Global Consulting Practice. White Paper. The Changing Face of Trade Surveillance and the Role of Analytics

Transcription

1 Global Cosultig Practice White Paper The Chagig Face of Trade Surveillace ad the Role of Aalytics

2 About the Author Suil Kumar Suil Kumar is a seior busiess cosultat with Tata Cosultacy Services' Capital Markets Cosultig Group. He is a solutios maager leadig Compliace ad Surveillace offerigs. He has over 19 years of experiece spaig across various NBFC fuctios, capital markets domai cosultig, ad IT solutios delivery. He has worked with some of the leadig regulators across the globe as cosultat i surveillace ad market oversight fuctios.

3 Abstract New fiacial products, tradig veues, ad advaced tradig techiques (such as algorithmic ad HFT) are makig capital market surveillace icreasigly challegig ad complex for regulators across the world. Iovatio has drive complexity i the global marketplace. This is evidet from the ever-icreasig volumes i dark pools, ad high frequecy tradig ad crossig egies becomig the ew ormal. Regulators across the world are resortig to the two-proged strategy of deterrece ad detectio to make securities markets a safer place to ivest ad to retai ivestor cofidece. They are collaboratig with each other ad sharig their experieces to equip their surveillace systems with advaced detectio techiques. They are also implemetig striget regulatory provisios with massive pealties for violatios to curb market abuse. Surveillace today etails a cross-market ad cross-asset class view of trades (or eve orders). Several aggregatios have to be made to draw the big picture ad detect potetial violatios. Itesive patter aalysis ca accurately detect tradig malpractices ad market abuse. It has emerged as a major subject area for research ad is beig icreasigly used i advaced detectio systems. Coupled with other capabilities such as Big Data aalytics, it has become the backboe of ear real-time ad order level surveillace. This paper highlights some emergig treds i securities market tradig ad i the surveillace domai. It delves ito the role of aalytics i capital market surveillace from the regulator's ad SRO's perspectives ad examies the efficacy of surveillace aalytics ad the ew tradig techiques deployed by market itermediaries. The author also predicts a few imperatives that will shape the future of trade surveillace.

4 Cotets 1. A Overview of Trade Surveillace 6 2. Types of Market Abuse ad Maipulatio 7 3. Emergig Treds i Securities Tradig ad Surveillace 7 1: Big Data powerig surveillace techology 7 2: Chagig tradig behavior ad patters 8 3: Dark pools growig to market-threateig proportios 8 4: Growig eed for ear real-time surveillace 9 5: Icreased use of order-level surveillace to couter ew tradig strategies 9 6: Emergece of cross-asset class, cross-market surveillace to detect subtle market abuse 10 7: Cross-commuicatio chael surveillace actig as a exteded arm of market surveillace 10 8: Emergece of cloud computig for capital market participats 10 Key Challeges i Market Surveillace 11 Role of Aalytics i Surveillace 12 Aalytics i Trade Surveillace 12 Effectiveess of Aalytics-Based Models i Trade Surveillace: The Market Experiece 13 Aalytics-based Surveillace: The Future 13 Coclusio 14

5 List of Abbreviatios ASIC ATS CEP EU FINRA HFT IIROC IOSCO MiFID II MoU NASDAQ OMX, NBFC NYSE SaaS SAST SEC SRO TWSE Australia Securities ad Ivestmets Commissio Alterative Tradig System Complex Evet Processig Europea Uio Fiacial Idustry Regulatory Authority High Frequecy Tradig Ivestmet Idustry Regulatory Orgaizatio of Caada Iteratioal Orgaizatio of Securities Commissios Markets i Fiacial Istrumets Directive Memoradum of Uderstadig Joit Veture betwee Natioal Associatio of Securities Dealers Automated Quotatios (NASDAQ) ad OMX a Nordic stock exchage group. No-Bakig Fiacial Compaies New York Stock Exchage Software as a Service Substatial Acquisitio of Stocks ad Takeovers Security ad Exchages Commissio of USA Self-Regulatory Orgaizatio Taiwa Stock Exchage Corp. T+2 Settlemet system that has a turaroud time of two days betwee the trasactio date ad the settlemet date

6 A Overview of Trade Surveillace Over the last few decades, capital markets across the world have see tremedous growth i various market activity parameters. These parameters iclude trade volumes (both traded quatity ad umber of trades), crossborder settlemets, advaced derivatives products, ew tradig strategies, ad veues. This has icreased the complexity of moitorig these trasactios across jurisdictios, safeguardig the iterest of commo ivestors who are ofte o the receivig ed of market abuse by the large players. Trade surveillace detects ay market abuse post facto ad also aticipates ay potetial market abuse by the market participats. It does this through tools, processes, ad ivestigative methods that esure market stability, trasparecy, security, ad efficiecy. Key trade surveillace activities iclude aalysis of the full depth of the order book, examiatio of complete audit trails of orders ad trades i suspected cases, ad aalysis of cross-market ad cross asset-class views of trades etered by the etity uder scrutiy. These are cosolidated to arrive at coclusive evidece of ay suspected market abuse or maipulatio. Types of Market Abuse ad Maipulatio Market abuse ad violatios ca be broadly classified ito the followig categories: Iformatio maipulatio: This ivolves actig o o-public price-sesitive iformatio about ay compay, ot yet available to the geeral market. This iformatio ca be misused to gai ufair advatage over other market players. This icludes platig false ews or setimets i the market. Price maipulatio: This refers to ay attempt to uduly ifluece the market price of fiacial assets with the goal of tamperig with the atural price discovery mechaism i that market. Volume maipulatio: This is doe by geeratig artificial volumes i the market to create a iterest i that particular asset. Such maipulatios are more widely see i illiquid securities. Market view maipulatio: This ivolves givig the market a icorrect picture of the order depth for a security. This creates false market setimets i the short or log side (depedig o the motive) which is the used to reap profits Table 1 lists some typical market abuse scearios i the above metioed categories. 6

7 Abuse Frot ruig Isider tradig Media abuse Pump ad dump Disclosure violatios Circular tradig Clustered tradig Markig the close Spoofig Layerig Descriptio Eterig ito equity or derivatives deliberately prior to a block trasactio that will ifluece the price of the security ad makig a profit from that movemet Tradig o the basis of upublished price-sesitive iformatio to book profits Spreadig false or misleadig market iformatio through commuicatio chaels such as social media ad blogs with the itetio of creatig a icorrect market view Takig a log positio i a scrip (usually a small-cap ad less liquid oe), jackig up the price through false iformatio, ad the exitig the market at the iflated price Violatig disclosure orms for Miimum Public Holdig, Substatial Acquisitio of Stocks ad Takeovers (SAST), ad Listig Iformatio Tradig withi a group of etities with the itetio of falsely rampig up that stock's activity levels ad maipulatig its price Egagig i a series of trasactios that give the impressio of activity or price movemet i a security Securig cotrol of the demad side of both derivative ad uderlyig markets ad maipulatig the price of the derivative or uderlyig asset Placig ad cacellig orders withi a short duratio with the itetio of creatig a false impressio of greater market depth ad capitalizig o the price advatage Uduly ifluecig price i a HFT sceario where a bid or ask is followed by several bids or asks aroud that price. The momet the iitial bid or ask is matched, all remaiig bids or asks that caused the desired price movemet are cacelled Emergig Treds i Securities Tradig ad Surveillace Some emergig treds i the capital market are shapig the behavior of market participats (amely, stock exchages, clearig houses, ad custodias) ad market regulators. Surveillace methods eed to keep up with these chages. 1: Big Data powerig surveillace techology Table 1: Commo Methods of Market Maipulatio Big Data is playig a key role i improvig the effectiveess of surveillace. Trade surveillace is experiecig icreased regulatory scrutiy ad complexities due to the prevalece of multiple commuicatio platforms, makig it difficult for regulators to perform market oversight fuctios. Big Data techology will play a more importat role i moitorig market participats tradig activity both at participats ad regulators eds. This is doe by igestig eormous volumes of various types of data origiatig from differet chaels (such as social media messages, blogs, s, phoe call logs, bak statemets) ad cosolidatig this structured ad ustructured data ito a usable database that will allow advaced patter-matchig aalytics to spot ay aomalous behavior. 7

8 Capital market etities are also icreasigly usig Big Data for ehaced busiess itelligece gatherig. They employ techiques such as Complex Evet Processig (CEP), busiess rule-based text miig, machie learig, ad predictive aalytics to perform market setimet aalysis, aomalous tradig behavior detectio, ad advaced tradig aalytics. However, there are still several challeges to the widespread adoptio of Big Data i capital markets surveillace. These iclude the lack of ehaced tools ad techiques for visualizatio ad successful deploymet by regulators ad ifrastructure etities, ad gaps i the skillsets (especially data scietists) eeded to admiister Big Data aalytics solutios etc. As capital market-specific usage of Big Data become more widespread, firms will ot oly have a better busiess case to adopt it, but will also become techically more equipped to leverage it. 2: Chagig tradig behavior ad patters The speed of tradig cotiues to icrease sigificatly. This is evidet i the fact that high frequecy tradig ow accouts for more tha half of the trades doe o exchages such as the NYSE ad NASDAQ. Equipped with stateof-the-art co-locatio ifrastructure, some of the latest tradig egies are capable of achievig extremely low ed-to-ed latecy. This has ecessitated accurate clock sychroizatio betwee tradig operators ad participats to esure a cosistet timestamp across multiple veues. This is i order to replay the market evets by recostructig tradig history for market oversight purposes. Faster tradig also brigs more complex surveillace eeds. There has bee a outcry agaist HFT, claimig it is tatamout to riggig the market by establishig a ufair speed advatage over huma traders. Some commo market maipulatio techiques i HFT are quote stuffig, spoofig, ad pump ad dump. However, the couter argumet is that it helps icrease liquidity by boostig volumes o tradig veues ad arrowig spreads by arbitragig away price iefficiecies. Regardless of whether HFT is cosidered fair or otherwise, detectig ay aomaly is difficult. Still harder is the task of figurig out the outliers i a data set, makig surveillace a arduous task i such cases. 3: Dark pools growig to market-threateig proportios I a effort to cut security trasactio costs, orders have, over time, moved away from the exchages. This rise of off-exchage tradig is ot i the iterest of the broader market because it reduces price trasparecy to such a extet that the publicly quoted prices for stocks o exchages may o loger properly reflect the state of the market. I the last few years, stock trades i the US have icreasigly moved off the exchages. Europe has also see a icrease i off-exchage trades, though the umbers are much lower. If such off-exchage tradig grows uabated, it ca seriously erode the quality of the lit part of the market ad the trasparecy of the securities market as a whole. This would be aki to withdrawig may of the valuable trasparecy iitiatives from the market that have take years to evolve. Various regulators have tried to brig trasparecy to this otherwise opaque market. For istace, the Fiacial Idustry Regulatory Authority (FINRA) allows o-professioal ivestors to view, free of charge o its website, the total shares traded each week by security i each alterative tradig system (ATS) or dark pool.¹ EU regulators will also have Markets i Fiacial Istrumets Directive II (MiFID II) i place by early It will have a array of ew provisios that will cap tradig activity i dark veues. [1] FINRA, FINRA Makes Dark Pool Data Available Free to the Ivestig Public, accessed o February 24, 2015, 8

9 This growig migratio to off-exchage tradig chaels is also givig rise to some iterestig market dyamics. NASDAQ OMX, which has traditioally bee a strog propoet of tighter regulatios o crossig etworks, is ow cotemplatig seekig regulatory permissio to take over dark pool operatios of baks. Its aim is to create a alterative reveue stream o the stregth of the techology, surveillace prowess, ad regulatory expertise of such dark pool operatios. 4: Growig eed for ear real-time surveillace Deterrece is more effective tha detectio. I today s markets, regulators are uder costat pressure to detect ad respod to ay market abuse, maipulatio, rogue tradig, ad rogue algorithms across asset classes ad markets i the shortest possible cycle time. They are expected to do this through expedited aalysis of real-time ad historical data. Techologically, it is possible to have o-the-fly cosolidated views of trades across markets, asset classes, segmets, ad veues. However, the real challege is decipherig what costitutes market abuse from a busiess stadpoit. The thresholds to determie market abuse chage with market coditios. For example, a tradig patter acceptable i volatile market coditios may become suspicious i a stable market ad vice versa. May forms of market abuse or maipulatio ivolve patter aalysis of tradig activity across multiple days. As a result, they ca be detected oly with a look-back period; this restricts real-time surveillace. So a bulk of this surveillace is doe o a T+1 basis. Near real-time surveillace has become a almost idispesable tool that aalyzes order ad trasactio data at a speed comparable to that of automated tradig itself. Surveillace systems powered by CEP egies form the backboe of preset-day surveillace. 5: Icreased use of order-level surveillace to couter ew tradig strategies With advacemets i techology, it has become possible to surveil at the order level i pre, at, ad post -trade scearios. These act as olie busiess rule-based checks o market participats limits ad have to be applied istataeously Pre-trade surveillace icludes risk ad limit checks, price volatility checks, ad wash trade filters. These are aimed at prevetig trasmissio of o-compliat orders. At-trade surveillace ivolves moitorig patters of order flow ad order etry or deletio at or aroud the best bid, lookig for layerig ad spoofig-type issues where sigificat volume is periodically exposed o oe side of the market while the maipulatig etity executes trades o the other side. Post-trade surveillace ivolves geeratig alerts that idetify levels ad patters of order flow cotrary to the historical tradig behavior i a give istrumet or trader, ad also uusual as compared with defied order-totrade ratios. These order patters are studied o a aalytics platform to idetify the suspicious oes. This categorizatio is based o patters idetified from previous istaces of market abuse programmed i to the system. This is a iterative machie learig process that is key to the aalytics model. The realm of surveillace has wideed with the additio of these scearios, ad this has preseted surveillace officers with more examples of suspicious tradig behaviour. 9

10 6: Emergece of cross-asset class, cross-market surveillace to detect subtle market abuse Surveillace is gettig icreasigly complex, as it is oly cosidered complete whe all fragmeted pieces of iformatio are cosolidated to draw the big picture. May trasactios that are cosidered acceptable i a give market tur out to be potetial market abuse whe aalyzed i cojuctio with some other related activity by a liked etity i aother market or with tradig i some other asset class. For example, a errat trader might resort to markig the close i the equity market to ear high profits i his derivatives positios for the correspodig uderlyig asset. I such a evet, a ulterior motive behid these trades is ot established uless his positios are carefully examied both i equity ad derivatives segmets. Idividually, both these tradig activities may escape the surveillace radar. Uless these trasactios are liked, it passes off as acceptable market behavior. Advaced detectio systems ca provide a volume-aggregated view of trades across veues ad asset classes by all liked etities, which brigs to light ay potetial market abuse. 7: Cross-commuicatio chael surveillace actig as a exteded arm of market surveillace Cross-asset class ad cross-market surveillace are ecessary but ot sufficiet to draw the complete picture. Compliace officers also have to cope with the challege of catchig errat traders who ca easily commuicate through various chaels such as voice, chat, , ad social media. The availability of a large umber of commuicatio chaels is a sigificat cause of cocer for regulators, who have to moitor every suspicious activity. While regulators are fially i charge of market oversight, eve exchages as SROs require surveillace with cosolidated data. All forms of market activities (such as orders, trades, aoucemets, corporate actios) ad commuicatio (icludig voice) eed to be aggregated i the form of a seamlessly itegrated iformatio thread across all commuicatio chaels. This will help regulators detect ay kid of market maipulatio. This will help them draw the big picture of market maipulatios ad will eable holistic surveillace that covers all relevat trade-related data ad commuicatio. 8: Emergece of cloud computig for capital market participats The demad for cloud computig services is o the rise as market participats seek to outsource expesive backoffice fuctios ad also develop the capability to deal with a slew of ew regulatory requiremets. I this ew world of diversified tradig strategies, traders ca o loger rely o legacy ifrastructure to meet their eeds. New techology, icludig the latest avatar of the cloud, Cloud 2.0, leds scalability to traders ifrastructure as per their eeds, without losig out o performace, reliability, ad security. This is the future of tradig. Several large baks such as JP Morga, HSBC, ad Citigroup have lauched cloud-related iitiatives; exchages icludig NASDAQ OMX, Deutsche Bourse, Eurex, ad TWSE are actively rollig out SaaS solutios for tradig, leveragig the beefits of cloud computig, Cloud computig is iducig traders to use the large pools of resources o the iteret. These cloud services are also attractive as they ca be deployed quickly, providig scalability ad helpig reduce IT ad capex ivestmets. This is helpig make the securities market more competitive. However, usig the cloud is also fraught with risks i terms of security, data privacy, compliace, ad availability. So this expasio by market participats through the cloud has to be matched with icreased vigilace ad 10

11 surveillace by market regulators. I this chaged eviromet, regulators have the opportuity to adapt to cloud services to effectively gather iformatio from market ifrastructure etities for surveillace purposes. Key Challeges i Market Surveillace The treds metioed above pose challeges to the trasparecy ad itegrity of the market that eed to be addressed by regulators across the globe. Some of the key challeges are: Techology iduced complexities: High frequecy tradig coupled with low latecy etworks ca expoetially icrease the risks of market abuse, as market participats have the ability to trade umerous products ad eormous volumes i the fractio of a secod. It ca chage tradig behavior to such a extet where ormal detectio algorithms may be of o use, providig erroeous results while a geuie case of market abuse may go uoticed. Limited cross-border regulator jurisdictio: Istrumets traded o domestic exchages of various coutries are sometimes listed or traded o other markets or coected to istrumets listed o foreig markets. Most regulators do ot have the ability or authority to perform cross-border surveillace as it falls outside their jurisdictio. Though the Iteratioal Orgaizatio of Securities Commissios (IOSCO) has a specialized MoU to obtai ad exchage such cross-border iformatio, this has some challeges the formalities ivolved ad the time take to obtai the requested iformatio, ad prevailig laws i the member coutry prohibitig sharig of certai iformatio. Complexity i aggregatig data from multiple tradig veues: Jurisdictios that have multiple listigs of the same istrumet at differet tradig veues face challeges i cosolidatig trade data. This is because differet veues coduct surveillace oly o their ow data. Some regulators do cosolidate order ad trade iformatio across domestic tradig veues to moitor those markets. For example, regulators like IIROC (for equity tradig) ad ASIC collect order ad trade iformatio across all tradig veues operatig i their jurisdictios i real-time. However, most regulators still do ot do so. Also, collectig data from multiple veues to create a meaigful audit trail is possible oly whe the clocks across these veues are sychroized. Lack of a uiform etity idetifier stadard: Differet coutries have differet broker ad cliet idetificatio covetios, ad most of them have some costraits. For example, Caada has a asset class-based idetifier across markets. This meas that if a trader trades i equities ad derivatives, he will do so uder two differet codes, ad ay aggregatio of volume or amout will ot be possible i the absece of a likage betwee the two. May other coutries have veue-based uique idetifiers that will require likages amog broker or cliet IDs across veues to aggregate the tradig activity. Data quality ad format issues: There are disparities i data maitaied across veues. The data eeds to be cleased ad scrubbed to udertake ay meaigful cosolidatio. For example, security data fields may be maitaied with differet specificatios ad busiess rules. This ot oly makes it difficult to cosolidate but also poses data itegrity challeges. Large ustructured data volumes: Today s surveillace systems have to deal with eormous amouts of ustructured data that eed to be deciphered to draw a overall picture. This requires busiess rules-based text miig, patter recogitio, ad setimet aalysis which eed to be re-calibrated at regular itervals to miimize false positive alerts. 11

12 Role of Aalytics i Surveillace Statistical model-based tools equip capital market regulators to moitor, aalyze, ad act o the data based o trade behavior patter recogitio. They also provide regulators with the ability to make iformed decisios i ear real-time basis. For example, a aalytics model for detectig frot ruig ca be desiged to detect a trade patter that suggests that certai trades precedig a large trade may have bee made with prior kowledge of the larger order. These models are capable of ot oly recogizig this patter but also assessig, out of the suspected cases, which are the potetial violatios. This is based o factors such as materiality, umber of istaces, ad likages. These are the flagged as alerts that are further aalyzed by surveillace officers. Aalytics i Trade Surveillace Typically, there are two dimesios to data aalytics that are helpful i detectio of market abuse: Establishig relatioships or likages amog the suspects: This techique is also referred to as lik aalysis. It looks for relatioships betwee two disparate data sources to idetify ay possible likage or coectio based o ay rule-based commoality. For istace, a typical lik aalysis ca be based o demographic parameters. If two etities have the same address, , or phoe umber, it is highly likely that they are liked. Ay surveillace tool should be applied at a aggregated level, with a view of all market activities performed by those liked etities. This could brig to light a lot more violatios that would have otherwise passed off as part of a acceptable tradig patter. Aberratioal performace detectio: This ivolves aalyzig volumes of data to idetify ay aberratios. For istace, the SEC uses a aberratioal performace detectio techique to ot oly idetify uexpected performace but to also esure regulatory compliace by market participats durig the examiatio process. This detectio method ivolves patter aalysis to arrive at whether the tradig behavior i questio is suspicious. The detectio platform, i this case, is equipped with a repository of past suspected tradig patters. This repository expads with the additio of every ew suspect patter (through a machie learig process). A typical, aalytics-based surveillace process flow is depicted i Figure 1. Model triggers Models(such as frot ruig, isider tradig models) sca order ad trade data ad idetify suspicious patters as per the rules ad patters repository Idetificatio of iitial suspects Throw up iitial list of suspects based o suspicious behavior detected usig the criteria stored i the rules egie. Determiatio of fial suspects Score iitial suspects based o criteria such as trade cocetratio i the give security, booked profit value, likages with other etities with dubious history. Geeratio of alerts with related iformatio Sed out alerts (high/ medium / low severity) with fial suspect iformatio such as liked etities, profit booked, price movemet caused, ad couterparties ivolved Aalysis of alerts by surveillace officers Aalyze alerts ad related iformatio to determie whether it is a geuie violatio Ivestigatio, judgmet, ad eforcemet Coduct ivestigatio, file case, arrive at judgmet, ad carry eforcemet actios. Process steps withi aalytics model Processes outside the model Figure 1: Process Flow of Aalytics-based Surveillace for Trade Surveillace 12

13 Effectiveess of Aalytics-Based Models i Trade Surveillace: The Market Experiece While aalytics-based surveillace adds a lot of power ad versatility to market abuse detectio capabilities, there are limitatios to its effectiveess. The implemetatio of a aalytics-based surveillace system ivolves challeges such as: Turaroud time for detectig violatio Surveillace models are oly capable of idetifyig fraud, market abuse, ad aomalous tradig after the icidet has occurred. I itra-day fraud scearios, the earliest they ca idetify the suspects is the ext day. This too requires the presece of historical istaces ad patters i the patters repository of the aalytical model. Limited model itelligece The power of a statistical model to detect market maipulatio depeds o the umber ad quality of live cases available to trai with. The more the cases, the more itelliget the model will become. Similarly, i the case of rulebased procedural models, there are set parameters i the form of thresholds ad cut-offs applied at the model s decisio poits. The models are calibrated based o itesive data aalysis over a large data sample. However, it is ot possible to have zero false positives or a 100 percet hit rate for ay model. Depedecies o etity likages The effectiveess of the model depeds o the accuracy of the sigle view of etity that is created ad used i the model. If this view has qualitative issues, it will limit the accuracy of the results produced by the model eve though it uses powerful algorithms ad statistically advaced equatios. Limited accuracy i setimet aalysis For setimet aalysis of ustructured data feeds, a combiatio of predefied idustry taxoomies ad userdefied taxoomies are required to arrive at the setimet values. However, setimet aalysis performed o the basis of these taxoomies will ot be 100 percet accurate i the classificatio of ustructured data items. Setimet aalysis may also miss subtle messages ad keywords with positive ad egative cootatios. Aalytics-based Surveillace: The Future Give all the chages i the securities tradig space, it is evidet that upgradig techology is essetial to meet the emergig eeds i surveillace. We expect these treds to materialize: Surveillace will coverge further towards advaced (ear) real-time aalytics. It will be able to aalyze trasactios fast eough to match automated tradig with real-time computig ad CEP as critical techology eablers. Surveillace will become more data-itesive ecompassig more types ad volumes of data. The iclusio of real-time ews ad social media data sources will trasform it ito a data itesive exercise relyig o computig power to correlate suspicious tradig with ay price-sesitive ews. The share of use of ustructured data will grow further i future. 13

14 Detectio models will have multi-asset class ad multi-commuicatio chael support. They will have the capability to produce aggregated views of trasactios usig a combiatio of data sources. Pre-built surveillace algorithms will be used for detectio of commo market abuses such as frot ruig, isider tradig, ad markig the close. As a result, the commo type of market abuses are covered without ay cofiguratio effort. Surveillace algorithms will become more extesible ad will support o-the-fly chages. This will help cope with chagig market dyamics (such as icreased volatility levels ad cyclical chages) so that false positives are kept i check. Coclusio The success of ay regulatory framework depeds o how techology- ad data-cetric the regulator s approach is. Such is the ifluece of data ad techology o surveillace that today, the regulators are better off hirig professioals with aalytics ad quatitative skills who ca lear surveillace o the job rather tha the other way roud. Surveillace is set to go through a metamorphic chage. Trade surveillace will soo have two parallel techology streams competig with each other. While oe will facilitate icreasigly complex tradig techologies, the other will help create a more robust framework to detect ay kid of potetial market abuse hidde withi these complex trades. It is a classic case of techology workig both for ad agaist effective surveillace. To realize the full potetial of a effective surveillace system, it has to be supported by a comprehesive case maagemet framework which will efficietly respod to alerts. Regulators ad SROs eed to take a more focused approach to surveillace ad make use of techology wherever possible. This has to be coupled with effective collaboratio amog regulators at a global level to facilitate a better cross-border surveillace regime, makig capital markets a safer place to ivest i. 14

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