2020 ANALYTICS ANALYTICS IS COMING OF AGE: TURNING AN ACADEMIC TOPIC INTO BUSINESS VALUE

Transcription

1 2020 ANALYTICS ANALYTICS IS COMING OF AGE: TURNING AN ACADEMIC TOPIC INTO BUSINESS VALUE IMAM HOQUE

2 2020 ANALYTICS TOPICS Introduction to the latest techniques o A hybrid approach o Modelling real real- life networks of relationships within the data o Dealing with unstructured text Contemporary approaches o Architectures o Big data o Real time o Tools and techniques Practical examples that deliver significant value: o Insurance fraud o Government compliance o Banking financial crime

3 2020 ANALYTICS ANALYTICS IS COMING OF AGE Yesterday: Today and Tomorrow: VAT fraud costing Europe 50bn a year Carousel scam escalates out of control as governments are accused of denial BNP Paribas to pay $9bn to settle sanctions violations Companies spend about $1 trillion on marketing globally each year. Our analysis and experience has revealed that companies can squeeze 15 to 20 percent better returns on that spend. That s as much as $200 billion Source: Rishi Bhandari, a senior expert at McKinsey

4 2020 ANALYTICS ANALYTICS HAS TO BE OPERATIONAL Front End Customer interaction Business Back-end Processes Events: / Web-visit Application Transaction Decisions & scoring Triage Deep investigation MI, Explore & Analytics Re-score: Entire customer base Regular cycles New data Core system or 3 rd party data feeds Data ingest, link and enhance Advanced Rules analytics SAS Hybrid approach Anomaly Social network detection analytics

5 2020 ANALYTICS MULTIPLE APPLICATIONS Compliance Targeting and Intelligence Detecting & Preventing Fraud Asset based Optimisation Customer Intelligence / Marketing Forecasting Risk

6 2020 ANALYTICS OUTCOMES ARE ONLY AS GOOD AS THE SMARTS SNA (example): A number of people on a network who have gone into default who have the same employer Predictive modelling (example): A logistic regression model built to identify fraudulent populations using a combination of customer metrics Anomaly detection (example): Applicant income is high in relation to Age Database Searches (example): Looking for matches across the blacklists Text mining (example): Transaction narrative showing low risk employers (the Police) Business rule (example): An applicant applying on 3 consecutive days

7 2020 ANALYTICS HYBRID MODELS ARE MORE TRANSPARENT Risk of income under declaration 65% Risk of income under declaration Income inconsistent with property post code Income not progressing in line with inflation Land registry indicates 4 properties No rental income declared Accountant links this person to others with undeclared rental income

8 2020 ANALYTICS SO WHY BLEND THESE TECHNIQUES? CONSIDER THIS How to measure Tax non-compliance What you know you know Existing debt Optimise debt collection and customer contact What you know you don t know Projections from known case typologies Encode known MO s as models and execute across data, sample alerts and project What you don t know you don t know Few or no examples, projections difficult Outlier analysis. Data exploration. Learn from other Tax authorities. Learn from other sectors. Encode, sample and project

9 LETS LOOK AT NETWORK DATA IN MORE DETAIL 3 rd party Insured vehicle 1 and vehicle 3 collide V3 V1 Insured driver claims for whiplash Injured passenger claims for whiplash Claim 1 st Feb Claim total 14000

10 1 WEEK BEFORE Insured vehicle 2 and vehicle 4 collide 3 rd party Insured driver claims for whiplash Injured passenger claims for whiplash V2 V4 Claim total Claim 25 th Jan

11 3 rd party 3 rd party V3 V4 V1 V2 Claim 1 st Feb Claim 25 th Jan

12 3 rd party P rd party V3 V4 V1 V2 Claim 1 st Feb Claim 25 th Jan P1 2013

13 3 rd party P rd party V3 V4 V1 V2 Claim 1 st Feb Claim 25 th Jan P Both claims on the network have high injury / damage ratio

14 3 rd party 3 rd party and V2 claimant have same family name coincidence? P rd party V3 V4 V1 V2 Claim 1 st Feb Claim 25 th Jan P1 2013

15 3 rd party P rd party V3 V4 V1 V2 Claim 1 st Feb P1 and P2 were paid for by the same credit card strengthens the connection? P Claim 25 th Jan

16 3 rd party P rd party V3 V4 V1 V2 Claim 1 st Feb Claim 25 th Jan P V2 and V4 were both repaired at the same small bodyshop coincidence?

17 3 rd party 3 rd parties have the same mobile phone number, except for the final digit P rd party V3 V4 V1 V2 Claim 1 st Feb Claim 25 th Jan P1 2013

18 LOOK BEYOND THE SINGLE CLAIM Investigators cannot typically achieve this view By analysing the single claim in silo, the potential fraud risk is not immediately clear.

19 2020 ANALYTICS ARCHITECTURE FOR OPERATIONALISING ANALYTICS Realtime Data Transactions / Events Realtime scoring ESP DQ Applications Batch Data Citizen / business Data Realtime Model Build VSD Analytics EM / EG / VS / Text Miner Scoring and Alerting Batch Hybrid Analytics Triage / Visualise SNA Visual Analytics Case Management ECM Internal data Unstructured Data 3 rd Party Data The Internet DQ / single view Extract entities from text Free Text Link entities Create discrete networks Batch Data Ingest DI / DQ SNA Content Categoriser Security models Audit trails Federation of data Model management Networked Intelligence Warehouse Data Governance Meta-data management Data lineage Reporting & Explore Visual Analytics Intel Management IM / SAND

20 2020 ANALYTICS BANKING IS PUSHING REAL-TIME BOUNDARIES

21 2020 ANALYTICS CASES FOR REVIEW AUTOMATICALLY GENERATED Inspector s work queue High risk cases Low risk risk cases C o p y r i g h t , SAS In s ti tu t e I nc. All r i gh t s r e se r ve d.

22 2020 ANALYTICS PROVIDE THE COMPLETE PICTURE TO USER Basic case details provided in one place Data provided to support investigation Reasons why the case was created automatically populated

23 2020 ANALYTICS LET THEM EXPLORE THE DATA Diagrams for all cases are automatically generated by the system

24 2020 ANALYTICS WORKING INTERACTIVELY WITH DATA IS KEY C o p y r i g h t , SAS In s ti tu t e I nc. All r i gh t s r e se r ve d.

25 2020 ANALYTICS WORKING INTERACTIVELY WITH DATA IS KEY

26 2020 ANALYTICS THE FASTEST GROWING SECTOR IN IT THIS IS WHY Big Data + Big Analytics = Big Benefits

27 2020 ANALYTICS / IMAM.HOQUE@SAS.COM