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1 Irregularities statistics from draft 280 Annual Fight Against Fraud Report for 2008 Maria NTZIOUNI-DOUMAS OLAF Train the trainers European Commission.

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Presentation on theme: "1 Irregularities statistics from draft 280 Annual Fight Against Fraud Report for 2008 Maria NTZIOUNI-DOUMAS OLAF Train the trainers European Commission."— Presentation transcript:

1 1 Irregularities statistics from draft 280 Annual Fight Against Fraud Report for 2008 Maria NTZIOUNI-DOUMAS OLAF Train the trainers European Commission seminar for managing and certifying authorities 9 June 2009

2 2 CONTENT OF THE PRESENTATION Statistical evaluation of irregularities for the PIF report –Useful links –Purpose –Methodology –Content

3 3 USEFUL LINKS Main page –http://ec.europa.eu/anti_fraud/reports/anti-fraud_en.htmlhttp://ec.europa.eu/anti_fraud/reports/anti-fraud_en.html Main Report –http://ec.europa.eu/anti_fraud/reports/commission/2007/ en.pdfhttp://ec.europa.eu/anti_fraud/reports/commission/2007/ en.pdf First Annex –http://ec.europa.eu/anti_fraud/reports/commission/2007/i nventaire_en.pdfhttp://ec.europa.eu/anti_fraud/reports/commission/2007/i nventaire_en.pdf Statistical Annex –http://ec.europa.eu/anti_fraud/reports/commission/2007/ statistics_en.pdfhttp://ec.europa.eu/anti_fraud/reports/commission/2007/ statistics_en.pdf

4 4 PURPOSE OF THE REPORT Art. 280§5 TEC The Commission, in cooperation with Member States, shall each year submit to the European Parliament and to the Council a report on the measures taken for the implementation of this Article. –Measures adopted by the MS –Measures adopted by the EC –Results (Reported Irregularities) Information to the public – European taxpayers

5 5 STRUCTURE OF THE ANNEX Introduction – Key facts Two parts: –Revenues (1 chapter: TOR); –Expenditure (5 chapters: Agriculture, EFF, SF, PA, DE) SF chapter is divided in five parts: –Reporting discipline –General trends (amounts involved, impact on budget, method of detection, type of irregularity, suspected frauds) –Specific analysis (Programming period ) –Recovery –Conclusions Annexes

6 6 DEFINITIONS Irregularity rate = Fraud rate = irregular amounts payments (from the EC to MS) irregular amounts (linked to suspicions of fraud) payments (from the EC to MS)

7 7 REPORTING DISCIPLINE CONCLUSIONS 1 Increased number of communications reported through the AFIS-ECR modules 1681 and 1831 (78%) 2 Increased attention by MS on the set deadlines (86%) 3 Delay between detection and reporting is satisfactory in 90% of the cases 4 Personal data are communicated in 97% of communications 5 Qualification of the irregularities better than previous years (78%) 6 Further harmonisation of national approaches still needed, but improvements

8 8 GENERAL TRENDS CONCLUSIONS 1 Higher number of irregularities (EU15 -1%; EU10 +52%), but lower amounts (EU15 -35%; EU %) 2 Lower impact on budget (1.25%) 3 ERDF remains the fund with highest n° of irregularities and irregular amounts. 4 IT, ES, UK, PT reported the highest number of irregularities. ES, IT, UK and PL reported the highest amounts 5 Control of documents is the most reported method of detection. Control by police is the most productive (> 540,000 per irreg.) 6 Not eligible expenditure is the most frequently reported type of irregularity. The ranking is in line with the previous years 7 Suspected frauds represent around 8% of total reported irregularities and 10% of the reported irregular amounts

9 9 IRREGULARITIES REPORTED BY MS N° of irregularities: +7% Irregular financial amounts: -27%

10 10 IRREGULARITIES BY FUND YEAR 2008 TREND BY FUND

11 11 IRREGULARITIES BY MS:

12 12 DETECTION METHODS AND TYPES OF IRREGULARITY

13 13 CASES OF SUSPECTED FRAUD

14 14 SPECIFIC ANALYSIS: PP CONCLUSIONS 1 Trend still increasing for reported irregularities; decrease for amounts 2 Irregularity rate still increasing 3 The greatest majority of irregularities related to the programming period are referred to Objective 1 regions but are in balance with payments 4 Objective 2 programmes present the highest irregularity rate; Objective 1 programmes have the highest fraud rate 5ERDF has the highest irregularity and fraud rates 6 HU, CZ, LV, LT, PL among the EU10 and SE, NL and IT among the EU15 have highest detection before payment

15 15 Trend still increasing for reported irregularities; decrease for amounts Irregularity rate still increasing

16 16 SITUATION BY FUND ERDF has the highest number of irregularities, irregular amounts and irregularity rate ESF is second

17 17 SITUATION BY OBJECTIVE Objective 2 programmes have the highest irregularity rate though the by number of irregularities it is only third after Obj. 1 and 3 Objective 1 irregular amounts share is in balance with payments share (72% each)

18 18 ESTIMATED IMPACT OF SUSPECTED FRAUDS BY FUND AND OBJECTIVE Objective 1 and ERDF financed programmes have the highest fraud rates (0.22% and 0.21% respectively) Fraud rate is calculated on the payments not on the audited expenditure

19 19 RECOVERY RATE CONCLUSIONS 1SF : around 25% of amounts paid are recovered : around 45-53% recovered; : 39% 3CF recovery: 58% of amounts paid : %; 2006: 77%; 2007: 64%

20 20 COHESION FUND 1 ES, PT and LT reported the highest number of irregularities and amounts 2Data still unreliable in terms of quality and quantity

21 21 CONCLUSIONS (1) Improved compliance of MS thanks to the efforts from OLAF and the EC also in view of the introduction of the new reporting system IMS ERDF is the fund with the highest number of irregularities and irregular amounts (increasing trend) Cases of suspected fraud present a decreasing trend

22 22 CONCLUSIONS (2) Programming period : –Irregularity rate (IR) increasing –Objective 2 programmes have highest IR –Objective 1 programmes have highest fraud rate (FR) –ERDF has highest IR and FR 25% of the amounts are recovered within few months following detection Further 25% in the following 5-6 years Irregularities linked to the Cohesion Fund are still of very low quality to allow any real conclusion to be drawn


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