Through peer-to-peer lending platforms, individuals can access loans at lower interest rates than traditional banks by connecting borrowers directly with lenders. This democratizes debt and expands access to funds for underserved segments that face high costs and barriers to obtaining loans from banks. By utilizing alternative data sources and analytics, P2P marketplaces can assess borrower creditworthiness in a more customized way than traditional banks, which rely primarily on credit scores and financial history. For both borrowers and lenders, P2P lending provides an opportunity for easier access to better rates than are available through conventional banking.
12. Changing the information Paradigm
Traditional Parameters
1. Demographics
2. Credit Bureau Data
3. Transactions Data
4. Existing Obligations
Psychometrics
1. Assessing Intention
2. Credit Assessment for Thin File
Customers
Big Data Analysis
1. Criminal History
2. Online Footprint
3. Location Data
4. Device Data
5. Interaction Patterns
Social Media Data
1. Peer Influence – Social web behavior
2. Scope of online activity
3. Social Graph
4. Technology Usage
5. Interaction patterns
Final Score
13. Undertaking Credit Appraisal through technology enabled process
Credit scoring using credit reports from
CIBIL and transaction level financial data
from Yodlee.com
Scoring
Credit verification / validation done
through:
• Mobile App (geo-location based)
• Yodlee.com
• Physical verification / Aadhar
• Lenddo Validation /
Verification
Regular credit monitoring to ensure risk
management using:
• Social Data
• Lenddo
• In-house analytics team
Risk
mitigation /
management
Credit Score
(Out of 400)
14.
15.
16.
17. Lenders
Tenure
(months
)
Rate of
Interest
Loan
Funded
EMI
Payable
Total Payable
Total
interest Paid
Processing
Fee @ 2%
Total
Payout
Mr. Sadanand Mishra 24 18% 30,000 1498 35952 5952 600 6552
Mrs. Malini Purushothaman 24 18% 10,000 499 11976 1976 200 2176
SAMARTH FINANCIAL -Pvt.
Ltd
24 18% 20,000 998 23952 3952 400 4352
Mr. Soumitra-Das 24 18% 30,000 1498 35952 5952 600 6552
Mr. Piyush Ranjan-Mishra 24 18% 10,000 499 11976 1976 200 2176
Total 1,00,000 4992 119808 19808 2000 21808
Use Case: Borrower: Mr. Prashant Nayak
Amount Borrowed : Rs. 100,000
Bank
Rate of
interest
Loan
EMI
Payable
Total Payable Total Interest
Processing
Fee @ 2%
Total
Payout
XYZ Bank 21% 100000 5139 123336 23336 2000 25336
Savings with Faircent Rs.3528
18. Borrowers
Tenure
(month)
Rate of
Interest
Loan
Funded
EMI
receivable
Total
Payback
Total
interest
Paid
Reinvest of
the principle
amount on
Faircent (At
Bank FD rates
of 8.75%)
Total
Payout
Processing
Fee @ 2%
Net
Payout
Mr. Mehender Singh 18 18% 14,000 893 16074 2,074 969 3,043 280 2,763
Mr. Prashant Nayak 24 18% 30,000 1498 35952 5952 2727 8,679 600 8,079
Mr. Mohan Rao 24 18% 30,000 1498 35952 5952 2727 8,679 600 8,079
Total 74,000 3889 87978 13,978 6423 20,401 1480 18,921
Use Case: Lender: Mr. Soumitra Das
Amount Lent: Rs. 74,000
Bank
Tenure
(Month)
Rate of
interest
FD
Amount
Interest
received
(Type: Re-
investment)
Total Payout Interest Earned
HDFC Bank FD 18 8.75% 14000 1937 15937 1937
HDFC Bank FD 24 8.75% 30000 5672 35672 5672
HDFC Bank FD 24 8.75% 30000 5672 35672 5672
Total 74000 13281 87281 13281
Incremental Earnings with
Faircent
Rs. 5,640
Notas del editor
The Algorithms would be designed to crunch data points spanning across entire financial, social and technological behavior to arrive at a composite score which operates at two levels: Determine the risk associated with a profile and from thereon, assign a score to determine the probability of default. The alternative fringe credit data are soft flags to understand the stability and more importantly the intention of a loan profile so that in case a default occurs then how would a loan account turn an NPA or still deliver. Also, at what stage is the likelihood of the account turning an NPA.
This profiling is an extensive interaction of a mosaic data points correlated statistically to arrive at the loan eligibility terms. More importantly, as time passes, the machine is going to have further wealth of information to highlight key drivers impacting a loan default. The Algorithms themselves are self learning in nature. Finally, with the help of technology this decision making is implemented and cross referenced in a matter of minutes! Thereby, ensuring effectiveness and efficiency.