Ad ria n W e is s, U B S AG F ra n k B lo ck, P re d ict A G P a tric k S c h ne m a nn, P re d ic t A G

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1 Ad ria n W e is s, U B S AG F ra n k B lo ck, P re d ict A G P a tric k S c h ne m a nn, P re d ic t A G

2 In o u r v ie w, th e w in n e rs w ill b e th o s e b a n k s th a t d e v e lo p a n d u s e i n f o r m a t io n t o s e iz e m a r k et s h a r e f r om t h ei r l e s s t a le n t ed c o m p et it or s B a n k A n a ly s t, M o rg a n S ta n le y D e a n W itte r A nalysts, in vestors a nd m anagers a re a ll s eekin g the s am e thing: b anks w hich a re truly a nd e ffectively le veraging c u s t om er - lev el i n f o r m a t io n t o c r ea t e c o m p et it iv e a d v a n t a g e. Ol iv er W y m a n & Co m p a n y

3 4 m io. p riva te r e ta il c lie n ts w ith a sse ts < C H F M a r k et s h a r e > 3 0 %, m a r k et p e n e t r a tio n > 4 0 % U B S of f e r s f u ll p r od u c t r a n g e R etail b ankin g o f U BS is v ery p rofitable N e w c o m p e tito rs, e.g. G E C a p ita l B a nk, P o s tfina nc e Traditio nal m arketin g b ecom ing m ore e xpensive a nd le ss e ffective Integratio n o f m ultip le d istribution c hannels Focus o n im provin g the p rofitability o f e xisting custom er p ortfolio w ith c ross-/up-sellin g a nd retentio n

4 B e t t e r c u s t o m e r s e le c t io n s b a s e d o n p r e d ic t iv e m o d e ls i m p r ov e t h e ef f e c t iv en es s of : - D ire ct m a ils - Cross sell activities of branches - R e te nt ion p ro g ra m s o f b ra n c h e s D e c i s i o n s u ppo r t f o r pr o d u c t de v e l o pm e n t an d pr i c i n g strategies based on detailed analysis of p ro d uc t/c h a nne l us a g e, t ra n s a c tion b e ha vi o r a n d pr o f i t abi l i t y at t h e c u s t o m e r l e v e l

5 Inform atio n-driven m arketin g requires n ew s kill s ets: -u nd e rs ta n d ing d a ta -analyzing d ata w ith s tatistics a nd a rtificial in tellig ence -im ple m entin g k now le dge in b usiness p rocesses - p r o c e s s r een g in eer in g T e c hno lo g y a c ts a s e n a b ler f o r d a ta m ini ng : - l a rg e s ca le d a ta b a se s ( > 200 G b yte, S A S fi ) - D a ta m ini ng t o o ls ( S A S fi, N e u ra lw o rks) - C o m p u tin g p o w e r ( S u n E n te rp rise 4000) I n t e g r at i n g I T - e x p e r t s an d qu an t i t a t i v e an al y s t s i n t o business organization

6 S trategic decis ion s upport Marketing D B Pricing Product development Communication strategies Predictive models Cus tomer P rofiling Reports Marketing information at the cus tomer level Direct mail Branch network Call center Internet

7 Data s ources CIF Profitability Mortgages Accounts S ecurities Cards Marketing execution C a m p a i gn m a n a ge m e n t In te rfa c e s to c u s to m e r contact systems Marketing DB Datamining Logistic regression Ne u ra l n e tw o rk s Gen et ic a lg o r it h m s Decision support C om plex re porting OL A P Ad - h o c q u e r y

8 P r oof - of - C on c e p t : Q u icks ta rt I ( 96/ 97) P rod uction : QuickStart II ( 97/98) Merger UBS - SBC QuickStart III ( 98/99) Integration: Qu ic k S t a r t I V ( 9 9 ) SAS/STAT fi A utoma tion, param etrizatio n SAS-Macros / AME A dd f unctional ity AM E Reporting AM E Reporting A dd f unctional ity QS I QS I I QS III QS I V Me t a d a t a i n di ctionaries Mi g r a t e D B t o ne w b a nk b y ch anging dictio naries o nly I n t e gr a t i n g d a t a of t w o b a n k s Sourcing Mo r e s o u r c e s f r o m sim ila r d ata legacy systems in ne w b a nk R T B, ex S B C R T B, ex S B C R T B, ex S B C A b a c u s, ex U B S A b a c u s, ex U B S

9 Reporting Mo deling Di re c t M a il Qu ic k S t a r t I V Qu ic k S t a r t I I Qu ic k S t a r t I I I SAS fi an d o t h e r appl SAS fi ma c r os, met a d a t a dr i v e n m a ppi n g pr o c e s s e s SAS fi dat a s e t s f o r dat a w a r e h o u s e datamod el = b usiness mod el SAS fi ma c r os, m e t a dat a dr i v e n do w nload pr ocesses Sources i c at i o n s Param eterized ro llup p rocesses SAS fi v i e w s, an al y t i c al dat a s e t Benefits: S c al abl e c o n c e p t ( M i o. c u s t o m e r s, > 1000 v a ria b le s / c u sto m e r) b a se d o n a p la t f o r m - in d ep en d e n t S A S fi wa r e h o u s e E a s ily p o rta b le to o the r e nvi ro nm e n ts (m e rg e r p ro v e n!) R e d u ce d co m p l e x i t y b e ca u s e data model = business model A ll p a ra m e te rs i n m e ta d a ta f ile s 3 6 mon t h h is t or y d a t a of 4 M io c u s t omer s o n S u n E n te rp rise 4000 S e rve r w ith 1 Tbyte disk RTB Abacus

10 Process: R ollu p Cr ea t e A n al y t i c al Va ria b le s Explorative Da ta - An alysis Mo del- B uild in g Scoring SAS fi - Co d e : Me tadata : Input Interface Environment Me ta d a ta Ou tp u t Interface Benefits: A u to m a ti o n i n c re a s e s p ro d u c ti v ity ( p ro d u c tio n c y c le f ro m 4-6 w e e k d o w n to 2-5 d a y s ) S tandard p rocedures a ssure c onsistent q uality L e s s tr a ini ng r e q u ire d f o r ne w ly hi re d d a ta m ine rs M odule s a llo w to e xpand functionality o n a n in crem ental b asis

11 Direct mails (examples): M utual fund s Phonebanking Inve s t a c c o unt VI S A c a rd Increase of purchase rates (again st traditio nal s electio ns b y p roduct m anagers) % Cross-selling branches (examples): M utual fund s Inve s t a c c o unt VI S A c a rd Increase of purchase rates (again st traditio nal s electio ns b y s ale s reps) 50 %

12 R apid a pplic ation d evelopm ent d eliv ered e arly b usiness v alue G eneric b usiness m odel w as e ssential in m erger s ituation E volutionary d esign a llo w ed the s ystem to g row a ccord ing to b usiness n eed s C o ns ta nt t e s tin g i n a p ro d uc tive e nvi ro nm e n t a s s u re d r e le va nc e o f inve s tm e nt s A u t o m a t ion of s t a n d a r d p r oc ed u r es s ig n if ic a n t ly i n c r ea s e d p r od u c t iv it y A u t o m a t ion k eep s k n ow le d g e i n p r oc es s e s, n o t i n h e a d s of ex p e r t s E ncapsulation, m odulariz ation a nd s tric t in terfacing a llo w ed to m anage com plexity o f technical in frastructure P a ra m e te riz a ti o n i n m e ta d a ta r e d u c e s m a in te n a n c e e ffo rts a n d i n c re a s e s UB S flexibility A G Ad r ia n M. W e is s M arket M anagemen Aeschenvorstadt 48 C H B a sel, S w itzerla nd adr ian -m.w eiss@ ubs.com t P rivate C lients Predict AG D r. P a tr ic k S c h ne m a nn & Dr. F ran k B lo c k We ih e rw e g 18 C H R e ina ch, S w itzerlan d patr ick.schue nem ann@ predict. ch Q u ic k S ta r t a n d the A u to m a ti c M o d e ling E n g ine have b een d eveloped in c olla boration w ith Predict AG, Switzerland

P a g e 5 1 of R e p o r t P B 4 / 0 9

P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e