The document discusses the implementation of distributed ledger technology for OTC trading, highlighting the shift from traditional methods, which involve direct payment and lack of shared records, to a decentralized architecture using Hyperledger Fabric. It outlines the benefits of a consortium model where organizations maintain control over their clusters and the introduction of functionalities like 'private collections' for managing trade data. Additionally, it presents examples of transaction ledgers and interest netting, reinforcing the potential of blockchain in enhancing transparency and efficiency in trading frameworks.

1. DISTRIBUTED LEDGER TECHNOLOGY
FOR OTC TRADING
Vitaliy Chernov
VP Blockchain Practice,
Blockchain Trainer at Altoros

2. How it works now
▪ Trade proposals / indicated quotes:
by phone or electronic messaging systems
provided by third parties
▪ Payment/asset settlement is processed between
counterparties directly: No clearing platform
▪ No shared records keeping system
▪ Additional confirmations of deals concluded
between parties are often necessary
▪ No history of deals = no market analysis
Source: slideplayer.com

3. How it will work: Decentralized Architecture
• The consortium of several equal Organizations
• Communicating in a Common Channel of Hyperledger Fabric
• Kafka-Orderer has a configurable structure
• No central authority:
each Organization controls its own Kafka-cluster
Kafka-
Orderer
Bank
“A”
Bank
“B”
Bank
“C”
Bank
“D”
Cluster
B
Cluster
C
Cluster
D
Cluster
A

4. How it will work: expanding network
Kafka-
Ordere
r
Bank
“A”
Bank
“B”
Bank
“C”
Bank
“D”
Bank
“E”
• The consortium of several equal Organizations
• Communicating in a Common Channel
• Kafka-Orderer has a configurable structure
• No central authority:
each Organization controls its own Kafka-cluster

5. How it will work: expanding network
Kafka-
Ordere
r
Bank
“A”
Bank
“B”
Bank
“C”
Bank
“D”
Bank
“E”
Bank
“F”
• The consortium of several equal Organizations
• Communicating in a Common Channel
• Kafka-Orderer has a configurable structure
• No central authority:
each Organization controls its own Kafka-cluster

6. How it will work: “private collections” function
Dashboard: EUR overnight DEPO
Lender ID Amount_Lend Rate Amount_Borrow Borrower ID
HRSZ 10,000,000 1.000%
BRUC 2,000,000 0.875%
TICY 5,000,000 0.750%
MUNC 1,000,000 0.625%
0.500% 2,000,000 NYBO
0.375% 1,000,000 DUCE
0.250% 5,000,000 SWIZ
0.125% 10,000,000 SBRE
Trade ledger
Timestam
p Amount Rate Borrower Lender
10:00:01 5,000,000 0.500% ##### #####
10:00:05 3,000,000 0.375% ##### #####
10:00:09 2,000,000 0.250% ##### #####
10:00:13 10,000,000 0.375% ##### #####
10:00:17 2,000,000 0.500% ##### #####
10:00:21 1,000,000 0.750% ##### #####
“private collections”

7. Aftermarket reports
Aftermarket clearing Value Dates:
Node: TICY 9/7/2018 9/8/2018
Cash out Cash in Counterparty
(5,000,000) NYBO 5,000,000
(3,000,000) DUCE 3,000,000
(10,000,000) SWIZ 10,000,000
5,000,000 SBRE (5,000,000)
3,000,000 HRSZ (3,000,000)
1,000,000 BRUC (1,000,000)
1,000,000 MUNC (1,000,000)
TOTAL, EUR (18,000,000.00) 10,000,000 8,000,000
Closing
balance (8,000,000)
9/7/2018
Interest netting, EUR
Receivable Payable Counterparty Value Date
68.49 NYBO 9/8/2018
30.82 DUCE 9/8/2018
68.49 SWIZ 9/8/2018
85.62 SBRE 9/8/2018
82.19 HRSZ 9/8/2018
23.97 BRUC 9/8/2018
17.12 MUNC 9/8/2018
TOTAL, EUR 167.81 208.90
netted, EUR (41.10)

8. What is inside the Chaincode
Function “Query” compiles the result from two parts:
• Public data
• Private collections
Trade ledger
Timestam
p Amount Rate Borrower Lender
10:00:01 5,000,000 0.500% NYBO TICY
10:00:05 3,000,000 0.375%
10:00:09 2,000,000 0.250%
10:00:13 10,000,000 0.375%
10:00:17 2,000,000 0.500%
10:00:21 1,000,000 0.750% TICY BRUC
@Altoros

9. What is inside the Chaincode
• Public • Private
The data stored separately:

10. Non-financial use case: customer reward

11. Blockchain for Enterprise.
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Architecture and Functional Use
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