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SOA Predictive Analytics Seminar Malaysia 27 Aug. 2018 | Kuala Lumpur, Malaysia Session 3 Upskilling for Predictive Analytics Travis M Short, FSA Upskilling for Predictive Analytics SOA Predictive Analytics Seminar Kuala Lumpur Travis


  1. SOA Predictive Analytics Seminar – Malaysia 27 Aug. 2018 | Kuala Lumpur, Malaysia Session 3 Upskilling for Predictive Analytics Travis M Short, FSA

  2. Upskilling for Predictive Analytics SOA Predictive Analytics Seminar – Kuala Lumpur Travis Short, FSA —27 August 2018 — Pac ific L ife Re

  3. The Actuary — What’s the future Actuary? ‘T he Onc e & F uture Ac tua ry’ – Da vid Holla nd, 1997 SOA Pre side nt – “I n his 1949 a ddre ss a s the first pre side nt o f the So c ie ty o f Ac tua rie s, E dmund M. Mc Co nne y a ske d: ‘What ar e ac tuar ie s?’ ” – … “T he a c tua ry in re a lity is a so und, pra c tic a l ra the r tha n to o the o re tic a l ma the ma tic ia n a pplying simple princ iple s o f pro b a b ilitie s to huma n a ffa irs in the unkno wn future .” – “T his is no t a b a d de finitio n fo r 1949, o r e ve n fo r 1997.” – “T he “Onc e a nd F uture Ac tua ry” is the mo de l b uilde r a nd ma na g e r, the fina nc ia l a rc hite c t a nd e ng ine e r, who c a n la y the fo unda tio n fo r a se c ure fina nc ia l future . I t is o urs to inve nt.” – T he Ac tua ry ma g a zine “a na lytic s” wo rd c o unt: 0 – T he Ac tua ry ma g a zine “CD-ROM” wo rd c o unt: 3 SOA Pre dic tive Ana lytic s Se mina r 2018 Aug - Upskilling fo r Pre dic tive Ana lytic s 2 Pac ific L ife Re

  4. Why ‘upskill’?— What’s your ‘data science’ value proposition? Amy He ine ike , Prime r AI: Upskilling a c tua rie s – “ “…da ta sc ie nc e is a b it o f a ho t – Wha t suppo rts yo ur g o a ls, a s a n individua l o r a s to pic , a nd so I think the re a re a lo t o f a n o rg a niza tio n? Wha t’ s yo ur e nd g a me ? pe o ple who think tha t if the y c a n ha ve – Sha ping yo ur pa th fo r upskilling a nd to o ls to the ‘ da ta sc ie nc e ’ la b e l, the n ma g ic , c o nside r ha ppine ss, a nd mo ne y will c o me to the m. So I re a lly sug g e st fig uring o ut – Ac tua rie s a nd no n-a c tua rie s wo rking to g e the r, wha t b its o f da ta sc ie nc e yo u a c tua lly struc turing te a ms fo r suc c e ss c a re a b o ut.” https:/ / www.kdnug g e ts.c o m/ 2018/ 05/ 8-use ful-a dvic e s-a spiring -da ta -sc ie ntists.html> SOA Pre dic tive Ana lytic s Se mina r 2018 Aug - Upskilling fo r Pre dic tive Ana lytic s 3 Pac ific L ife Re

  5. EY— on applications of advanced analytics for insurance https:/ / www.e y.c o m/ Pub lic a tio n/ vwL UAsse ts/ Adva nc e d_a na lytic s_fo r_insura nc e / %24F I L E / Adv-a na lytic s_insura nc e _AUNZ00000335.pdf SOA Pre dic tive Ana lytic s Se mina r 2018 Aug - Upskilling fo r Pre dic tive Ana lytic s 4 Pac ific L ife Re

  6. The Actuary: “analytics” word count 2012 “Adva nc e d Busine ss Ana lytic s fo r Ac tua rie s is a se t o f to o ls a nd te c hniq ue s use d to de sc rib e , pre dic t, a nd re c o mme nd b usine ss c o urse s o f a c tio n b a se d o n c o nsume r a nd distrib uto r b e ha vio r. I t dra ws fro m ma ny disc ipline s. I t re lie s he a vily o n va st a mo unts o f da ta a nd c o mputing po we r, sta tistic s, mo de ling , o ptimiza tio n, da shb o a rd a nd a le rts, ma rke t re se a rc h, a nd c luste ring . Adva nc e d b usine ss a na lytic s pro vide s e mplo ye rs with insig htful de c isio n ma king a nd a ffo rds the o ppo rtunity to a sse ss a ma rke tpla c e fro m a to ta lly ne w pe rspe c tive .” Ac tua rie s in Adva nc e Busine ss Ana lytic s White Pa pe r, 2012, L isa T o urville (Cha ir) SOA Pre dic tive Ana lytic s Se mina r 2018 Aug - Upskilling fo r Pre dic tive Ana lytic s 5 Pac ific L ife Re

  7. The Actuary: “analytics” word count 2018: PA Pilot a nd E xa m PA “Mo dule 1: Pre dic tive Ana lytic s T o o ls Mo dule 2: E ffe c tive Pro b le m De finitio n a nd Pro je c t Ma na g e me nt Mo dule 3: Da ta De sig n, T ra nsfo rma tio n a nd Visua liza tio n Mo dule 4: Da ta E xplo ra tio n Mo dule 5: F e a ture Ge ne ra tio n a nd Se le c tio n Mo dule 6: Mo de l De ve lo pme nt a nd Va lida tio n Within e a c h mo dule , the re we re kno wle dg e c he c ks, e xe rc ise s, e nd-o f-mo dule te sts a nd o ppo rtunitie s to inte ra c t with o the r pa rtic ipa nts via a priva te disc ussio n fo rum. At time s, the mo dule instruc tio ns wo uld a sk fo r spe c ific inte ra c tio ns. At a ny time , pa rtic ipa nts we re fre e to use the fo rum to ma ke c o mme nts o r a sk fo r he lp. T he pa rtic ipa nts wo rke d o n a va rie ty o f da ta se ts, using RStudio to pe rfo rm the a na lyse s.” SOA Pre dic tive Ana lytic s Se mina r 2018 Aug - Upskilling fo r Pre dic tive Ana lytic s 6 Pac ific L ife Re

  8. Exam PA: Predictive Analytics 2018 De c e mbe r E xa m PA 1. Pre dic tive Ana lytic s Pro b le ms a nd T o o ls (R, RStudio ) 2. T o pic : Pro b le m De finitio n 3. T o pic : Da ta Visua liza tio n 4. T o pic : Da ta T ype s a nd E xplo ra tio n 5. T o pic : Da ta I ssue s a nd Re so lutio ns 6. T o pic : Ge ne ra lize d L ine a r Mo de ls 7. T o pic : De c isio n T re e s 8. T o pic : Cluste r a nd Princ ipa l Co mpo ne nt Ana lyse s 9. T o pic : Co mmunic a tio n T he PA E xa m is a dministe re d a s a five -ho ur pro je c t re q uiring a na lysis o f a da ta se t in the c o nte xt o f a b usine ss pro b le m a nd sub missio n o f a re po rt. https:/ / www.so a .o rg / E duc a tio n/ E xa m-Re q / e du-e xa m-pa -de ta il.a spx SOA Pre dic tive Ana lytic s Se mina r 2018 Aug - Upskilling fo r Pre dic tive Ana lytic s 7 Pac ific L ife Re

  9. Exam PA Syllabus Textbooks F our te xtbooks on sylla bus – I SL te xtb o o k – de finite ly (fre e o nline ) – R fo r e ve ryo ne – a n o ptio n to le a rn R – Da ta visua liza tio n … c urre ntly o nline • http:/ / so c viz.c o / – Re g re ssio n mo de ling … • Mig ht b e a re fre she r o ptio n fo r yo u – No te tha t I SL a nd the re g re ssio n b o o k a re a lso o n the pre -re q E xa m SRM sylla b us SOA Pre dic tive Ana lytic s Se mina r 2018 Aug - Upskilling fo r Pre dic tive Ana lytic s 8 Pac ific L ife Re

  10. Intro to Statistical Learning – videos too! ISL Vide os online – Yo ur ne w c o mmute ma te ria l – https:/ / la g unita .sta nfo rd .e du/ c o urse s/ Huma nitie sSc ie nc e s/ St a tL e a rning / Winte r2016/ info – On Yo uT ub e to o ( se e link b e lo w) https:/ / www.r-b lo g g e rs.c o m/ in-de pth-intro duc tio n-to -ma c hine -le a rning -in-15-ho urs-o f-e xpe rt-vide o s/ SOA Pre dic tive Ana lytic s Se mina r 2018 Aug - Upskilling fo r Pre dic tive Ana lytic s 9 Pac ific L ife Re

  11. ISL: flexibility vs interpretability Sta tistic a l le a rning – Yo u’ ll le a rn a va rie ty o f po te ntia l mo de ls – Ho w muc h do e s inte rpre ta b ility ma tte r? • GDPR, rig ht to a n e xpla na tio n? SOA Pre dic tive Ana lytic s Se mina r 2018 Aug - Upskilling fo r Pre dic tive Ana lytic s 10 Pac ific L ife Re

  12. ISL: training and holdout testing Mode lling princ iple s… – T he g re e n tra ining mo de l a ppa re ntly ha s b e tte r fit – ho we ve r tha t do e s no t ho ld fo r a te sting sa mple SOA Pre dic tive Ana lytic s Se mina r 2018 Aug - Upskilling fo r Pre dic tive Ana lytic s 11 Pac ific L ife Re

  13. ISL: practical application of many models… Re g re ssion Cla ssific a tion – L o g istic re g re ssio n (le ft), line a r disc rimina nt a na lysis, QDA, K NN – E xa mple he re o f ho ldo ut fit dive rg ing fro m the tra ining fit fo r the ‘ g re e n’ mo de l T re e Ba se d Me thods Support Ve c tor Ma c hine s Cla ssific a tion – PCA, K -me a ns c luste ring , hie ra rc hic a l c luste ring , SOA Pre dic tive Ana lytic s Se mina r 2018 Aug - Upskilling fo r Pre dic tive Ana lytic s 12 Pac ific L ife Re

  14. Data Visualization: A Practical Introduction (ggplot for R) Da ta Visua liza tion – so c viz.c o – g g plo t pa c ka g e a g o to fo r R – No te the ma p c o de b e lo w is simple r tha n c o de fo r visua ls sho wn to the le ft SOA Pre dic tive Ana lytic s Se mina r 2018 Aug - Upskilling fo r Pre dic tive Ana lytic s 13 Pac ific L ife Re

  15. This map was the US Presidential election – Re d (Re pub lic a n) i.e . T rump – Blue (De mo c ra t) SOA Pre dic tive Ana lytic s Se mina r 2018 Aug - Upskilling fo r Pre dic tive Ana lytic s 14 Pac ific L ife Re

  16. Beyond Intro to Statistical Learning E le me nts of Sta tistic a l L e a rning – F ro m I SL : “…I n this ne w b o o k, we c o ve r ma ny o f the sa me to pic s a s E SL , b ut we c o nc e ntra te mo re o n the a pplic a tio ns o f the me tho ds a nd le ss o n the ma the ma tic a l de ta ils.” – E SL pro vide s mo re tho ro ug h ma the ma tic a l de ta il Va rious othe r options – Ma c hine L e a rning : A Pro b a b ilistic Pe rspe c tive : K e vin P. Murphy – Pa tte rn Re c o g nitio n a nd Ma c hine L e a rning b y Christo phe r Bisho p SOA Pre dic tive Ana lytic s Se mina r 2018 Aug - Upskilling fo r Pre dic tive Ana lytic s 15 Pac ific L ife Re

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