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READINESS FOR EFFECTIVE COOPERATION – THE NEW

CHALLENGE FOR CENTRAL BANKS:CASE OF GEORGIA

B y N a n a A s l a m a z i s h v i l iH e a d o f M o n e t a r y S t a t i s t i c s D i v i s i o n

N a t i o n a l B a n k o f G e o r g i a

Workshop on the Implementation of the 2008 SNA in EECCA Countries and Linkages with BPM6 and GFSM

20146-8 May 2015, Istanbul, Turkey

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Outline of Presentation

• Introduction• SebStat: Step Forward Towards Innovative

Solution • How the data are structured?• SebStat as an additional data source for SNA

and BOP compilers• SebStat: How does it work?• Lessons Learned and Way Forward

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Introduction

• Against the background of rapidly increasing statistical standards and requirements National Bank of Georgia (NBG) carries out a consistent strategy for the sustainable development of statistics under its mandate.

• Moreover, expanding and improving our data sources and statistical production, in general, we strongly believe that we should think about other compilers of macroeconomic statistics also.

• This task is quite solvable with the recently launched completely new statistical business process model for National Bank of Georgia, so called SebStat.

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SebStat: Step Forward Towards Innovative solution

• SebStat is an innovative statistical business process model for NBG providing full range of possibilities to satisfy requirements of monetary and financial statistics, as well as the needs of various macroeconomic statistics, directly or indirectly.

• This can be achieved by structuring of statistical and financial data using standardized approach for all statistical domain under the NBG’s mandate.

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How the data are structured?

Is it difficult to define data structure properly?The answer is “Yes” and “No”.

In order to build the data structure several phases shall be done:– Identification of peer data groups to create proper

data families for Central Bank’s needs;– Elaboration of the Code Lists for each data families;– Development of appropriate methodology how the

financial instruments should be classified and structured properly by financial institutions;

– The room for further development should be left.

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How the data are structured? (example for monthly financial statement data structuring)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Data

Entry

Data

Cha

ract

eristi

cs

Clie

nts’

Char

acte

ristic

s

Addi

tiona

l Info

Attrib

utes

Financial Statement Data Structure

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How the data are structured? (example for monthly financial statement data structuring)

The structure of financial statement data (FIM_Data Family) consists of:• Data entries

– Data family– Source– Frequency

• Data characteristics– Financial/nonfinancial instruments– Assets/liabilities– Stock/flow– Maturity– Currency

• Client’s characteristics– Residency– Institutional sector– Type of economic activities– Region (if resident)

• Additional info– Additional info on loans– Loans collateral– Range (for loans&Deposits)

• Attributes– Interest rate– Measure type

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How the data are structured: Other data families

FEX_data Family - Foreign Currency

Transactions

• Data entries (data family, source, frequency)

• Type of transactions• Buying prices• Selling prices• Counterpart• Measure type

MTR_Data Family - Money Transfer

Operations

• Data entries (data family, source, frequency)

• Type of transactions• Type of wire transfer• Country (sender/receiver)• Currency• Measure type

BPC_Data Family - Payment Cards’

Statistics

• Data entries (data family, source, frequency)

• Card type and category• Type of transaction• Type of service post• Measure type

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SebStat as an additional data source for SNA and BOP compilers

1. Data sources to calculate the output of financial corporations

FIM_Data Family – Monthly Financial

Statements

FEX_Data Family – Foreign Currency

Transactions

• Output of Financial Institutions

• Financial Intermediation• Central Bank

• Monetary Policy Services• FISIM

• Deposit-taking Corporations• Explicit fees charged in lieu of providing

services• FISIM• Transactions in foreign currencies

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SebStat as an additional data source for SNA and BOP compilers2. Data sources to calculate the part of international transactions of Goods and Services Account of BOP

BPC_Family- Payment

Cards’ Statistics

• Goods and Services Account• Goods

• E-Commerce (to be added)

• Services• Travel• Additional information to financial

intermediation services, related with acquiring of payment cards

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SebStat as an additional data source for SNA and BOP compilers

3. Data sources for calculation of PIB & SIB items of BOP

MTR_Data Family- Money

Transfer Operations

• PIB - Primary Income Balance• Compensation of Employees

• SIB - Secondary Income Balance• Personal Transfers

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Financial/Nonfinancial InstrumentsAssets/Liabilities

Stock/Flow

Maturity

Currency

Residency

Institutional Sector

Type of economic activity

Region

Additional info on loans

Loan’s collateral

Range

Interest rate

Measure type

Monetary Gold&SDRs

Currency

Deposits

Securities other than shares

Loans

Shares and other equity

Insurance technical reserves

Financial derivatives

Other accounts receivable/payable

Nonfinancial assets

Report description (example)

Indicator: Loans

Assets

Stock

Maturity: 10 year and more

Currency: GEL

Counterpart description:

Residency: Resident

Sector: Nonfinancial corporation

Economic activity: Trade

Region: Kakheti

Additional info: SME loan

Collateral: Real estate

Range: 5000-25000

Interest rate:

Measure type: BV (book value)

SebStat: How does it work?

Next Skip Back

Generate

February

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Table 1. Loans granted by commercial banks, Jan 2010-Feb 2015 (Mln GEL)1.01.2010 1.02.2010 … 1.02.2015

Loans, total … … … …

SebStat: How does it work? (example)

01/01/1

0

01/04/1

0

01/07/1

0

01/10/1

0

01/01/1

1

01/04/1

1

01/07/1

1

01/10/1

1

01/01/1

2

01/04/1

2

01/07/1

2

01/10/1

2

01/01/1

3

01/04/1

3

01/07/1

3

01/10/1

3

01/01/1

4

01/04/1

4

01/07/1

4

01/10/1

4

01/01/1

50.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

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Lessons Learned

• Based on Georgian experience, it is obvious, that comprehensive multifunctional statistical data model for Central Bank is best solution in order to meet not only own statistical requirements, but also needs of other macroeconomic statistical systems compilers;

• The right cooperation strategy with data providers is essential, to ensure project success in terms of data relevancy and quality, and readiness for boosting joint effort aimed at strengthening of statistical capacity;

• Close cooperation with SNA, BOP and GFS compilers on the earlier stage of project designing is important to ensure data model comprehensiveness and methodological consistency.

• In addition to high level management support, it is very important to have the backing of international partners to raising awareness of the similar achievements on the national and international level, in order to get more benefit from each other’s experience and knowledge.

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Thank you------

Contact information: Nana Aslamazishvili

tel: (995 32) 2406 251, e-mail: naslamazishvili@nbg.gov.ge