Velkommen til seminar om forsikringssvindel COPYRIGHT 2009, SAS INSTITUTE INC. ALL RIGHTS RESERVED.

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2 Seminar om forsikringssvindel 8. oktober 2009 Jim Nielsen Direktør, Business Advisory & Innovation COPYRIGHT 2009, SAS INSTITUTE INC. ALL RIGHTS RESERVED. Allan Cervin Divisionsdirektør, Insurance & Pension

5 Udsnit af udfordringer i danske virksomheder som SAS løser VISION Hvordan sikres systematisk gennemgang af alle skadesanmeldelser med henblik på identificering af svindel for at prioritere efterforskningen og minimere skadesudbetaling til svindel? Hvordan automatiseres skadesbehandlingen med fast tracking og udvælgelse til efterforskning på tværs af alle kontaktkanaler (web, telefon, papir)? Hvordan minimeres selskabets risikoprofil med henblik på at frigøre kapital? COPYRIGHT 2009, SAS INSTITUTE INC. ALL RIGHTS RESERVED.

6 SAS Institute KUNDER GLOBALT HERAF 725 I DANMARK REPRÆSENTERET I 113 LANDE GLOBAL HQ I NORTH CAROLINA KONTORER I KØBENHAVN OG SKANDERBORG THE POWER TO KNOW 91 af top 100 virksomhederne på 2008 FORTUNE Global listen Det offentlige, financial services, farma, manufacturing, communications, retail medarbejdere heraf +300 i Danmark 22% af omsætningen går direkte til forskning og udvikling MARKEDSLEDER Grundlagt i 1976 etableret i Danmark siden 1984 Verdens største privatejede softwareleverandør USD 2,26 mia. globalt og 514 mio. DKK i Danmark Global vækst alle år Markedsleder i Danmark COPYRIGHT 2009, SAS INSTITUTE INC. ALL RIGHTS RESERVED.

8 Velkomst, v. Jim Nielsen, SAS Institute Fakta og trends om det danske forsikringsmarked og værktøjer i kampen mod forsikringssvindel, v. Arne Knippel, Forsikring & Pension Pause Combating insurance fraud, v. David Hartley, SAS EMEA Topdanmark: Sådan arbejder vi med bekæmpelse af forsikringssvindel, v. Ole Peter Rasmussen, Topdanmark Opsamling og konklusioner, v. Esben Ejsing, SAS Institute AGENDA COPYRIGHT 2009, 2008, SAS INSTITUTE INC. ALL RIGHTS RESERVED.

9 Forsikring & Pension SAS Institute 8. oktober 2009 Arne Knippel Kontorchef

10 Udvikling i forsikringssvindel: 1994: "Underlige sager" 1998: "Misbrugssager" 2004: "Forsikringssvindel"

11 1995: ca. 15 "misbrugskonsulenter" "skadeinspektører" (50++) "Vi har kun ærlige kunder" 2009: 42 konsulenter/skadeinspektører (40-)

12 Pågående drøftelse om forsikringssvindel: DK: Manglende data (10% - hmmm?) NL: Svindel 150 EUR/familie F: Svindel 40% motor D: Svindel 4 mia EUR/årligt; 28%: ikke krim. S: Svindel 10% (1982) UK: Svindel mest indbo, motor dyrest UK: Svindel - 44/årligt/forsikrede

13 Intet skadesregister Bekæmpelse kontra branchens image Efterforskningsmetoder til diskussion Stemningen ved at vende?

14 Stigende antal sager: 4-WD, leasing, personskadesager, indbrudssager, lystfartøjer Udlandssager ("bananstater") Bander (Baltikum m.m.)

15 Tekniske forholdsregler: Div. "F-D-S" m.m. Emnet diskuteres (seminarer, politiskolen m.m.) Befolkningens holdning? Data og officiel F&P strategi på vej

16 Efterlysn. PV, MC m.m.

17 Østkontoret - Danmark Forsikringsselskaberne SOS Politiet Bilimportører Østkontoret Interpol Udenlandske kontakter

18 Verband Dänischer Automobilversicherungsgesellschaften Stowarzyszenie Dunskich Firm Ubezpieczeniowych Danish Insurance Association AMALIEGADE 10, DK-1256 KOEBENHAVN K GESUCHT WIRD **** POSZUKUJEMY **** SEARCH Nr.: Fabrikat/-typ: Fabrikat/-typ: Fabrikat/-typ: Baujahr: Rok budowy: Model(year): Fahrgestellnummer: Numer podwozia: Chassis No.: Motornummer: Numer silnika: Engine number Letztes Kennzeichen: Ostatni numer rejestracyjny: Registration No.: Farbe/Kolor/Colour: VW CARAVELLE - 2,5 TDI 2008 WV2 ZZZ 70Z XH ACV YV34535 RED Besondere Merkmale: Uwagi: Special characteristics: Verschwunden am: Zostal skradziony w dniu: Disappeared on(date): Verschwunden (wo): Zostal skradziony w (gdzie): Disappeared from(place): Gemeldet am/an der Polizei in: Zgloszono do policji w /w dniu:: Reported on the /to the plice in: DK - KØBENHAVN KØBENHAVN Bitte Informationen an: Informacje do: Information please to: FAX: oder/lub/or FAX:

19 Efterlysn. PV, MC m.m. LAGOS

20 LAGOS (LAst, GOds- og Sættevogne) ITD, RP Comcenter, Forsikring & Pension (Østk.)

21 Efterlysn. PV, MC m.m. LAGOS Entreprenørmateriel

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25 Efterlysn. PV, MC m.m. LAGOS Entreprenørmateriel Lystfartøjer m.m.

26 HVAD REGISTRERES? ALT REGISTRÉRBART SEJLENDE MATERIEL (ALT MED ET SKROGNUMMER) ,00 HANDELSVÆRDI ALLE BÅDMOTORER

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28 Efterlysn. PV, MC m.m. LAGOS Entreprenørmateriel Lystfartøjer m.m. Repatriering

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30 Efterlysn. PV, MC m.m. LAGOS Entreprenørmateriel Lystfartøjer m.m. Repatriering Rastepladser

31 Overfald på chauffører

32 "Sikre rastepladser" 2009

33 Efterlysn. PV, MC m.m. LAGOS Entreprenørmateriel Lystfartøjer m.m. Repatriering Rastepladser Svindeludvalg

34 Efterlysn. PV, MC m.m. LAGOS Entreprenørmateriel Lystfartøjer m.m. Repatriering Rastepladser Svindeludvalg SU politi forsikr.

35 Efterlysn. PV, MC m.m. LAGOS Entreprenørmateriel Lystfartøjer m.m. Repatriering Rastepladser Svindeludvalg SU politi forsikr. Foredrag undervis. m.m.

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37 Tyverimetoder biler årgang 1998 eller nyere, 93 indrapporterede Borttransporteret 2 Diverse 16 Nøgleindbrud i hjemmet 31 Nøgleindbrud på værksted 15 Andre nøgletyverier 24 Overfald (personskade) 1 Showroomjacking 0 Startspærre omg. 1 Tricktyveri 1 Falsk ID 1 Car jacking 1 I alt 93

38 Efterlysn. PV, MC m.m. LAGOS Entreprenørmateriel Lystfartøjer m.m. Repatriering SU politi forsikr. Foredrag undervis. m.m. Rastepladser Svindeludvalg Bilfinderkorps

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40 DK netværk Efterlysn. PV, MC m.m. LAGOS Entreprenørmateriel Lystfartøjer m.m. Repatriering SU politi forsikr. Foredrag undervis. m.m. Rastepladser Svindeludvalg Bilfinderkorps

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42 IAATI DK netværk Efterlysn. PV, MC m.m. LAGOS Entreprenørmateriel Lystfartøjer m.m. Repatriering SU politi forsikr. Foredrag undervis. m.m. Rastepladser Svindeludvalg Bilfinderkorps

43 "EN HURTIG OMKLÆDNING" SETRABUS STJÅLET FRA PARKERINGSPLADS I NORDITALIEN 21-22/7/09 29/7/09 FINDES BUSSEN I VENEDIG MED NY IDENTITET.

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45 IAATI IASIU DK netværk Efterlysn. PV, MC m.m. LAGOS Entreprenørmateriel Lystfartøjer m.m. Repatriering SU politi forsikr. Foredrag undervis. m.m. Rastepladser Svindeludvalg Bilfinderkorps

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47 SAFIRER OG DIAMANTER ct = Gr = Krav på 1.9 Millioner kroner

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50 INGEN BUTIK PÅ ADRESSEN NY/GL. MOMSNUMMER IKKE EKSISTERENDE ÉT TELEFONNUMMER I BRUG EJ BUTIK TID TRE DAGE PRIS USD 500 SPARET ,00

51 IAATI IASIU IAMI DK netværk Efterlysn. PV, MC m.m. LAGOS Entreprenørmateriel Lystfartøjer m.m. Repatriering SU politi forsikr. Foredrag undervis. m.m. Rastepladser Svindeludvalg Bilfinderkorps

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53 IAATI IASIU IAMI TAPA DK netværk Efterlysn. PV, MC m.m. LAGOS Entreprenørmateriel Lystfartøjer m.m. Repatriering SU politi forsikr. Foredrag undervis. m.m. Rastepladser Svindeludvalg Bilfinderkorps

54 Profile Transported Asset Protection Association - Europe, Middle East and Africa (TAPA-EMEA), is an association of security professionals and related business partners from various manufacturing and transportation companies (200+) Members Air Cargo security, British American Tobacco, Compass security, DFDS Transport Ltd., DHL, DSV A/S, Freight Watch, Fujitsu-Siemens, G4S, Gucci Group, Hewlett Packard Co., KLM Cargo, Lufthansa Cargo, Maersk Logistics, Motorola, Metropolitan Police, Nokia, Omega, Police Frankfurt/Main, Phillips Electronics, Samsung, Sony-Ericsson, Special Cargo Handling, TNT, Toshiba m. m. fl.

55 Incident Information Service (IIS) Freight Security Requirements (FSR)

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57 IAATI IASIU IAMI TAPA Intern. netværk DK netværk Efterlysn. PV, MC m.m. LAGOS Entreprenørmateriel Lystfartøjer m.m. Repatriering SU politi forsikr. Foredrag undervis. m.m. Rastepladser Svindeludvalg Bilfinderkorps

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59 Landcruiser jagt i Polen

60 Intern. IAATI IASIU IAMI TAPA CEA: ICRV netværk DK netværk Efterlysn. PV, MC m.m. LAGOS Entreprenørmateriel Lystfartøjer m.m. Verifikation SU politi forsikr. Foredrag undervis. m.m. Rastepladser Svindeludvalg Bilfinderkorps

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62 Intern. IAATI IASIU IAMI TAPA CEA: ICRV netværk DK netværk Efterlysn. PV, MC m.m. LAGOS Entreprenørmateriel Lystfartøjer m.m. DELTA Verifikation Repatriering SU politi forsikr. Foredrag undervis. m.m. Rastepladser Svindeludvalg Bilfinderkorps

63 "..de stjal både lastvogn og heste.."

64 Skete disse uheld virkeligt?

65 Døde hun i Odessa!?

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67 Spørgsmål? Tak for opmærksomheden!

71 Agenda Insurance Fraud growing problem SAS experience Tackling fraud - analytical techniques Tacking fraud - proposed architecture Why and How SAS can help you Copyright 2009, SAS Institute Inc. All rights reserved.

72 Global Non-Life Insurance Claims Fraud US National Insurance Crime Bureau estimates that 10%+ of all property and casualty claims are fraudulent US Insurance Information Institute $30 billion losses in Non-Life Insurance Fraud annually Insurance Council of Australia estimates that Costs Australians $1.6 billion per year. 10 and 15% of insurance claims across of lines exhibit elements of fraud. Swedish Association claim 5 to 10% of claims include fraud South African Insurance Institute estimates that Adds about 15% to short term premiums Costs ZAR 3 billion per annum Association of British Insurers estimates that Costs insurers circa 1.9bn per annum In 2009 estimated adds about 44 to average insurance premium 0.73bn fraud detected in 2008 (30% ) Swiss Insurance Association estimate that 10% of claims paid are fraudulent German Insurance Association estimates that That fraud costs circa 4bn per annum Copyright 2009, SAS Institute Inc. All rights reserved.

74 Cost effective insurance fraud detection Estimated that fraud adds an additional 10% to final premiums paid worldwide Main argument against is cost effectiveness of insurance detection Can be an expensive process proving fraud Need to warn off potential fraudsters Early mover advantage Copyright 2009, SAS Institute Inc. All rights reserved.

76 Does analytical fraud detection scoring work? Insurer 1 Estimated 7m+ of savings 70% cases identified now fraudulent Insurer 2 Major national fraud ring broken multi m of savings Insurer 3 Estimated annual savings 14m per annum on motor book using data and text mining Significant increase in hit rate. Insurer 4 1m+ of savings in first phase Hit rate improved from 27% to 60% Insurer 5 Estimated 20m+ of savings Increase fraud detection Copyright 2009, SAS Institute Inc. All rights reserved.

77 Business issue Solution Benefits Unknown level of fraud Only simple rules in place Integral part of a BI program SAS Data Warehouse, Data Mining and Performance Management 5% of claims proven to be fraudulent and stopped reduced claims paid Predict for example car stereo model most likely to be stolen and negotiate better rate with suppliers Overall 30 to 35% increase in profitability of auto book "With our SAS data warehouse, we are able to segment customer data by having discovered certain relationships between data sets which are red flags for fraud-related losses. AXA OYAK discovered that 5 percent of its claims payouts were fraudulent, and these can now be corrected and prevented in the future," Ali Eilat Assistant General Manager of Underwriting, AXA Oyak Copyright 2009, SAS Institute Inc. All rights reserved.

78 Business issue Solution Benefits Designed and implemented a fraud solution for auto insurance (define strategy, change management, organization, solution design and the SAS technical solution). SAS Data Mining solution Designed and created a Fraud Datamart from different data sources Defined and implemented fraud Models using SAS datamining techniques. Implemented a fraud solution for auto insurance (define strategy, change management, organization, solution design and the SAS technical solution). Increased detection rates (+ 200M per year) Copyright 2009, SAS Institute Inc. All rights reserved.

79 Business issue Solution Benefits Estimated fraud between $30bn and $50bn across industry PA. Increase detection rates with same number of investigators. Expressed as a core competence by senior management. SAS Data Mining Increased detection rates to see annualized savings of $11.5m. Investigators handle 30% more cases Later equates to increased productivity gains of $200,000 per quarter "With SAS, we're able to work better faster. That, in turn, improves our ability to detect fraud. And, with SAS, we can model what normal claims look like so that we can then spot the deviations. Ultimately, we will be able to prevent questionable claims from ever being considered for payment." Chris Scheib, Manager Data Mining and Pattern Discovery, Highway Copyright 2009, SAS Institute Inc. All rights reserved.

81 Fraud Actual Fraud Non-Fraud Actual Non-Fraud Fraud Actual Non-Fraud Analytics -The Confidence Challenge Non-Fraud Model Predicts Fraud Model Predicts Non-Fraud Fraud Correct Dismissal False Detection Correct Dismissal False Detection False Dismissal Correct Detection Model Predicts Non-Fraud Correct Dismissal Fraud False Detection False Dismissal Correct Detection Copyright 2009, SAS Institute Inc. All rights reserved. Reputation impact False Dismissal Correct Detection

82 Analytical techniques Insurance Rules Database Searching Anomaly Detection Complex Patterns Social Networking Analysis/ Associate Link Patterns Text Mining Copyright 2009, SAS Institute Inc. All rights reserved.

83 Technique 1 Insurance Rules Test each transaction against a series of predetermined algorithms or rules. Aggregate scores and compare to threshold values Low pass High fail Medium pass but monitor Rule 1 Rule 2 Rule 3 Rule 4 Rule 5 Rule 6 Rule 7 Rule 8 Rule 9 Rule 10. Rule 11. Rule 12. Copyright 2009, SAS Institute Inc. All rights reserved.

84 Technique 1 Insurance Rules Sample Rules X claims in last X years X similar claims paid in xx months Claim amount over $x,000 Time to renewal less than XX working days Policy within XX days of inception/endorsement Changes in sum insured/coverage in last x months Consults claims chaser (loss assessor) before doctor Whiplash injury < 72 hours after accident (soft tissue damage takes this long, typically, to be felt) Car "torched" and insured has fire cover.. Unusual time of day e.g. 11pm - 6am Time delay in reporting claim Copyright 2009, SAS Institute Inc. All rights reserved.

85 Technique 1 Insurance Rules Sample Rules Readily admits liability. Insured overly aggressive Absence of forcible/violent entry Vehicle struck by rental car Registration document in another name No independent witnesses none or family and friends only No police report Inconsistency with police report Property damage/personal injury inconsistent No proof of ownership Multiple versions of accident Non disclosure of previous claims or convictions Valuations, receipts etc excessive Valuations, receipts do not exist Copyright 2009, SAS Institute Inc. All rights reserved. Subsequent to initial notification

86 Technique 2 Database Searching In-house and across industry (co-operatives) Positive Identification Match against data already held on file Known customer On Hot-list WANTED Negative verification No match Cannot be verified REWARD Data protection issue? Copyright 2009, SAS Institute Inc. All rights reserved.

88 Technique 3 Anomaly Detection Profiling Model behaviours of groups or individuals Build models of expected behaviour from history Individual Peer-groups Notification of deviation from predicted norms Issues Strong understanding of the data Need to be trained Short life cycle Copyright 2009, SAS Institute Inc. All rights reserved.

89 Technique 3 Anomaly Detection Clustering Goal: Identify abnormal groups of claims - Globally abnormal - Abnormal in relation to selected base segmentation, profiling - Mulitvaraiate outliers values only abnormal in relation to each other Copyright 2009, SAS Institute Inc. All rights reserved.

90 Technique 4 Advanced Analytics Generalize fraud patterns for automatic detection - Identify typical patterns - Score new claims automatically for fraud propensity Modeling techniques: - Predictive Modeling: Decision Trees Neural Networks Regression Base: confirmed historical cases (fraud flag) Result: Fraud risk score Model Management Need to manage many complex models Copyright 2009, SAS Institute Inc. All rights reserved.

91 Technique 5 Social Network Analysis Associate Link Patterns or Data linking analysis Detect unexplained Relationships - Profiling of suspicious cases - Direct and indirect links between seemingly unrelated transactions Modeling Techniques - Associations / Sequence analysis - Link analysis / Path analysis - Fuzzy matching Copyright 2009, SAS Institute Inc. All rights reserved.

92 Technique 6 - Text Mining Upto 80% of claims data can be unstructured Look for patterns that might suggest fraud examples:- Multiple claimants using exact same words Specific scripted comments on a particular type of claim Certain words suggest possible lie where claimant disances themselves from claim such as using fell rather than drop or the road was wet, it had been raining, rather than I was driving too fast along the wet road. Look for new patterns of words over time Copyright 2009, SAS Institute Inc. All rights reserved.

95 SAS Fraud Framework Process Flow Operational Data Sources Exploratory Data Analysis & Transformation Fraud Data Staging Business Rules Alert Generation Process Alert Administration Social Network Analysis Claimant Analytics Anomaly Detection Network Rules Network Analytics Claims Predictive Modeling Policies Intelligent Fraud Repository Learn and Improve Cycle Alert Management & Reporting Case Management Copyright 2009, SAS Institute Inc. All rights reserved.

96 Data Mart and Exploration Area Fraud Data Staging Physical customer centric repository of data for fraud detection Moral hazard Physical hazard Claims Accounts Distribution channel Third Party data etc. Storage of history Scoring area Staging area Copyright 2009, SAS Institute Inc. All rights reserved.

98 Business Rules Engine Analytic Validation and Business Rule Refinement Set up business rules quickly and easily Users can amend Owned by fraud experts to provide rapid response Includes 149 pre defined fraud rules Copyright 2009, SAS Institute Inc. All rights reserved.

99 Analytic Engine Analytic Approach: Unknown Patterns Use when no target exists Examine current behavior to identify outliers and abnormal transactions that are somewhat different from ordinary transactions Include univariate and multivariate outlier detection techniques, such as peer group comparison, clustering, trend analysis, and so on Includes 25 pre set anomaly detection techniques Copyright 2009, SAS Institute Inc. All rights reserved.

100 Analytic Engine Analytic Approach: Complex Patterns Use when a known target (fraud) is available Use historical behavioral information of known fraud to identify suspicious behaviors similar to previous fraud patterns Include parametric and nonparametric predictive models, such as generalized linear model, tree, neural networks, and so on SAS IP to help set up first predictive models Copyright 2009, SAS Institute Inc. All rights reserved.

101 Analytic Engine Social Network Analysis Network scoring Rule and analytic-based Analytic measures of association help users know where to look in network Net-CHAID Density, beta index (network) Risk ranking based on hypergeometric distribution, degree, closeness, betweeness (node) Modularity (sub network) Pre set social networking analysis Copyright 2009, SAS Institute Inc. All rights reserved.

102 Alert Generation Process Copyright 2009, SAS Institute Inc. All rights reserved. For individual claim Runs Business Rules Runs SNA compares to expected outcome Runs Analytics Produces score uses web service API to push score to claims management solution Works in real time, near real time or batch Real time first notification Real time or batch subsequent info becomes available

109 Alert Management and Reporting Directs and deduplicates Suppresses alerts (where suppression event) Displays alerts Provides automatic update of reports through portal Copyright 2009, SAS Institute Inc. All rights reserved.

113 SAS Institute Seminar om forsikringssvindel 8. oktober 2009

114 En hverdagsrisiko antal skader pr. dag i bilskader 182 indbrud 133 arbejdsskader 158 brande Øvrige 1.760

115 Normer mht. forsikringsmisbrug før og nu 120. De fleste mennesker forsøger at få en større erstatning end værdien af skaden Helt enig Delvis enig 52% Delvis uenig Helt uenig Ved Ikke % 247. Jeg tror, de fleste mennesker er enig om, at man godt kan snyde forsikringsselskaber lidt uden 40% derved at begå noget kriminelt % Kilde: Jørgen Goul Andersen Borgerne og lovene, Aarhus Universitetsforlag side 357

116 Undersøgelse i Søndags Avisen

117 Kendetegn Der kan opdeles i 3 hovedkategorier Forbrydere, der bevidst og i større målestok svindler De pæne borgere, der fifler med anmeldelser De indbildte syge, de der selv tror på deres lidelse

118 Hvorfor gøre noget Internationale analyser har vist at ca. 10% af skadeudbetalinger er svindel En analyse i Forsikring og Pensions regi har vist, at ca 20% af skaderne på særlige områder som bilbrande og totalforsvundne biler er relateret til svindel Befolkningens holdning til svindel jf. undersøgelser De unge i dag svindler mere end for 10 år siden. Der må derfor henover tid forventes stadig stigende omkostninger til svinde. Skaderegister (Datatilsynet sagde ultimo 2006 nej) Afklaring af svindel er en lavt prioriteret opgave hos Politiet

119 Før og nu Indtil år 2000 År 2000 Kun freelance skadeinspektører Få sager til undersøgelse Lav hitrate Etablering og løbende udvikling af særlig afdeling kun med fokus på svindelsager 5 administrative medarbejdere 8 skadeinspektører

120 Overordnet strategi Sikre en effektiv bekæmpelse af svindel, for derved at opnå at svindlens effekt hos Topdanmark er mindre end det generelle niveau på markedet Forebygge og bekæmpe al slags svindel hos kunder m.fl. Anvende erfaring og nyeste teknologi som midler til bekæmpelse af svindel Overholde gældende lovgivning Arbejdet foregår indenfor de etiske regler publiceret af Forsikring og Pension

121 Hvad gør vi Kunder Processer Medarbejdere Interne samarbejdspartnere Eksterne samarbejdspartnere IT Forebygge (alle) Grovscreene (skadebehandler) Screene Behandle (Svindelkonsulent) Evaluere (Svindelafdeling) Feedback-loop (Svindelafdeling/analytikere)

122 Arrangerede uheld PC-Crash er et avanceret PC program til brug for rekonstruktion og simulering af kollisioner mellem alle former for trafikanter. Kollisioner med op til 32 køretøjer kan simuleres todimensionelt og i 3D. Endvidere kan kollisioner med fodgængere samt andre typer af bløde trafikanter simuleres med angivelse af påvirkninger på den enkelte kropsdel. PC-Crash er udviklet af firmaet Dr. Steffan Datentechnik i Linz i Østrig. Programmet benyttes i de fleste europæiske lande samt i Nordamerika, Canada og Japan.

123 Nye muligheder på godt og ondt

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125 Medierne Positivt historier, information mv. Negativt den forurettede mod forsikringsselskabet Action Via medier skabe bevidsthed om at det er socialt uacceptabelt at svindle

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129 Kunden er omdrejningspunktet SAS Institutes viden, kompetencer, erfaringer og best practice SUPPORT FORRETNING UDDANNNELSE KUNDEN PROJEKT/ PROCES TEKNOLOGI COPYRIGHT 2009, SAS INSTITUTE INC. ALL RIGHTS RESERVED.

130 SAS for Insurance Risk Asset/Liability Management Ratemaking & Loss Reserving Operational, Market & Credit Risk Anti-Money Laundering Claims Fraud Detection Finance Scorecards & Dashboards Activity Based Management Budgeting & Planning Consolidation & Reporting Regulatory Compliance Customers Customer Lifetime Value Customer Interaction Management Acquisition, Cross-Sell & Retention Segmentation & Profiling Campaign Management & Optimization Operations Claims Analytics Channel Management Product Profitability Working Planning & Management IT Performance Management COPYRIGHT 2009, SAS INSTITUTE INC. ALL RIGHTS RESERVED.

131 Hvordan kan SAS Institute hjælpe? Stor erfaring i implementering af komplekse modeller Konsulenter Projektchefer Projektmedarbejdere Procesfacilitator Sparringspartner Revisionsspor Åben arkitektur State-of-the-art statistik COPYRIGHT 2009, SAS INSTITUTE INC. ALL RIGHTS RESERVED.

132 Handlingsplan SAS Fraud Framework Dato Aktivitet Deltagere firma Deltagere SAS Institute Status Kommentarer 12. oktober 2009 Præsentation af SAS Fraud Frameworkkonceptet 2. november 2009 Fastlæggelse af evalueringsproces 16. Nobember Workshop: Analyse af as is/to be inden for fraud analysis 19. november It- og dataworkshop 9. december Arbejdsmøde: Afdækning af integration af SAS fraud framework i kundens processer Medio december Medio december Ultimo december Januar Gennemgang af business case og to be - proces for SAS Fraud Framework Fastlæggelse af scope og faser for implementering af SAS Fraud Framework Fremlæggelse af tilbud for løsning og scopet leverance Aftale indgåes 1. april 2010 Kickoff for fase 1 Ultimo juli 2010 Go-live fase 1 COPYRIGHT 2009, SAS INSTITUTE INC. ALL RIGHTS RESERVED.