Interbank Payments and FinTech

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Interbank Payments and FinTech

Yuji Kawada Akiko Kobayashi Akihiko Watanabe Shuji Kobayakawa

Joint Conference by the Bank of Japan and the University of Tokyo “FinTech and the Future of Currency” November 18, 2016

Presentation Overview

1. What FinTech means for central banking 2. Application of DLT to interbank payments 3. Issues for further work

* The views expressed in this presentation are those of the authors and do not necessarily represent those of the Bank of Japan.

1

1. What FinTech means for central banking

Central bank money: Monetary base (JPY 413.9 tri)

Deposits at BOJ accounts (JPY 312.8 tri)

Banknotes issued (JPY 96.4 tri)

Coins in circulation (JPY 4.7 tri)

Central bank digital currency?

Distributed ledger technology (DLT) ? * Figures are averages for October 2016.

2

1. What FinTech means for central banking Possible application of distributed ledger technology Central bank digital currency Euro area ECB: Published staff research paper on application of DLT in securities post-trading (April 2016)

UK BOE: Conducted a PoC with the private sector on the transfer of ownership of a fictional asset (June 2016)

University College London: Published a paper on central bank digital currency (RSCoin) (February 2016)

Canada BOC: Launched a project with the private sector to explore the possibility of issuing, transferring and settling central bank issued assets on DLT (June 2016)

Russia BOR: Developed prototype of a networking tool for market participants using DLT (October 2016)

China

Sweden

PBOC: Announced plans to consider issuing digital currency in the long term (January 2016)

Riksbank: Announced intention to investigate the possible issuance of e-krona as a complement to cash (November 2016)

3

2. Application of DLT to interbank payments: Overview of staff study

 Objective: To deepen our understanding of the basic characteristics of the distributed ledger technology (DLT) by experimenting with a fictional DLT-based interbank payment system  Points of evaluation  Performance: how the number of nodes and the amount of traffic affect performance  Smart contract (Chaincode): whether complex operational flows can be realized on a DLT arrangement  DLT platform used: Hyperledger fabric

* We are grateful for the valuable inputs provided by IBM Japan, NTT Data, and Hitachi in conducting the study.

4

2. Application of DLT to interbank payments: Test environment  Environment: virtual machine on a standalone PC  Number of validating nodes: 4-16 nodes  Consensus algorithm: PBFT (Practical Byzantine Fault Tolerance) Standalone PC (Windows, 64bit, 8GB) Virtual machine(Linux, 64bit, 5GB) Chaincode

REST API Client

- Consensus - Execute chaincode - Update ledger (blockchain, KVS)

Validating Node 検証ノード(VP) 検証ノード(VP) A 検証ノード(VP) B C D

Certification Authority

Authentication for - Participants/nodes - Transactions

5

2. Application of DLT to interbank payments : Process flow Client X (Sender)

Validating Node A

Validating Node B

Validating Node C

Validating Node D

1. Client X sends payment request with digital signature to Validating Node A. Send Payment Request

Receive Payment Request

2. Validating Node A acknowledges receipt of payment request.

3. Validating Node A broadcasts payment request to other nodes.

Receive Payment Request

4. Validating nodes check digital signature, transaction serial number, etc. 5. Validating nodes execute the payment after more than 2/3 of nodes confirm the transaction. 6. Validating nodes record balances to KVS, and add transaction information (payment request, time stamp, etc.) as a new block.

Receive Payment Request

Receive Payment Request

Execute payment

Execute payment

Consensus

Execute payment

KVS

Block -chain

Execute payment

KVS

Block -chain

KVS

Block -chain

KVS

Block -chain

6

2. Application of DLT to interbank payments: Performance  Lower performance (longer latency between payment request and ledger update) as the number of nodes increases.  Increased delay in latency with increase in payment traffic (RPS, number of requests per second).

It took 12.5 seconds on average to process a traffic of 1,000 RPS under 7 nodes without CA.

7

2. Application of DLT to interbank payments: Performance  The extent of delay in latency caused by payment traffic increased with increase in the number of nodes. * Lower performance may be due to limitations in the test environment (e.g., CPU).

 Certificate Authority (CA), which issues Transaction Certificates for each transaction, could become a performance bottleneck; however, no significant impact was observed in this study. Block Commit Latency (NoCA, BatchSize=500, Loop=5) RPS10

RPS100

Effect of CA (BatchSize=500, Loop=5)

RPS1000

RPS10

30

RPS100

RPS1000

2.0 1.8 1.6

x19.6

Ratio (CA/NoCA)

Latency (seconds)

25 20

x15.3

15 10

x6.1

1.4 1.2

1.0 0.8 0.6 0.4

5

0.2 0

0.0 4

10 # of Nodes

16

4

10 # of Nodes

16

8

2. Application of DLT to interbank payments: Liquidity-saving features  Using “smart contracts” (chaincode), (i) centralized queuing and (ii) bilateral offsetting were programmed.

Payment instruction

Yes

Bilateral Offsetting

(Includes single gross settlement.)

Event-driven No

Settlement

Queue

No

Time-driven

Multilateral Offsetting

Yes

9

2. Application of DLT to interbank payments: Transaction data  Liquidity-saving features, using actual transaction data for a high-volume day (March 31, 2016 ), were tested.  Traffic reached its peak at around 9:00 with approximately 100 RPS and decreases thereafter. For this study, data for the period of 9:15-9:30 were used (approximately 12,000 transactions). Request Per Second via Queuing-offsetting Accounts on March 31, 2016 # of RPS

Entry-Bilateral Event-Counterparty

Entry-Single Multilateral

Event-Bilateral Cumulative Ratio

Event-Single 100%

90

90%

80

80%

70

70%

60

60%

50

50%

40

40%

30

30%

20

20%

10

10%

0

0% 7:32:42 7:46:04 7:57:08 8:08:11 8:19:14 8:35:32 8:52:21 9:05:55 9:17:11 9:28:47 9:39:59 9:51:47 10:03:23 10:15:58 10:28:18 10:41:12 10:54:24 11:08:14 11:22:56 11:38:13 11:53:39 12:08:12 12:25:22 12:45:27 13:02:38 13:18:42 13:34:43 13:50:35 14:05:38 14:21:28 14:38:06 14:54:05

100

Source: Bank of Japan.

10

2. Application of DLT to interbank payments:Preliminary results  Due to limitations in the test environment (insufficient CPU power), it took more than 60 minutes to send the requests for the period of 9:15-9:30, with average latency of 2.1 seconds and maximum latency of 10.8 seconds.

11

3. Issues for further work Tentative results • Increase in the number of validating nodes and transaction volume results in longer latency between payment request and ledger update. • Complex business flows such as queuing and offsetting functionalities can be implemented in a DLT arrangement by using smart contracts.

Issues for further work • Evaluate other aspects of DLT including availability (e.g., whether the arrangement can continue to function in the event under which one or more validating nodes are not properly functioning) • Take into account ongoing improvements in DLT (e.g., next-generation consensus algorithm planned for Hyperledger fabric) • Evaluate potential application of DLT platforms other than fabric • Enhance test environment in order to obtain a more accurate view on factors affecting performance 12

3. Issues for further work

Role of Central Banks Catalyst

Operator Overseer

Financial Market Infrastructure Safety

Efficiency

13

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Interbank Payments and FinTech

Interbank Payments and FinTech Yuji Kawada Akiko Kobayashi Akihiko Watanabe Shuji Kobayakawa Joint Conference by the Bank of Japan and the Universit...

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