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SCET Financial Inclusion Collider Challenge: Saad Hirani | Wing Vasiksiri | Wyckliffe Aluga
Convenient Financial Access for All
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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
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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