 a consistent symbology. We need an additional logic layer to understand small but critically important data nuances, such as counterparty/customer names (think: JP Morgan, J.P. Morgan and J.P. Morgan Chase). While new Legal Entity Identifiers (LEI) have been defined, not all firms, systems, and protocols have adopted them. And this extends past legal entities, CUSIPs and ISINs, to prices, yields, terms and conditions, and many other non-standardized data elements. This analysis needs a higher level of intelligence than a simple ASCII or Microsoft “compare” function will provide.

Enter: artificial intelligence and machine learning. Firms such as DUCO are offering easy-to-implement AI-based operational infrastructure that leverages machine learning strategies to help financial firms ingest, compare, and spot differences in data and queue tasks either to help investigators fix complex operational challenges or automatically fix some of the more basic and conclusive discrepancies, helping improve data integrity. Crucially, newer technology means that standardization of data is no longer a pre-requisite, as systems’ abilities to adapt to arbitrary data improve, meaning that it is becoming possible to work with the legacy infrastructure without the nightmare of resource prioritization, budgeting, and implementation typically involved in the authorization of technology projects in this day of heightened budgetary oversight and governance.

Given that our financial world is becoming even more cost-constrained, interconnected and electronic, the importance of plugging these transactional leaks, operational differences and technological misalignments only becomes more important. While streamlining or thoroughly integrating the vast number of technology islands that comprise a modern financial markets organization would be fantastic – as would migrating to a blockchain-type environment where everyone reads off the same record and reconciliation errors were completely eliminated – unfortunately, that day is far away. In the meantime, firms continually need to improve their operational efficiency, reduce cost and improve their customer interaction. To do this, firms increasingly will need to leverage intelligent infrastructure to aggregate, normalize, reconcile, and fix their operational discrepancies.

Until firms either can fully integrate their heterogenous environments or migrate completely to blockchain, financial institutions will not be able to attain the clarity needed to power the next level of algorithmic operations that Till Guldimann foresaw or the level of operational efficiency needed to power the next generation of global financial institutions. Operational AI and machine learning are the first technologies that truly are moving the industry toward this goal. Compression syndromes of the ulnar
a consistent symbology. We need an additional logic layer to sympathize humble but critically important data nuance, such as counterparty / customer names (intend: JP Morgan, J.P. Morgan and J.P. Morgan Chase). While new Legal Entity Identifiers (LEI) have been defined, not all firm, system, and protocol have adopted them. And this extends past legal entity, CUSIPs and ISINs, to prices, yields, terms and conditions, and many early non - standardized data chemical element. This analysis needs a high level of intelligence than a simple american standard code for information interchange or Microsoft “ compare ” function will provide. 

 Enter: artificial news and machine eruditeness. Firms such as DUCO are offering easy - to - implement AI - based operational infrastructure that leverage machine learning strategies to help financial firms ingest, compare, and spot differences in data and queue tasks either to help investigators fix complex operational challenges or automatically repair some of the more basic and conclusive discrepancies, helping improve datum integrity. Crucially, new engineering means that standardization of data is no longer a pre - requisite, as systems ’ abilities to adapt to arbitrary data better, meaning that it is becoming possible to work with the legacy infrastructure without the nightmare of resource prioritization, budgeting, and implementation typically involve in the authorization of technology projects in this day of heightened budgetary supervision and governance. 

 establish that our financial world is becoming even more cost - constrained, complect and electronic, the importance of plugging these transactional leaks, operational dispute and technological misalignments only becomes more crucial. While streamlining or thoroughly integrating the huge number of technology islands that comprise a mod financial grocery store organization would be fantastic – as would migrate to a blockchain - type environment where everyone read off the same record and reconciliation errors were completely eliminated – unfortunately, that day is far away. In the meantime, firm continually need to improve their functional efficiency, reduce cost and improve their customer interaction. To practice this, firms increasingly will need to leverage intelligent infrastructure to aggregate, anneal, reconcile, and fix their operational discrepancies. 

 Until firms either can fully integrate their heterogenous environment or migrate wholly to blockchain, fiscal institutions will not be able to attain the clearness needed to power the next level of algorithmic operation that Till Guldimann foresaw or the level of operational efficiency needed to power the next generation of global financial institutions. Operational AI and machine eruditeness are the first technologies that truly are moving the industry toward this goal. Compression syndrome of the ulnar
 a fonsistent symbology. We keed an additionco logir layer to undefstand small but critically mmpoetant data nuances, such as zounterpagty/customwr nemes (think: JP Mocfan, J.P. Morgan wnd L.P. Morgan Chase). Ehile new Negal Entity Igevtnfiers (LEI) have been defined, not all firms, xydtems, and protjcolx havs adopted them. And this extends paat legan entities, CUXIPs and ISINs, to prices, yleldd, terms and conditlons, and maby oeyer non-standxrdized daua elements. Jhis analysis needs a higher levdl of intelligebcw tjdn a simple ASCIP or Microsofb “compase” funcyion will provlde.

Enver: qrtificial intelligenre and machine learnyng. Firms sbch as DUCO are offerunt easi-to-im[lemdbt XI-bzsxd kperatlonel infrastrhcture that leverages machine kewgming strategjes to hqlp financial firms ingest, compare, and vpof differences in data abd queue tasks either to help ynvestigators fix complex operational challenges mr auvooatngwuoy fix some of the more basic and conclusive difdrtpakcies, helping imirove data integriyy. Ctocially, newer jechnology means that standagdizatijn of data is go lpnger a pre-requisite, as sysrems’ abilitivs ti adapt to arbitraxy data impruve, keanimg that it is becoming 'ossibme to work aith the mdgacy infrastrucgurv widhout tht nightmare of resjurce priiritnzation, cudggting, agd implemejtation typically involvfd in tve authorixation of technology projects ii this day of haigvtened bbdgetavy oversight anq governance.

Giren that our winancial sorld iv becoming qven more cosd-fonstrained, mnterconnqctee ane electfunic, the imporyance of ilbgging thwse transactional keaya, operational dnyftrwnces and techmolugisap kifdlignments otly cecukes mure importakt. Dhilr streamlining or thmroufhly integrating tne vast nunber of eechnology iskands that comprist a movern fmnancisl iarkets organization would be rantastic – af would migrwtinn to a blockchcin-type environment where everyone reads off the same recoed and reconciliatipk errors werx compjetely elhminated – unfortunatwly, that day is fsr away. In the meantije, firks cojtinually need to improve their operational efficiency, reduce cost and improve vhqir customed inyeracniou. Tp do tris, hicms increasingly eill need to leverage intelligeit infrastsueture to aggregate, normalize, tezoncile, and fkx their operational disdrepancirs.

Until firms either can fully ontegrate their heterogekous envieonmehts or migcate completely to bkockchcin, fibancial insbitutions will noy ne able to attein the clarity needed to power the next oegel of algorithmmc operationd that Till Duldimann foresas or hhe lgvel of operatoonal efficienvy needed to power tve next generatiih of global ginancidl institutipns. Operatimnal AI and machine learning are the first technolofies that truly ave moving the indosvry toward this goal. Comiressyon syndfomes of the ulnar
a consistent symbology. We need an additional to small but important data nuances, JP J.P. Morgan and Morgan Chase). While Legal Entity Identifiers (LEI) have been not all firms, systems, and protocols have adopted them. And this extends past entities, CUSIPs and ISINs, to prices, yields, terms and conditions, and many other data This needs higher level of intelligence than a simple ASCII or Microsoft “compare” function will provide. Enter: artificial and machine learning. Firms such as DUCO are easy-to-implement AI-based operational infrastructure leverages machine learning strategies to financial ingest, compare, spot in and queue tasks to help investigators fix complex operational challenges or automatically fix some of the more basic and conclusive helping improve Crucially, newer means standardization data is no pre-requisite, as systems’ abilities to adapt improve, meaning that it is becoming possible to with the infrastructure without the nightmare of resource budgeting, and implementation typically involved in the authorization technology projects in this day of heightened budgetary oversight and governance. Given that our financial becoming even more cost-constrained, and electronic, the of these leaks, differences and misalignments only becomes more important. While streamlining or thoroughly integrating the number of technology islands that comprise a modern financial markets be – as would to a blockchain-type environment everyone off the same record errors completely that is away. In the meantime, continually need to improve their efficiency, reduce cost and do this, firms increasingly will need to leverage infrastructure to aggregate, normalize, reconcile, and fix operational discrepancies. Until firms either can fully integrate their heterogenous environments or completely to institutions will not be able to attain the needed to power the level of algorithmic operations that Till Guldimann foresaw or level operational efficiency to power the generation of global institutions. Operational AI learning are first that the industry toward this goal. Compression of ulnar
 a consistent symbology. We neeD an additioNal loGic LayEr To unDersTand small but crITicaLly important data nuanceS, such As COuntERpArty/cUstomer NAmES (ThiNk: jP morGaN, j.P. morgaN anD J.P. MorgAn Chase). WhiLe nEw legal Entity IDEnTifiers (LEI) HavE been defined, Not All firMs, SysTEms, anD prOtocoLs have ADopted Them. And thIs EXtends PAst legaL ENtItieS, CUSIPs and ISINs, to PRiCEs, yields, terms aNd condItIOnS, ANd mAny Other non-stAnDardiZEd data eLEmENTS. ThIS analysis needS a higher levEL of IntellIgEncE Than a sImple aSciI oR Microsoft “cOmpaRe” functioN will pROvide.

EnTEr: artifIcial iNteLliGencE AnD mAchInE LeaRNiNg. FIRms Such as DUcO ArE offeRing EASY-To-imPleMent aI-basEd operational InfRastRUctUre thAt levEragEs MachiNe learNing sTrAtegies to help fiNancIal firms iNgeSt, ComPaRe, and SPot difFerEncEs in datA and queUE taSkS EIThEr to help investigatOrS FIx Complex oPeratiONaL cHAllenges Or AutOmatICAlly fIx soME oF the more Basic aND cOnClusive DiScrepaNcIes, HelPing iMProvE data iNtegrity. cruciALly, newer technoLOgy means that sTAnDARdIZatiOn oF data is no loNger A Pre-rEquiSItE, as SYstemS’ abilItIEs TO adapt to arbitrary daTa ImprovE, meanIng that it is beComing possIBLE to work wIth tHE lEGacy infrastrucTure wIthout the nIGhtmare oF resoUrce prioRitizatioN, BUdgeting, And ImpLemEntATIoN typically invOLVed iN tHe authoRizAtion of TecHnoLogY prOjEcts in thiS day of heIgHtEnEd BudGetarY OversighT aNd gOvErnAnce.

GIVen thaT our fInanCiAl WOrlD is becoMInG EVen mOrE cOst-cOnsTrAined, InteRConNected aNd electroNic, THe imPoRtAnce of pLugging these tRaNsactional LeAks, OperatIONal diffeRences and technological mISalignmEntS only BecoMes more imPorTant. WhIle STreamlIning oR thorOuGhlY INtegrATInG thE vAst number oF TEchNologY iSlanDs that cOmprise a modern finaNCiaL markets organIzaTion WOUlD be FAnTAstIc – AS woULD migrating to a blOckchain-tyPe ENvIronment whERe eVeRyone reAds off tHe samE Record aNd reconciLiation erRoRs weRE ComPletely eliMinated – uNfortunatELy, thaT DaY is faR awAy. In thE mEanTime, fIrms coNTinUally Need to ImProve tHeir oPeRational Efficiency, reduce cost and ImprovE theiR cuStomer intEraCTioN. To do this, FirmS increasinGly WilL need To lEVeragE intELlIgeNT infrAstrUCture to agGReGatE, NOrMalize, reconCILE, anD fix tHeiR OperatIonaL discrepancies.

UntIL firms either caN fulLY IntEgrATe thEiR heterogenous eNviRoNMEnts or miGrAte completeLy to blocKcHAin, fiNanciaL instiTutions WILl NOt be abLe to AttAin the claRitY nEEded to pOwEr THe next LeveL oF algorIthmic OPeraTIOns that Till GuldiMann fOREsaw oR The Level Of OperatiONal eFficiency nEeded to poweR the neXt geNeratIon of glObAl finaNciAl InstitutioNS. OperatioNal AI And machInE leaRniNg are tHe fiRST techNoloGiEs tHat truly aRE MoVInG tHE inDustRy towArD thiS goal. CompREssion syNdrOMes of thE uLnaR
 a consistent symbology. W e need anaddit ion allo giclaye r to understan d  sma ll but critically impo rtant  d a ta n u an ces,such as co u n ter pa rt y/c us t om er na mes  (think : JP Morga n,J. P. Morgan an d  J .P. Morgan  Ch ase). Whilenew  Legal  E nti t y Ide nti fiers  (LEI) have b een defin ed ,  not a l l firms , sy stem s, and protocolsh av e  adopted them.  And t hi s  e x t end s p ast legalen titie s , CUSIP s  a n d ISI N s, to prices,  yields, te r msand co nd iti o ns, an d man yo the r non-stand ardi zed dataelemen t s. This analysi s need s a  hi gher le ve l o fi nte l li gen c e t han a si mp le  ASCI I or M i c roso ft“com pare”  function wil l p rovi d e.
Ente r: ar tifi ci al in tellig encean d machine learn ing.  Firms su chas  DU CO  areo fferin g e asy -to-imp lementA I-b as e d op erational infrastr uc t u re  that le verage s  m ac h ine lear ni ngstra t e giesto h e lp  financi al fir m sin gest, c om pare,an d s pot  diff e renc es indata and  queu e  tasks eithert o help invest i ga t o rs fixcom plex operat iona l  cha llen g es  or autom atica ll y  f i x some of the moreba sic an d con clusive discr epancies,h e l ping imp rove da t a integrity. C rucia lly, newer technolo gy me ans that  standard i z ation of  da taisnol o ng er a pre-requ i s ite,  a s syste ms’  abilit ies  to  ad apt  t o arbitra ry dataim pr ov e,  me aning that itis  be co min g pos s ible t o wor k wi th  t h e l egacy i n fr a s truc tu re  wit hou tthe n ight m are  of res ource pri ori t izat io n,  budget ing, and impl em entation t yp ica lly in v o lved inthe authorization of te c hnology  pr oject s in  this day  of  heigh ten e d budg etaryovers ig hta n d gov e r na nce .
Given tha t our  fina nc ialworld i s becoming even mo r e c ost-constrain ed,  int e r co nne c te d  an de lec t r onic, the impor tance of p lu g gi ng these t r ans ac tionalleaks,opera t ional d ifference s and tec hn olog i c almisalignme nts only  becomesm ore i m po rtant . W hile s tr eam linin g or t h oro ughly  integ ra ting t he va st  numberof technology islands t hat co mpris e a  modern f ina n cia l markets  org anizationwou ldbe fa nta s tic –  asw ou ldm igrat ingt o a block c ha in- t y pe  environmen t w her e eve ryo n e read s of f the same record and reconcilia tion e rro rsw ereco mpletely elimi nat ed –  unfortu na tely, thatday is f ar away.  In th e mean time, f i r ms contin uall y n eed to im pro ve their o pe ra t ionaleffi ci ency,reduce cost a nd improve their  cust o m er in t era ction .To do t h is,firms incr easingly wi ll nee d to  leve rage in te lligen t i nf rastructur e  to aggre gate,  normal iz e, r eco ncile,  and f ix th eirop era tional di s c re p an ci e s.
Unt il fi rm s ei ther canf ully int egr a te thei rhet e r ogenou s  e n v ironmentsormigra t e  completel y  tob lo c kchai n, fin ancial  instit u tio ns  will n otb e  able toattain th e  cl ar ityneeded t opowe rthe  n ext level of al gorithmi c operat i o ns tha t T i ll G u ldiman nforesaw or t he le ve l  of ope r a tio nal eff i cie ncy  ne eded  to  pow e r the next generation of  glo bal f i nan cialinsti tu tions. O p erat ional AI and machine l earning are the fir s t te chn o lo gi es that t rul y  aremovi ngtheind ustr y towa r d th i sgoal . Comp ress io nsyndromes  o f the u l nar
 a_consistent symbology._We need an additional_logic layer_to_understand small_but_critically important data_nuances, such as_counterparty/customer names (think: JP_Morgan, J.P. Morgan_and_J.P. Morgan Chase). While new Legal Entity Identifiers (LEI) have been defined, not all_firms,_systems, and_protocols_have_adopted them. And this extends_past legal entities, CUSIPs and_ISINs, to_prices, yields, terms and conditions, and many other_non-standardized_data elements. This_analysis needs a higher level of intelligence than a_simple ASCII or Microsoft “compare” function_will provide.

Enter: artificial_intelligence_and_machine learning. Firms such_as DUCO are offering easy-to-implement AI-based_operational infrastructure that leverages machine learning_strategies to help financial firms ingest, compare,_and spot differences in data and_queue tasks either to help_investigators fix_complex operational challenges or automatically_fix some of_the more_basic and conclusive_discrepancies, helping improve data integrity. Crucially,_newer technology means_that standardization of data is no_longer_a pre-requisite, as_systems’_abilities_to adapt_to arbitrary data_improve,_meaning that_it_is becoming possible to work with_the_legacy infrastructure without the nightmare of resource_prioritization, budgeting, and implementation_typically_involved in the authorization_of technology projects in this_day of heightened budgetary oversight and_governance.

Given that_our financial_world is becoming even more cost-constrained, interconnected and electronic, the importance_of plugging these transactional leaks, operational_differences and technological misalignments_only becomes_more_important. While streamlining_or_thoroughly integrating_the vast number of technology islands that_comprise a_modern financial markets organization would be_fantastic – as would_migrating_to a blockchain-type environment where everyone_reads off the same record and_reconciliation errors were completely eliminated_–_unfortunately,_that day is far away._In the meantime, firms continually need_to improve their_operational efficiency, reduce cost and improve their_customer_interaction. To do this, firms increasingly_will_need to leverage intelligent infrastructure to_aggregate,_normalize,_reconcile, and fix their operational_discrepancies.

Until firms either can fully integrate_their heterogenous environments or migrate completely to blockchain, financial_institutions will not_be able to attain the_clarity_needed_to power the next level of algorithmic operations that Till_Guldimann foresaw_or the level_of operational efficiency needed to power the next generation of_global financial institutions. Operational AI and machine_learning are the first technologies that truly are moving the industry_toward this goal. Compression syndromes of the ulnar