The document summarizes a presentation on structured finance given by Prajeesh Jayaram. It discusses gaps in the supply and demand of money that structured finance addresses, such as the need for returns between stocks and loans. It also covers how structured finance products like securitization allow originators to sell loan receivables to access funds today in exchange for future repayments. The document highlights how data analytics can be used to analyze loan pools and predict credit losses.

1. Session on Structured Finance
Non-Core Weekend
For Finance & Analytics Club, IIT Kanpur
By Prajeesh Jayaram on 12th February 2022

2. This presentation has been prepared by Prajeesh Jayaram (author) acting independently. All
opinion exclusively belongs to the author and is not an opinion of the author’s employers or any
organisation the author is associated with, past or present. The presentation may not be
reproduced, acted upon or disseminated, in whole or in part, without the author’s consent.
The presentation may contain market information that may not be verified and the author makes
no representation nor warranties of any kind on the correctness, factual accuracy and
completeness of the content. All numbers in the presentation including interest rates, default
probabilities are illustrations and may not convey the current or past market conditions. The
author takes no responsibility of the conclusions the audience may derive based on such
information. Any liability of whatsoever nature and however arising, relating to the contents of
this presentation is hereby expressly disclaimed.
This presentation is prepared and presented solely for educational purposes. This presentation
does not constitute legal, tax, structuring and/or investing advice. Should users/audience of this
presentation need legal, tax, investing and/or structuring advice, they are urged to contact
relevant advisers in the relevant jurisdictions.
Disclaimer

3. Agenda
• Gaps in Supply demand dynamics of money
Why Structured Finance?
• Getting tomorrow’s money, today
What’s the solution for financing problems?
• Can math and models go wrong!? Incentives and assumptions do
How are deals and loan pools analysed?
Questions and Discussions

4. Agenda
• Gaps in Supply demand dynamics of money
Why Structured Finance?
• Getting tomorrow’s money, today
What’s the solution for financing problems?
• Can math and models go wrong!? Incentives and assumptions do
How are deals and loan pools analysed?
Questions and Discussions

5. Why What How
Flow of Money & Risk Pricing
Sacrificing today’s
money for
tomorrow’s needs
& expenses
Money
Deployers
Banks
Funds
Non-Banks
Money
Providers
Households
Companies
Governments
Money
Seekers
Governments
Companies
Households
Analyse risk
and fix price
of money
Want tomorrow’s
money for today’s
projects &
expenses
• Inflation
• Risk
Return
Parties looking for
different returns
for different
tenures
Parties taking part
in different risks

6. Why What How
Network Among Deployers
Banks: Trusted by Money Providers
Non-Banks: Better understands Money Seekers
Funds: Varied mandates, varied regulations
Money Deployer
Specialization
Banks
NBFCs
Funds
Why are money deployers
dependent on each other?
Savers
Borrowers
Investors

7. Gap 1: In-between Stocks and Loans
Borrowings / Loans / Debt
• Fixed Income - Loans & Bonds
Anything in between?
• Special situations / hybrid?
Stocks / Equity
• Private Equity, Listed Shares
Increasing
Returns
6%-11%
20%-30%
Increasing
Risks
Why What How
Illustration of a start-up break-up
50% 50%

8. Empathize Define Ideate
Why What How
Loans: Value Chain & Risk Taking
Student 1 Financer
Loan 1
Student 2
Student n
…..
Evaluates & takes risk
of students not paying
Big Loan
Banker
Deposit 1
Saver 1
Want education loan
(1 mn each)
We can repay from
future salary
Want corporate loan
I can repay from loan
repayments
Got deposit money
Will lend to capable
financers
We trust a bank but
not a financer or a
student
Evaluates & takes risk
of financer not paying
Loan 2
Loan n
Depo. 2
Depo. m
Saver 2
Saver m

9. Empathize Define Ideate
Why What How
Returns and P2P Lending
Students Financer
9%
(floating?)
7%
Banker
4%
Depositor
Returns mentioned are for illustration
The promise of a peer to peer lending network!
Digital platform
automating risk
evaluation
6%
8%

10. Empathize Define Ideate
Why What How
Gap 2: Selling Loans
Borrow. 1 FInancer
Borrow. 2
Borrow.
100
…..
Borrowings
80 Mn
Banker
Deposits
Depositors
1. Exhausted money
Want to give 100
more loans
2. No more trust financer
May consider taking the
risk of loans directly
Loan 1
Loan 2
Loan
100
Can the Financer sell to Banker, a pool of loans?
Banker wants Financer to stay for collections!
Remember banker is a wholesaler.
3. Loan maybe for 15
years while
borrowing is for 3 yrs
100 Mn

11. Agenda
• Gaps in Supply demand dynamics of money
Why Structured Finance?
• Pledge whatever you have, getting tomorrow’s money, today
What’s the solution for financing problems?
• Can math and models go wrong!? Incentives and assumptions do
How are deals and loan pools analysed?
Questions and Discussions

12. Structured Credit
Borrowings / Debt
• Fixed Income - Loans & Bonds
Mezzanine / In-between
• Structured Credit by NBFCs, PCFs
Stocks / Equity
• Private Equity, Listed Shares
Increasing
Returns
6%-11%
11%-25%
20%-30%
Increasing
Risks
Non-vanilla
Flexible
Situational
Recoverable
Structured Credit is
Why What How
Illustration of a start-up break-up
50% 50%
0% 100% 75% pledged
100% 25% 75%
Repaid Not repaid

13. Empathize Define Ideate
Why What How
Selling Loan Receivables
Borrow. 1 Originator
Borrow. 2
Borrow.
100
…..
Borrowings
Banker
Deposits
Depositors
Originator sells the loans…Securitization
100
Borrowers
100
Loans
Trust/SPE
Originator
Loans
sold
Money Money Investor
or Banker
Borrowings
Evaluates & takes risk
of students not paying
Takes on risk of
financer not repaying
Banker
Investment
Loan 1
Loan 2
Loan
100
Sold loan receivables and got
tomorrow’s money today!
Collection, recovery
Monetise loan receivables, but continue servicing
1. Want to give 100 more loans
2. Can take the risks of student loans directly
3. 3. Loan for 15 yrs while borrowing for 3 yrs

14. Securitization
A securitisation Trust will issue different securities to different types of investors
Trust
100%
Senior Bond [6%]
Mezz. Bond [8%]
Junior Bond [16%]
Pension Fund
Mutual Fund
PC Fund / Orig.
60%
30%
10%
Want safest investments (like AAA
ratings), ok with low returns
Want moderate return, can take
moderate risk
Want very high returns, can take
higher risk / skin in the game
Loans will give 9% return but these bonds only have aggregate return of 7.6%
Rest of the money can be used for expenses
Why What How
100
Borrowers
100
Loans
Trust/SPE
Loans
sold
Money
Money Investor
or Banker
Investment
Originator
Collection, recovery

15. Trust and Transparency
Why What How
Senior Bond
Mezz. Bond
Junior Bond
₹6
₹3
₹1
100
Borrowers
Collections
₹10
Trust
Originator
Info
Collection, recovery
Can smart contracts & blockchain be a
catalyst for digitisation?
Money and information Flow
Investors
Investor believes that Originator will put in effort to collect & recover
Investor trusts Originator to pass on the money collected as per the waterfall
Market trusts that the information provided by the Originator is accurate
Real world structures are more complicated and these conditions can be programmed
Trustee Auditor Rating
Lower Cost Speed
Security Transparency
Regulator Lawyer

16. Structured Finance – let your imagination run wild
Structured
Credit
Securitisation
Existing Assets
Retail Loans
Wholesale
Loans
Future Flows
Risk transfers and other structured products
Credit Insurance Weather
Unstructured Finance
Mezzanine ABS/RMBS CDO/CMBS
Pooling of loans by
banks or financers
Why What How
monetize
predictable cash
flows expected
from business
Financers is
one of the
many types
of Originators
Structured Finance Products can have
characteristics of bonds, stocks, insurance…

17. Agenda
• Gaps in Supply demand dynamics of money
Why Structured Finance?
• Getting tomorrow’s money, today
What’s the solution for financing problems?
• Can math and models go wrong!? Incentives and assumptions do
How are loan pools analysed?
Questions and Discussions

18. Loan Loss Mathematics
Exp. Credit Loss
ECL = PD*LGD
Borrower
capacity and
behaviour
Probability
of default
PD
E.g. 5%
Exposure
and possible
recovery
Loss Given
Default
LGD
E.g. 50%
Pool diversity
Correlation
E.g. 20%
Pool
granularity
Top n Loan
Concentration
Why What How
Credit Loss - case of a single loan
Credit Loss - case of a loan pool
Probability not meaningful
Deterministic test
PD of pool = function (loan
PDs, correlation)

19. 0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Probablity
%
Loss%
Simplified Model
Why What How
Case of a single loan Case of a loan pool
To default or
not to default
Bernoulli
Random
Variable
P(X=1) = P(default) = PD
P(X=0) = 1-PD
Binomial
Distribution
N trials of
Bernoulli
experiment
P(X=n) = P(n loans default)
n <= N
Case of a large loan pool Bring in correlation
Large N -> Binomial approaches Normal!
0%
1%
2%
3%
4%
5%
3.0% 3.5% 4.0% 4.5% 5.0% 5.5% 6.0% 6.5% 7.0%
Probability
%
% of loans defaulting in pool
For PD = 5%, N = 2000
Source
Correlation of 5%, 20%, 50%, 90%. PD = 10%

20. Use of Data Science for PD & LGD
Why What How
Originator’s portfolio
Analyse originator’s portfolio data (old and new)
to understand overall financer performance
Investors analyse which kind of loans are
performing and select such kind in the pool
Pool is compared with portfolio to predict
possible future defaults in pool (PD)
Descriptive
Analytics
Diagnostic
Analytics
Predictive
Analytics
Pool to be securitised
All loans given out by originator, old
and live constitute population
Loans part of pool to be securitised
is a sample set of above population

21.  Securitisation
 Structured Credit
 Blockchain and tokenization
 Smart contracts
 Correlation of defaults play an critical role
 Correlations increase during crisis
 Loans sold down is a sample of the lender’s overall portfolio
 Big data capabilities may further improve credit decisioning and pool selection
Securitisation has inherent inefficiencies that may be overcome by bringing in
Summary
Structured finance reduces frictions and fills gaps in the risk-return spectrum
While analysing losses in a collection of loans
Finance has been using data analytics concepts for quite some time

22. Agenda
• Gaps in Supply demand dynamics of money
Why Structured Finance?
• Getting tomorrow’s money, today
What’s the solution for financing problems?
• Can math and models go wrong!? Incentives and assumptions do
How are loan pools analysed?
Questions and Discussions

23. Thank You
pramax@gmail.com
www.linkedin.com/in/pramax

24. Bonus Slide 1: Deal Process
Origination
Minimum
Holding
Period
Due
Diligence,
Credit
Assessment
Deal
Structure
Deal
Execution
Warranties,
Servicing,
Disclosures
Originator
Investor
Trustee
Borrowers
Collections
Trust
Originator
Info
Collection, recovery
Trustee Auditor Rating
Regulator Lawyer
Investors

25. Bonus Slide 2: Issues with Securitisation
• Data storage, reconciliation, transfer, and transparency across multiple
independent entities
• Regulators not getting systemic view of the ownership of the underlying
securitized assets
• Smaller entities face information asymmetry and network disadvantages
• Time lag in info and payment flows leading to counterparty risk and capital tie-up
• Fraud risk - no assets, double-pledging assets
Issues with current market infrastructure
• Consistency: A single source of information (truth?) for all participants
• Audit trail: Chronological and immutable trail of all transactions avoiding fraud
• Investment appeal: Realtime valuation, price discovery & deeper investor pool
• Disintermediation: Increase in speed, lower costs, certainty of execution
Blockchain’s proposed advantages