mobilizing big data to support investigations into fraud ...€¦ · mobilizing big data to support...

Mobilizing Big Data to support

investigations into fraud & corruptionThe case of public procurement

Mihály FazekasCentral European University and

Government Transparency Institute

[email protected]

2018. 10. 31. 1

19th Conference of International

Investigators, Incheon, South Korea,

10-11/10/2018

mailto:[email protected]

Two main topics

1. Recent innovations in corruption

measurementGlobal availability of data and risk indicators

2. Data can be mobilized for investigations

Currently available or easily acceible data can be used for

risk mapping as well as investigation support

2018. 10. 31. 2

Why would you care?

2018. 10. 31. 3

‚Fat’ Leonard & the corruption probe of 60 admirals

I. Corruption proxies & underlying

data

2018. 10. 31. 4

Corruption definition

In public procurement, the aim of corruption is to steer the contract to the favored bidder without detection. This is done in a number of ways, including:

– Avoiding competition through, e.g., unjustified sole sourcing or direct contract awards.

– Favoring a certain bidder by tailoring specifications, sharing inside information, etc.

See: World Bank Integrity Presidency (2009) Fraud and Corruption. AwarenessHandbook, World Bank, Washington DC. pp. 7.

2018. 10. 31. 5

Conceptualizing public procurement

corruption indicators

October 31, 2018 6

Contracting

bodySupplierContract

Particularistic tie

Tendering Risk Indicators

(TRI)

Supplier Risk

Indicators (SRI)

Contracting Body

Risk Indicators

(CBRI)Political

Connections

Indicators (PCI)

Source: Fazekas, M., Cingolani, L., & Tóth, B. (2016). A comprehensive review of objectivecorruption proxies in public procurement: risky actors, transactions, and vehicles of rent extraction: GTI-WP/2016:03. Government Transparency Institute. Budapest.

What kinds of data can help anticorruption?

A complex data template

• Public procurement data: contract-level

• Company data: registry, financials, ownership/management

• Political officeholder data

• Treasury accounts of public organisations

• Legal challenge, prosecutions, debarrments

2018. 10. 31. 7

Ok, but where is this data?

Sometimes

in text files

2018. 10. 31. 8


Sometimes in

structured html

2018. 10. 31. 9


2018. 10. 31. 10

RARELY in

downloadable

dataset (e.g.

json)

Open data available: DIGIWHIST,

BA/DFID & beyond

2018. 10. 31. 11

Unprecedented open data available!

1. Full European data&indicators on DIGIWHIST watchdogportals: https://opentender.eu

17.5 million contracts, 32 countries+EC

2. Development aid funded procurement+selected developingcountries: World Bank, IDB, Europeaid + Tanzanian nationaldata (www.govtransparency.eu/index.php/category/databases

3. Approach scaleable and standardized: ongoing work in▪ Selected developing countries’ national data: Brazil, Chile,

Colombia, India, Indonesia, Jamaica, Mexico, Paraguay, South Africa , Uganda

▪ Selected developed countries: US(federal contracting)

If you are interested, get in touch, happy to share data and collaborate!

https://opentender.eu/

http://www.govtransparency.eu/index.php/category/databases

2018. 10. 31.

Modelling corrupt

contracting: single bidding

12

Distribution of contracts according to

the advertisement period

Probability of single bid submitted for contracts

compared with the market norm of 48+ days

Source: EU’s Tenders

Electronic Daily (TED),

Portugal , 2009-2014

Single bidding

Tight deadline

2018. 10. 31. 13

Lack of competition ~ single bidding(on national competitive markets)

Single bidding correlates with subjective indicators of corruption

SRI: company age at the time of

winning a contract

Ms. Winston Wolkoff, 6 weeks old company & the 26 million USD

SRI: Expected success of companies by

age

1. Gradual

build-up of

contracts

2. Natural

fluctuation

over time

2018. 10. 31. 15

SRI: Observed success of companies at

‚specific’ ages

2018. 10. 31. 16

1. ‚Just’ founded

companies

2. Companies

founded

under party

last in power

Hungary, 2010

Source: Fazekas, M., Lukács, P. A., & Tóth, I. J. (2015). The Political Economy of Grand Corruption in Public Procurement in the Construction Sector of Hungary. In A. Mungiu-Pippidi (Ed.), Government Favouritism in Europe. The Anticorruption Report 3 (pp. 53–68). Berlin: Barbara Budrich Publishers.

Clusters of risk: young firms

2018. 10. 31. 17

Source: Fazekas, Mihály , & Tóth, Bence, (2017), Proxy indicators for the corrupt misuse of corporations. U4 Brief. October 2017:6. U4 -Chr. Michelsen Institute, Bergen, Norway.

2018. 10. 31.

18

Surprise success

goes together with

procurement red

flags (CRI)

SRI: Politically driven company success: Hungary a paradigmatic case

Companies lose/win

surprisingly when

government changes

Hungary, 2009-2012

Source: Dávid-Barrett, Elizabeth and Fazekas, Mihály, (2016). Corrupt Contracting: Partisan

Favouritism in Public Procurement. GTI-WP/2016:02, Budapest: Government

Transparency Institute.

2018. 10. 31.

19

Few companies

lose/win surprisingly

when government

changes

UK, 2009-2012

Surprise success

sometimes goes

together with

procurement red flags

(CRI)

SRI: Politically driven company success: UK exception to the rule

Source: Dávid-Barrett, Elizabeth and Fazekas, Mihály, (2016). Corrupt Contracting: Partisan

Favouritism in Public Procurement. GTI-WP/2016:02, Budapest: Government

Transparency Institute.

Minimum data

scope for a

corruption&

collusion risk

assessment

See:

https://opentender.eu/

blog/2017-03-towards-

more-transparency/

Variable group Variable

BuyerBuyer’s nameBuyer’s unique IDBuyer’s address

Bidder / bids

Bidder’s nameBidder’s unique ID/tax IDBidder’s address Number of bids submittedNumber of bids excludedBid price (details on total and unit prices)Exact time of bid submissionBid type (winner/loser bid)Beneficial owners

Tender / contract

Procedure typeFramework agreement (1st/2nd stage)Estimated price (details on total or unit prices)Procurement type (service, supply, work)CPV codes (by product type weight)NUTS code(s) of contract implementationStatus (cancelled, pending, etc.)

Dates

Call for tender publication dateBid submission deadlineContract start and end datesPublication date of contract awardDate of contract completion

Subcontracting Subcontractor’s name and unique ID (tax ID)Subcontractor’s share

Consortium Consortium members’ name and unique ID (tax ID)Consortium members’ share

Contract performance

Contract performance end date

Was performance according to the contract

Explanation in case of deferring from contract

Information on contract modification

Information on performance qualityOctober 31, 2018 20

https://opentender.eu/blog/2017-03-towards-more-transparency/

II. Risk management tools

2018. 10. 31. 21

Using data for corruption prevention

1. Risk assessment:– Mezo-level (e.g. sectoral, regional)– Counterpart-level– Project/tender-level

2. Policy advice: e.g. identifying vulnerabilities&trends

3. Automatic compliance checks: e.g. applyingprocurement rules

2017.09.14. 22

Sectoral risk scoring:

infrastructure subsectors

2018. 10. 31. 23Source: Fazekas, M. & Tóth, B. (2017), Infrastructure for whom? Corruption risks ininfrastructure provision across Europe. In Hammerschmid, G, Kostka, G. & Wegrich, K. (Eds.), The Governance Report 2016. Oxford University Press, ch 11.

2018. 10. 31. 24

Corruption risks in infrastructure spending

by region

Some regions

in otherwise

low corruption

risk countries

carry high

risks

Source: Fazekas, M. & Tóth, B. (2017), Infrastructure for whom? Corruption risks in infrastructureprovision across Europe. In Hammerschmid, G, Kostka, G. & Wegrich, K. (Eds.), The GovernanceReport 2016. Oxford University Press, ch 11.

Organisational risk scoring: EIB exampleEIB counterpart avg. organisational risk scores

General PP behavior ~ EIB funded procurement behavior

250,000+ tenders, 10 tailored red flags

2018. 10. 31. 25

02

04

06

08

0

Fre

que

ncy

0 .2 .4 .6 .8(mean) cri_eib

Corruption & overpriced projects

2018. 10. 31. 26

Source: Fazekas, M. & Tóth, B. (2017), Infrastructure for whom? Corruption risks ininfrastructure provisionacross Europe. InHammerschmid, G, Kostka, G. & Wegrich, K. (Eds.), The Governance Report 2016. Oxford University Press, ch11.

2018. 10. 31. 27

Corruption and road prices

Source: Fazekas, M. & Tóth, B. (2017), Infrastructure for whom? Corruption risks ininfrastructure provision across Europe. In Hammerschmid, G, Kostka, G. & Wegrich, K. (Eds.), The Governance Report 2016. Oxford University Press, ch 11.

Considerable clustering of risks

buyer-supplier bimodal network

N org. contract >=5 or <=50

2017.09.14. 28

Complex risk scoring: Corruption

risks cluster in contracting networks

2018. 10. 31. 29

i) ii)

iii) iv)

80

85

90

95

100

avera

ge

ann

ou

nce

men

t com

ple

ten

ess

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015year

Continental Western Europe Anglo-Saxon

Scandinavia Central and Eastern Europe

Mediterranean Europe

46

81

01

2

avera

ge

bid

de

r n

um

ber

(tri

mm

ed

)

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015year




05

10

15

20

avera

ge

sa

vin

gs r

ate

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015year




01

02

03

0

sin

gle

bid

din

g

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015year




Quality of governance change over time

Source: Fazekas, Mihály, (2017): Assessing the Quality of Government at the Regional Level Using Public Procurement Data. WP 12/2017, Brussels: European Commission, Directorate-General for Regional Policy.

Automatic compliance checks:

Misplaced tenders: avoiding TED

30

0

150

300

450

600

Num

ber

of te

nde

rs

-100000 -50000 0 50000 100000Estimated tender value

from national database from TED

Source: Tóth, B., Fazekas, M. (2017): Compliance and strategic contract manipulation around single market regulatory thresholds – the

case of Poland. GTI-WP/2017:01, Budapest: Government Transparency Institute. See:

http://www.govtransparency.eu/index.php/2017/08/28/compliance-and-strategic-contract-manipulation-around-single-market-regulatory-

thresholds-the-case-of-poland/

Number of contracts around the EU publication threshold – Services, central government, Poland

http://www.govtransparency.eu/index.php/2017/08/28/compliance-and-strategic-contract-manipulation-around-single-market-regulatory-thresholds-the-case-of-poland/

Potential contract slicing

Number of tenders around the EU publication threshold in 2010-2011 (left) and 2012-2013 (right) – Services, local government, Poland

31

0

200

400

600

800

Nu

mb

er

of te

nd

ers

100000 150000 200000 250000 300000

Tender value

0

200

400

600

800

100

0

Nu

mb

er

of te

nd

ers

100000 150000 200000 250000 300000

Tender value






Does gaming matter?

Ratio of single bidder contracts around the EU threshold (2010-2015) – local authorities, services, Poland

32

0.1

0.3

0.5

0.7

0.9

Me

an

of sin

gle

b

-40000 -20000 0 20000 40000estorfin_value_distance_ext_alt






3 options for tapping into Big Data

analytics

1. Use existing national systems: many countries

have sufficient national data (LINKING!)

– Can get started tomorrow☺

2. Invest into national systems: e.g. WB’s work in

Bangladesh

– Mid-term solution: 3-5 years until quality data is ready

3. Build on donor PP system

– Many donors have such a system or building one, BUT

it is hard to get right

2018. 10. 31. 33

Further readings: digiwhist.eu/resources

2017.09.14. 34

Fazekas, M., & Kocsis, G. (2017). Uncovering High-Level Corruption: Cross-NationalCorruption Proxies Using Government Contracting Data. British Journal of PoliticalScience, available online.

Fazekas, Mihály, (2017): Assessing the Quality of Government at the Regional Level Using Public Procurement Data. WP 12/2017, Brussels: European Commission, Directorate-General for Regional Policy.

Dávid-Barrett, Elizabeth, Fazekas, Mihály, Hellmann, Olli, Márk, Lili, & McCorley, Ciara, (2017): Controlling Corruption in Development Aid: New Evidence from Contract-Level Data. SCSC Working Paper No. 1. Sussex Centre for the Study of Corruption, University of Sussex.

Tóth, B., Fazekas, M. (2017): Compliance and strategic contract manipulation around single market regulatory thresholds – the case of Poland. GTI-WP/2017:01, Budapest: Government Transparency Institute.

Fazekas, Mihály , & Tóth, Bence, (2017), Proxy indicators for the corrupt misuse of corporations. U4 Brief. October 2017:6. U4 - Chr. Michelsen Institute, Bergen, Norway

Fazekas, M. & Tóth, B. (2017), Infrastructure for whom? Corruption risks in infrastructureprovision across Europe. In Hammerschmid, G, Kostka, G. & Wegrich, K. (Eds.), The Governance Report 2016. Oxford University Press, ch 11.

Fazekas, M., Cingolani, L., & Tóth, B. (2016). A comprehensive review of objectivecorruption proxies in public procurement: risky actors, transactions, and vehicles of rent extraction: GTI-WP/2016:03. Government Transparency Institute. Budapest.

Fazekas, M. and Tóth, I. J. (2016). From corruption to state capture: A new analytical framework with empirical applications from Hungary. Political Research Quarterly, 69(2).

Dávid-Barrett, Elizabeth and Fazekas, Mihály, (2016). Corrupt Contracting: Partisan Favouritism in Public Procurement. GTI-WP/2016:02, Budapest: Government Transparency Institute.

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