1. Core requirements Why does it matter
1 Embedded in the purchase flow  Financing must be embedded early in the purchase journey to capture core benefits
2
Proven ability to provide a simple and
compelling user experience
 Critical to optimize conversion and reduce cart abandonment
 Efforts to provide multiple products less likely to succeed
3 Flexible underwriting models
 Need to provide a credit decision in real-time
 Support broad range of credit profiles (e.g., thin/ no-file)
4 Low friction to implement
 Ensure product can be rapidly implemented and scale across merchant sizes with
low effort, high standardization
5 Compete on price
 Commoditized product (in short term) will require solution providers to offer low
cost to ensure acceptance
6 Fund merchant incentives
 Solution providers are increasingly competing on incentives (e.g., payments to
merchants) in order differentiate
Requirements for BNPL

2. ARAAMPay Customizable Product Suite to Meet Consumer,
Merchant and Partner Needs
For Consumers
Broad product offerings provide flexibility that
fit individual needs. Transparent APRs prior to
checkout.
For Merchants
Products offered without redirecting the
consumer from your site. Transparent MDR.
For Partners
Ability to tailor decisioning and adjust payment
product offerings, including own products
(Rs 1000- Rs
10,000)
EDUCATIONAL WEBINAR SERIES
Rs 25,000 ++

3. Araam Pay Financial offering What It means
Pay in 4
Buy today and pay in 3 equal installments
every 2 weeks at no interest
Pay in 30 days
Buy today , pay after 30 days at no
interest
0% financing needs
Pay in equal installments for 3-12 months
with no interest
>0% financing needs
Pay in equal months for 6-48 months
with an interest rate.

4. Ideal unit economics of AARAMPay for becoming successful in parallel of GMV
% of GMV
Revenue
Merchant Fee 2 - 8% p.a
Late Fee 1 - 2% p.a
Expenses
Net Transaction Losses 1 - 5% p.a
Cost of Capital 15 – 18% p.a
Servicing Costs <1% p.a
Marketing Costs <1% p.a

5. Digital Channels
ARAAMPay Interface
Container-LOS
BRE based
Credit Model
Core Banking Platform of Lender
API’s for
disbursement
post confirmation
Bureau
Check
Geo
Tagging
Other
De-Dupe
Behavio
ral data
Alternat
e Data
Bank
Stat.
Limits set in aap
L M S which
includes
Management,
repayments,
Analytics etc
Default Collections
Default Intimations for collection
Collections
and Escrow

6. Unstructured data,
Natural language Progression,
Social data mining- Processing
• Age
• Location
• Marital Status
• Background-Working/Non Working
Segmentation algorithms
Advanced sampling and
Machine learning
Based credit scoring
Salary
WORKING-CONDITION
EXTREME HOURS
SCHEDULE
EXCEPTIONAL
LOCATION
PEOPLE
FOCUS
Bureau Check
R
WORK
Amount of Loan
Credit and Other
social checks
Age and Marital
Status
Children
Region
Gender Occupation
Predict default Fraud detection Credit scoring Risk management
Credit Model Snapshot

7. • A machine learning model returns a
PROBABILITY of default.
• We have to decide on a threshold value,
above which the loan will be declined.
Clients with a probability of default higher than the
threshold are denied credit.
2% 40% 80%
Threshold:
50%
The decision boundary.

8. Merchant Onboarding
Credit Type Selection Credit Limit Setup Securitization Execution
Consumer
Merchant Addition
Initiation
Consumer Credit Management
Merchant Management
Product Selection Customer
Underwriting
Customer Credit
Setup
Reconcilation
Subvention or Merchant Fees
1
7
BNPL Request Initiation
8
Credit Cancellation
14
Merchant Level Accounting
15
Invoice Generation
16
Refund / Return
Processing
17
18
Obligation Monitoring
19
Merchant confirms
order Validation for
subvention
20
21 22
Receivables & Payables
Management
23
Repayment Processing
13
Payment Processing
12
10
Repayment Mode
Selection
11
Merchant KYC Validation
2
Merchant
Underwriting
3
Merchant AccountOpening &
Limit Setup
4
Platform Fee Discussion
and closure with
agreement
5
Merchant Platform Access
6