This document discusses intelligent data insights in central banks. It summarizes the data and statistical production processes used by central banks, including credit risk data, securities holdings, and monetary financial institution statistics. It also describes standardization efforts including validation and transformation languages to guide data extraction and processing. Finally, it provides examples of execution platforms used by the Bank of Italy to implement logical representations and transformations on statistical data.

1. Towards intelligent data insights in central banks
Roma, 24th Feb 2017
Luigi Bellomarini, IT Department
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Challenges and opportunities for declarative languages

2. WHAT INSIGHTS
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• Credit and credit risk data & Institutional Units register
• Securities Holdings Statistics & Securities register
• Monetary Financial Institutions Balance Sheets Statistics
• Monetary Financial Institutions Interest Rates Statistics
• Balance of Payments
• National Accounts
• Single Supervisory Mechanism
• Regulatory frameworks

3. THE DATA PRODUCTION PROCESS
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BANKSINPUTLAYER
Banks
Operational
Systems
BANKSOUTPUTLAYER
PRIMARYREPORTING
Transformations by
banks
NATIONALSTATIST.PRODUCTION
SECONDARYREPORTING
SUPRANATIONAL
STATISTICALPRODUCTION
Transformations by
central banks
Transformations by
international institutions

4. STANDARDIZATION
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• Of processes, models and languages
• Guide the process in the banks
• Extract the data into harmonized models
• Standardize the transformations
• Validation & Trasformation Language (VTL)
GSBPM
Information ModelProcess Model
VTL
Operand Operand
Expression
Result

5. VTL: A STANDARD LANGUAGE (FROM SDMX INITIATIVE)
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High-level and business oriented
• Fully declarative approach
• Logic and functional paradigms
Mathematical functions are first-class objects
• VTL manipulates data as mathematical functions
• Based on operators (higher-order functions)
Sector
City
Reference Date
Loans = 9.876.543
Deposits = 10.234.567
Loans value type = measured
Deposits value type = estimated
Naples
Private
31 Dec
2010
Dimensions Measures Attributes
City Reference
Date
Sector Loans Deposits
Loans
value
type
Deposits
value type
Naples 2010 12 31 private 9.876.543 10.234.567 measured estimated
Naples 2010 12 31 public 543.210 654.321 measured measured
Naples 2009 12 31 private 9.210.876 10.987.654 estimated estimated
Naples 2009 12 31 public 876.543 1.654.123 measured measured
… … … … … …
Rome 2010 12 31 private 1.234.567 1.546.897 measured measured
… … … … … … ,,,

6. VTL – A GRAPH OF TRANSFORMATIONS
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Banks & OFIs reports …
D1
D2
D3
D4
D5
T1
T3
T2
D10
D12
D13
D15
D17
D16T13
T12
T1
4
Other data sources
D51
D52
T53
T52
T51
Economic research models
D54
D53
T54
C.C.R.
D21
D22
D23
D24T22
T21
D60
D61
Statistical bulletin
T60
T61
Statistical products
D70
T71
T70
T72D71
D72
D41
T42
T41
D42
Supervision models
D4 = get ( D1_LOANS_FLOW, keep (DATE, BANK, AMOUNT), sum (AMOUNT))
D5 = get ( D2_LOANS_STOCK, keep (DATE, BANK, AMOUNT), sum (AMOUNT))
D6_CHECK = check( D5 = lag(D5, -1) + D4)
It’s a DAG!

7. EXECUTION PLATFORMS - @Bank of Italy
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VTL
RGDP := PQR * RGDPPC
tmp <- merge(PQR,RGDPPC,by=c("q","r")) tmp$i <-
tmp["p"] * tmp["g"]
TGDP <- tmp[-c("p","g")]
Rgdp = get_tab(pqr * rgdppc)
INSERT INTO RGDP(Q,R,P)
SELECT C2.Q AS Q, C2.R AS R, C1.P*C2.G AS P
FROM PQR C1 , RGDPPC C2
WHERE C1.Q = C2.Q AND C1.R = C2.R
PQR(q,r,p), RGDPPC(q,r,g) à ∃𝑧 RGDP(q,r, z)
User specification
Logical representation
IT implementation

8. INFOSTAT
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9. FROM A SET OF RULES ... TOWARDS A KNOWLEDGE BASE
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Metadata-driven system
Declarative representation
Active dictionary
Integrated approach
NO INFERENCE
• Knowledge base
generation
• First Order Languages
REASONING
AI
Cognitive Computing

10. CREDITS
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• SDMX TWG and VTL task force (www.sdmx.org)
• Statistical Data and Concept Representation
The Banca d’Italia’s Active Statistical Metainformation System,
Modelling Levels in the Statistical Information System of Bank of Italy,
The “Matrix” Model: unified model for statistical representation and proces
sing, Vincenzo Del Vecchio, Fabio Di Giovanni et al. (https://www.bancaditalia.it/
statistiche/raccolta-dati/sistema-informativo-statistico/index.html)
• BIRD project (http://banks-integrated-reporting-dictionary.eu)
• GSBPM (http://www1.unece.org/stat/platform/display/GSBPM/GSBPM+v5.0)
https://creativecommons.org/licenses/by-nc-sa/3.0/