ATMs unable to meet consumer demand900

043%100%65.8 million

Of All online shopping will be on mobile by 2020

Increase year on year increase in micro-savers

Increase in mobile money transactions in 2015

Branchless Banking transactions occurred in 2015

27Appendix 8: Product Wireframes

SECURITY / IDENTITY VERIFICATION

Transaction Confirmation & Record

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Factory

Mini Mart

LandLord

Grocery Store

Power Bill

School

Paid Cash John His

Wife

Open account and Deposit free

Send Money to his wife

Send Money for rent

Withdraw

Pay Bill

Pay School fees

Karachi Peshawar

Appendix 9: Transactions from 0-1

29Appendix 10: The Big Picture

This previous market has been untapped by banks and the

transactional data of this specific population segment does not exist

Data from the underbanked

By seeing how often consumers make deposits and withdrawals we can understand how or even if this

segment saves

Consumers Savings Habit

The transactional data will also allow us to see who consumer sends money to and how often they do so

Understand Purchasing Power

By analyzing the locations and frequency of interactions of our agents we will have a better geographical understanding of the country

Population Density

Eventually we will amass enough data from each consumer, based on their spending and savings habit, to generate credit scores

Generate Potential Credit Score