Session 97 PD, Predictive Modeling for Actuaries: Predictive Modeling and Data Science Around the World Moderator: Brian D. Holland, FSA, MAAA

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1 Sessin 97 PD, Predictive Mdeling fr Actuaries: Predictive Mdeling and Data Science Arund the Wrld Mderatr: Brian D. Hlland, FSA, MAAA Presenters: Niklas Vasilglu, Ph.D. Flavi Villanustre

2 Frm linear regressin t matrix factrizatins Niklas Vasilglu

3 The statistician s tlkit Hypthesis testing (Likelihd Ratis) Crrelatins (Pearsn s) Linear Regressin Btstrap Cnfidence Intervals Jint/Cnditinal prbability estimatrs NnParametric Tests

4 The 100+ year ld predictr Regressin Linear Lgistic Quantile Pissn Plynmial Nnparametric

5 The classic ne y=σa i x i

6 Why d peple lve regressin? They spent hurs studying it in cllege It is simple Easy t interpret Peple think they can d causal analysis A lt f free sftware Nice and rigrus cnfidence intervals

7 Frm gd lw t bad high dimensins Data in the past Structured Clean Well designed and actively cllected Data nw Unstructured Dirty Passively cllected Exchange agencies

8 Unstructured Data Text Clickstream Buying histry Lcatin Images Weblgs UPS shipment traffic etc

9 The pwer f unstructured data If yu are nt cnvinced abut them lk at Ggle Netflix Amazn Online Ad cmpanies Generate billins f revenues

10 The high-dimensinal regressin The frmula remains the same Unstructured variables becme categrical Hw d yu slve it nw?

11 Scaling issues 1. Strage 2. Management a. Querying b. Jining 3. Slver a. Training b. Eliminate redundant variables

12 Scaling Slutins The distributed strage GFS/HDFS (early 2000) The birth f map-reduce Hadp (2005) BigSQL systems (Ggle BigQuery) (2012) Vpal Wabit (2008)

13 Vpal Wabit (http://hunch.net/~vw/) Ordinary Least Square is O(Nk 2 ) prhibitive Online training is the slutin One sample at a time StepWise Regressin is prhibitive L 1 regularizatin is the slutin When dimensinality is huge ~1B crdinate gradient descent feature hashing

14 Interactins Linear mdels are gd but they might be insufficient Fr example user recmmendatins Given histrical user-mvie ratings predict new ratings user_rating= Σa i *user + Σb i *mvie This apprach desn t wrk

15 The pwer f 2nd rder effects

16 Similar Items

17 Back t regressin Wuldn t be nice if we culd use the pairwise interactins in high-dimensinal regressin? y=σa i x i +ΣΣb ij x i x j???? b is k x k Fr k=1m b is ~1B!!!! k k B

18 The factrizatin machine (www.libfm.rg) y=σa i x i +ΣΣ <b i,b j > x i x j k d b k B b

19 A few ingredients f a gd recipe in the science f data Massive Data Feed aggregatin High Dimensinality Pair-wise data interactins Apprximate methds fr training

20 Big Data analytics in the real wrld SOA 2014 Annual Meeting & Exhibit Oct , Orland, Flrida Presenter: Dr. Flavi Villanustre Lead f the HPCC Systems Initiative

21 Real wrld examples f Big Data Analytics 2

22 Real Life Graph Analytics Scenari This view f carrier data shws seven knwn fraud claims and an additinal linked claim. The Insurance cmpany data nly finds a cnnectin between tw f the seven claims, and nly identified ne ther claim as being weakly cnnected. WHT/

23 Real Life Graph Analytics Task After adding the LexID t the carrier Data, LexisNexis HPCC technlgy then explred 2 additinal degrees f relative separatin Family 1 Result The results shwed tw family grups intercnnected n all f these seven claims. Family 2 The links were much strnger than the carrier data previusly supprted. WHT/

24 Prperty Transactin Risk Three cre transactin variables measured Velcity Prfit (r nt) Buyer t Seller Relatinship Distance (Ptential f Cllusin) Flipping Cllusin Prfit WHT/

25 Overview Prperty Transactin Risk ±700 mill Deeds Derived Public Data Relatinships frm +/- 50 terabyte database Data Factry Clean Cllusin Graph Analytics Chrnlgical Analysis f all prperty Sales Large Scale Suspicius Cluster Ranking Histrical Prperty Sales Indicatrs and Cunts Persn / Netwrk Level Indicatrs and Cunts WHT/

26 Suspicius Equity Stripping Cluster WHT/

27 Results Large scale measurement f influencers strategically placed t ptentially direct suspicius transactins. All BIG DATA n ne supercmputer measuring ver a decade f prperty transfers natinwide. BIG DATA Prducts t turn ther BIG DATA int cmpelling intelligence. Large Scale Graph Analytics allw fr identifying knwn unknwns. Flrida Prf f Cncept Highest ranked influencers Identified knwn ringleaders in flipping and equity stripping schemes. Typically nt cnnected directly t suspicius transactins. Knwn ringleaders nt the Highest Ranking. Clusters with high levels f ptential cllusin. Clusters fflading prperty, generating defaults. Agile Framewrk able t keep step with emerging schemes in real estate. WHT/

28 Scial Graph and Prescriptins Scenari Healthcare insurers need better analytics t identify drug seeking behavir and schemes that recruit members t use their membership fraudulently. Grups f peple cllude t surce schedule drugs thrugh multiple members t avid being detected by rules based systems. Prviders recruit members t prvide and escalate services that are nt rendered. Task Given a large set f prescriptins. Calculate nrmal scial distributins f each brand and detect where there is an unusual scializatin f prescriptins and services. Result The analysis detected scial grups that are surcing Vicdin and ther schedule drugs. Identifies prescribers and pharmacies invlved t help the insurer fcus investigatins and intervene strategically t mitigate risk. WHT/

29 Medicaid Case Study Scenari Prf f cncept fr Office f the Medicaid Inspectr Generatin (OMIG) f large Nrtheastern state. Scial grups game the Medicaid system which results in fraud and imprper payments. Task Given a large list f names and addresses, identify scial clusters f Medicaid recipients living in expensive huses, driving expensive huses. Result Interesting recipients were identified using asset variables, revealing hundreds f high-end autmbiles and prperties. Leveraging the Public Data Scial Graph, large scial grups f interesting recipients were identified alng with links t prvider netwrks. The analysis identified key individuals nt in the data supplied alng with cnnectins t suspicius vlumes f prperty flipping ptentially indicative f mrtgage fraud and mney laundering WHT/

30 The Data/Infrmatin flw Cyber Security Financial Services Gvernment Big Data Health Care Insurance Online Reservatins Retail Telecmmunicatins Transprtatin & Lgistics Weblg Analysis INDUSTRY SOLUTIONS Custmer Data Integratin Data Fusin Fraud Detectin and Preventin Knw Yur Custmer Master Data Management Weblg Analysis WHT/

31 Cmpnents f the Open Surce HPCC Systems platfrm The HPCC Systems platfrm includes: Thr: batch riented data manipulatin, linking and analytics engine Rxie: real-time data delivery and analytics engine A high level declarative dataflw language: ECL Implicitly parallel N side effects Cde/data encapsulatin Extensible Highly ptimized Builds graphical executin plans Cmpiles int C++ and native machine cde Cmmn t Thr and Rxie An extensive library f ECL mdules, including data prfiling, linking, graph analytics and Machine Learning WHT/

32 The Open Surce HPCC Systems platfrm WHT/

33 Recrd linkage, entity disambiguatin, entity reslutin 14

34 Scalable Autmated Linking Technlgy (SALT) The acrnym stands fr Scalable Autmated Linking Technlgy Declarative linking paradigm Cmpiles t ECL, which cmpiles t C++ Prvides fr autmated data prfiling, QA/QC, parsing, cleansing, nrmalizatin and standardizatin Sphisticated specificity based linking and clustering Links entities t attributes and entities tgether Data Surces Iterative prcess leveraging inference gained in prir Data Preparatin Prcesses (ETL) steps 42 Lines f SALT 3,980 Lines f ECL 482,410 Lines f C++ Prfiling Parsing Cleansing Nrmalizatin Standardizatin Matching Weights and Threshld Cmputatin Blcking/ Searching Weight Assignment and Recrd Cmparisn Recrd Match Decisin Linked Data File Additinal Data Ingest Linking Iteratins WHT/ Recrd Linkage Prcesses 15

35 Rules based vs. prbabilistic recrd linkage Thrughly emplying statistical algrithms, SALT has the tw significant advantages ver rules: 1. SALT sufficiently explre all credible matches INPUT SALT Match, because the system has learnt that Villanustre is specific because the frequency f ccurrence is small and there is nly ne present in Atlanta Flavi Villanustre, Atlanta Javi Villanustre, Atlanta Recrd 1 Recrd 2 Errr RULES NO MATCH, because the rules determine that Flavi and Javi are nt the same 2. SALT effectively minimizes false matches INPUT Jhn Smith, Atlanta Jhn Smith, Atlanta WHT/ Errr SALT RULES NO Match, because the system has learnt that Jhn Smith is nt specific because the frequency f ccurrence is large and there are many present in Atlanta MATCH, because the rules determine that Jhn Smith and the city fr bth the recrds match 16

36 Entity linkage and assciatins SALT als links entities t frm netwrks f relatinships T clustering Frm disparate data T shwing relatinships WHT/

37 Data Visualizatin 18

38 Data Visualizatin: Result Cell Frmatting Output WHT/

39 Data Visualizatin: Result Cell Frmatting Basic Chart WHT/

40 Data Visualizatin: Result Cell Frmatting Tree Structures WHT/

41 Data Visualizatin: Result Cell Frmatting Graphs WHT/

42 Takeaways Data integratin, and particularly entity reslutin, is at the cre f Big Data analytics Understanding the semantics f the data is very imprtant Data prfiling, parsing, cleansing, nrmalizatin, standardizatin and linking represent 80% f the battle Iterative analysis is paramunt Having an adequate level f abstractin in the tlset used can reduce the effrt significantly Visualizatin is key, and nt just in the explratry data analysis phase WHT/

43 Useful Links LexisNexis Open Surce HPCC Systems Platfrm: SALT: Machine Learning prtal: Online Training: The HPCC Systems blg: Our GitHub prtal: https://github.cm/hpcc-systems Cmmunity Frums: Cntact infrmatin: Phne: WHT/ CONFIDENTIAL. Cpyright 2014 LexisNexis. All rights reserved. 24

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