working collaboratively to share and analyse data to prevent, detect and deter fraud

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Page 1: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud
Page 2: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud

Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter

Fraud

Nick Jennings,Counter Fraud Manager,

Hertfordshire County Council

Page 3: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud

Shared Services and

Data Sharing?Nick Jennings

Hertfordshire Shared Anti-Fraud Service

Page 4: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud

The SAFS partnership:

Page 5: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud

Our aims:Ensure ongoing effectiveness and resilience of anti-fraud arrangements post SFIS Deliver financial benefits in terms of cost savings or increased revenue

Create a ‘Data-Hub’ for Hertfordshire

Create a recognised centre of excellence that is able to disseminate alerts and share best practice nationally

Page 6: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud

Collect data monthly Secure/ with limited user access High level data from a variety of

services Create a suite of scheduled data-

matches Identify fraud quickly Provide a dash-board of Intelligence Allow targeted ‘Campaigns’- NFI & SPD

Data-Hub for Hertfordshire:

Page 7: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud

A DATA-HubHow dangerous can it be?

Page 8: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud

How often Have I heard?• “Breaking down barriers”• “Improved Data-Sharing”• “Cross Departmental working”• “Open up gateways”• “Free flow of data”• “Change the inaccurate interpretation of

the law”• “Joint working to prevent duplication of

effort”

Page 9: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud

What’s the reality? • ICO Guidance- Has it been ignored? • Legal Advisors- “Six of the Best”• Barristers & ‘Experts’• NHS –only communicate by FAX! (Until Oct

2016)• SFIS & Drawbridges• DWP- NDU- “Fax us- but only for treason LOL”• UK- DVLA v US- DVLA

Page 10: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud
Page 11: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud

SAFS Provides:• Board Representation• Resilient reactive investigation service• Hotline/Webpage for reporting.• Shared Strategy/Policies/Procedures• Shared Anti-Fraud Response Plan• Shared Best Practice• Fraud Alerts• Fraud Awareness Events• NFI and Local Data-Hub

Page 12: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud
Page 13: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud

LA CFOs Tool Kit:• Council Data and Expertise• S.29/35 DPA• Authorised Powers (POSHFA/CTRS/SSFA) • NAFN/ LAIOG / CIPFA / TEICCAF• Home Office • UKBA/ NHS/ DVLA• Land Registry/ Companies House• Local Police (Intel/POCA/ERSU/VODS/Arrests)• 3rd Party Specialists

Page 14: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud

New Skills:• HR & Discipline Training • ‘Corporate Fraud’ Training• Business Rates and Avoidance• Tenancy Fraud• Cyber Crime/ Payment Fraud• Presentation Skills• Professional Accreditations• More focused approach• Measurable outputs

Page 15: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud

Nine months of SAFS:

• Blue Badge- 63 • Business Rates- 6 • Council Tax- 336 • Payment- 9 • Schools- 9 • Staff- 21 • Contract/ Procurement- 15• Tenancy Fraud- 79

Page 16: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud

But still NO Data-Hub

We do have…• a draft ‘Information Sharing Protocol’-

signed off!• the Data-Warehouse• local ‘Data Access Agreements’• Anti-Fraud & Corruption Strategies• Fraud Prosecution Policies• Anti-Fraud Action Plans.

Page 17: Working Collaboratively to Share and Analyse Data to Prevent, Detect and Deter Fraud