Enhanced data usage for improved efficiency and growth

Enhanced data usage for

improved efficiency and

growth

27 February 2017

Tom Alaerts, Head of Business Solutions, APAC

Geertjan van Bochove, Head of Analytics, APAC

Business Forum Singapore | enhanced data usage | February 2017 2

Major challenges at banks: not enough data readily available

Now mainly tactical stand-alone solutions to cope with business and regulatory demand

Intraday regulatory

reporting solution

Cash

Management

function

Forecasting

LCR

Financial

Crime

Compliance

Timely and accurate data

drive business and reporting

value chain

Reconciliation

Business Forum Singapore | enhanced data usage | February 2017

Technological innovation | Apache Hadoop

An open-source framework that allows for the distributed processing of large

data sets across clusters of commodity computers using a simple

programming model.

3

Scalable infrastructure

Cost effective

Fast *

Resilient to failure

Storage requirements

Native security * HDFS = Hadoop Distributed File System

? Legal boundaries of data ?

? Aggregation of data ?

? Access to data policies ?


Major challenges at banks: Local versus Enterprise data governance

Payments

data

AU

Payments

data

SG

Analyst

US


Major challenges at banks: Data quality not consistent

Do you link to all possible data-sources?

Do you know the definition of all data-sources?

Do you know what data has been

processed?

Transparency = confidence

Are all data-points connectable?

Do the data-objects accurately represent ‘real

world’ values?

Definitions of formats (i.e. dd/mm/yyyy)

used across the board?

Do values get the same status across the

board?

Chief data officer guarding data quality


Connecting back-offices and data-points a major challenge at banks

US

Source 1

Reference table / key for

software to find records

AU

Source 2 Other

data 50

Other

data 51

. . . . . .

Customer input

Processing at the bank

Clearing Settlement Finality


ISO20022 supports links between financial transaction data and defines common data

types and the understanding of those data types

Reusable Data Types

& Components

Message Definitions

Business Processes

Business Model

Static Data Dictionary

“The Concepts” Dynamic Business Process

Catalogue

“The Context”

A full payment chain – example

Process diagram can be used for drill down visualisations

Debtor’s

Agent

Ordering

Institution

Instructing

Agent

Sender

Instructed

Agent

Receiver

Creditor’s

Agent

Account With

Institution

Debtor

Ordering

Customer

Creditor

Beneficiary

Customer

Your Institution

Mexico

Local Bank

Mexico

Your Institution

HK

Local Bank

China

SWIFT

SWIFT



How does SWIFT support the

community to overcome this data

challenge?


Banks’ cross-border business under pressure

Liquidity

and

Funding

Financial

Crime

Compliance

NSFR LCR ILR FATF Sanctions Wolfsburg

Costs Competition

Your Back office apps

IBAN

validation

Correspondent

SSIs

Clearing

System

members

etc

MT 103, ISO 20022 PACS008...

…

Your institution

Correspondent / Counterparty

FileAct

routing National sort

codes incl

local language

BICs

Costs

Good, complete ref data in your messages ensures

smooth, correct processing


12

Bank

holidays

Payments

Routing

information

> 820,000

Standing

Settlement

Instructions

> 1,000,000 National bank

identifiers (181 countries)

250

Country

codes

IBAN data of

71 countries,

including

36 SEPA

countries

417,000

Legal

Entity

Identifiers

ALL

Currency

codes

200,000

local

language

codes incl

CNAPS,

Zengin…

FileAct

addresses

Clearing

Systems

membership

112,000

BICs

Credit

ratings

SWIFTRef data – a rich database with much more than BICs, up to date and

always sourced from the original publisher.

SWIFTRef

Costs


13

System error,

42.10% Insufficient reference

data, 57.90%

Non SWIFTRef subscriber

Invalid BIC

destination 26.32%

Branch code

error 15.79%

BIC11 error 10.52%

Address error 5.26%

System error, 92.31%

Insufficient reference

data, 7.69%

SWIFTRef subscriber

Importance of accurate reference data

Costs



SWIFTRef – Value Proposition

14

Bank data

Build your Master Bank Database

Identify Financial Institutions

International payment validation

National ID – BIC translation

Cross-reference of more identifiers

(CHIPS, LEI…)

Understanding bank hierarchy

IBAN/SEPA data

Validate IBANs and BIC codes

Find BICs from IBANs

Construct IBANs from BBANs

SSI data

Find the beneficiaries’ BIC

Find the correspondent bank

Find the intermediary bank

ISO country codes

ISO currency codes

Commercial payments, FX, MM

Payments Plus (XML data feeds or TXT files)

Bankers World Online (online tool) Look-up all the data contained in SWIFTRef and more (i.e correspondent banking data)

Bank Directory Plus (file)

BIC Plus (file)

Entity Plus (file)

Reach Plus (file)

IBAN Plus (file) SSI Plus (file)

Costs


Business intelligence portfolio

Competition

Services RMB tracker

Products

Global Transaction

Banking

Watch Analytics

SWIFT Scope

On-site Business Intelligence

Workshops & Training

Enriched Watch

data Cash & Trade

SWIFT Business Intelligence products & services support various business lines and give opportunities

to optimise functions and understand where business growth sits in the market

Central banks

Peer benchmarking

Corporate Business

Development report

Watch Analytics

Premium

RMB Market

Insights

NEW

Global Transaction

Banking

ASEAN commercial cross-border flows 2016 (first 3 quarters) cross-border MT103 value sent and received

Source: SWIFT Watch Analytics


• 27% commercial flows are intra-ASEAN

• Extra-ASEAN flows are mainly with the USA, UK, Hong Kong, China and Japan

SG

ID

MY

PH

BN

VN

MM KH

LA

TH

United States

Hong Kong

United Kingdom

Japan

China

Australia

Netherlands

Korea, Republic of

Taiwan

Switzerland

United Arab Emirates

France

India

Germany

Belgium

Others

Extra-ASEAN Commercial Flows in 2016 by value

Outgoing pyaments Incoming payments

Competition

Bank C

Bank A + B average

Bank D

Bank E Bank F

Bank G

Bank H

Bank J

Bank K

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

-40% -30% -20% -10% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Benchmarking Payments and trade finance in Hong Kong


Competition

Trade value growth 2015/2016

Payment volume growth

2015/2016

*Size of the bubble

represents the value

of payments and trade

combined for 2016

18

New Banking Analytics Premium

Banking

Analytics

Premium

Business & Strategy

WHAT:

New set of data insights

In Cash & Payments

And Trade Finance

WHY:

• New business opportunities

• Strategic and competitive analysis

• Focused sales force

• Insightful market practices

• Etc.

Payments

Intermediation

Payments

Details of

Charges

Trade LC’s

Confirmations

Trade LC’s

Length

MT103s & MT202s

WHAT:

. Details of charges (field 71A)

. Instructed currencies (field 33B)

. Initial Ordering and End Beneficiary

Countries (field 52A and 57A/58A)

WHY:

• Map clearing business

• Discover new revenue streams

• Benchmark payments charges

against market practices

• Understand your STP efficiency

• Focus on high value payments

MT700 & MT400

WHAT:

. Confirmation instructions (field 49)

. L/C tenor length buckets

(field 31C/31D)

. Credit availability (field 41A)

WHY:

• Increase LCs individual margin

• Understand Market trends

• Assess Potential of LCs corridors

• Request faster LCs payments

• Look LCs confirmed LCs locations

• Focus on high value LCs

RA

NK

ING

/ D

AIL

Y D

ATA

/ C

UR

RE

NC

Y S

TA

TS

/ E

tc.

Competition



SWIFT Intraday Liquidity Reporting Survey

Banks share the same data management challenges

Lack of Business

Practice for Intraday

Reporting for

Nostro & LVPS

Accounts

Lack of Data

Centralisation

No Practice on Data

Model

for Regulatory

Reporting

Too low transaction’s

coverage

Lack of timeliness vs.

time stamping

Transaction types:

“ book transfers”

No defined use cases

Decentralised

management of Nostro

accounts

No central treasury

application

Different messaging

instances

Lack of definition for

regulatory tools (e.g.

credit lines, time specific

obligations)

No common high level

data model beyond

message types (e.g. extraction, data

aggregation)

Liquidity

and

Funding


SWIFT messaging covers the main data challenges for intraday

liquidity monitoring

Source: Industry feedback from 35 individual banks workshops on BCBS 248

Source: BCBS

Daily maximum intraday liquidity usage “Use of transaction-by-transaction data of movements on the account(s)”

Start of the business day

Largest positive net

cumulative position

Largest negative net

cumulative position

Net

cum

ula

tive p

ositio

n

• Many banks have historically worked based on their

internal projections systems

• There are reporting coverage and consistency issues

both as a Direct Participant of LVPS and as a user of

Correspondent Banking services

SWIFT messaging key source of critical

transactional data for real-time liquidity monitoring

Liquidity

and

Funding

20


Data consumption globally by banks – what do we see?

Liquidity

and

Funding


How can SWIFT help with ILR in practice?

Review

As-is (optional)

Use SWIFT data to understand liquidity flows at all levels

Gap

analysis

Leverage SWIFT experience to assess data coverage and reporting readiness

Data

consolidation

Data capturing from all sources relevant for intraday liquidity, SWIFT and non-SWIFT flows

Data

transformation

/ normalization

Transform and normalize liquidity information flows, establish data logic and calculation metrics

Visualization

and reporting

Generate key

metrics

Monitor account

positions

Produce

regulatory

reports

Liquidity

and

Funding


SWIFT Scope – End-to-end real-time business intelligence solution

Collects all

necessary intraday

reporting data

Integrates and

processes the data

Produces key

metrics and reports

Liquidity

and

Funding

Enriching your ISO 20022-based data model for investigations

Debtor’s

Agent

Ordering

Institution

Instructing

Agent

Sender

Instructed

Agent

Receiver

Creditor’s

Agent

Account With

Institution

Debtor

Ordering

Customer

Creditor

Beneficiary

Customer

Your Institution

Mexico

Local Bank

Mexico

Your Institution

HK

Local Bank

China

SWIFT

SWIFT

Financial

Crime

Complian

ce

National

Sort

Code?

Bank Directory Plus

> 1,000,000 national sort codes

Entity Plus


Aggregated

amount towards

legal parent?

Reporting?

Intensity map (value/number of payments/change % since previous Q / etc)

from Mexico overview -> drill down

drill down

Financial

Crime

Complian

ce


Drivers behind entity identifiers, LEI, BIC

Many new regulations related to Risk Management are being imposed since the economic crisis.

Regulatory Reporting of the transactions done with counter-parties is an important component.

Regulatory Reporting on transactions requires

• To aggregate transactions per counter-party legal entity

• Mapping of proprietary IDs or BICs in transactions on the LEIs of the counter-parties

• Automation due to high numbers of counter-parties and it subsidiaries/branches

The BIC is an established identifier in financial transactions

• Counterparties and beneficiaries

• Addressing, SWIFT connectivity

The LEI is becoming an enabler for

• Risk management, transparency, transaction reporting

• Dodd-Frank, Emir, MiFID II, MiFIR, Solvency II, Basel III…

Regulations that require LEIs (Nov 2015)*

• 21 regulations mandate LEI

• 22 regulations require either LEI or another identifier 26 * Progress Report LEI ROC, 5 Nov 2015

Financial

Crime

Complian

ce

27

Entity Plus in numbers (December 2016)

Identifier cross-reference

file

1,158,000 Total Identifiers

472,000 LEIs

120,000 BICs

242,000 GIINs

231,000 BRNs

1,100 MICs

15,000 IDs added per month

Entity relationship

file

55,000 Total FI entities with a parent & ult.

parent relationships

87,000 BICs with a parent & ult. parent

relationships

16,550 GIINs with a parent & ult. parent

relationships

75,000 LEIs (Fis) with a parent & ult. parent

relationships

250,000+ FI’s legal entities

Financial

Crime

Complian

ce


28

SWIFTRef Entity Plus - Cross-referencing via parent entities

ING Luxembourg S.A.

BIC CELLLULLXXX

GIIN 5B7B1V.00014.ME.442

BRN B6041

EID 000OF60

ING Luxembourg S.A.

BIC CELLLULLTRD EID 001THFM

Domestic branch or

type of business

Head-Office

(legal entity)

ING Belgie N.V.

LEI JLS56RAMYQZECFUF2G44

BIC INGBBEB1XXX

GIIN 5B7B1V.00004.ME.056

BRN 403.200.393

EID 000S3RC Legal parent

(legal entity)

HEADOFFICE

OWNER *

* gradually populated

Financial

Crime

Complian

ce



Take-aways

Access to and

understanding of

data is key

Competitive,

regulatory and

cost pressure

make data-

driven banking a

necessity

SWIFT’s suite of

solutions is

tailored to

support your

data challenges

www.swift.com


Presentations & Public Speaking

Enhanced data usage for improved efficiency and growth