Banking & Financial Services. White Paper. Managing Enterprise Financial Risk Using Big Data Technologies

Transcription

1 Bakig & Fiacial Services White Paper Maagig Eterprise Fiacial Risk Usig Big Data Techologies

2 About the Author Alok Bisai Alok Bisai is a Solutio Architect with the Tata Cosultacy Services (TCS) BFS Risk Maagemet practice. At the Risk Practice, Alok evaluates the applicatio of Next Geeratio techologies to Risk Maagemet. Over the years, he has gaied experiece i Market Risk Maagemet ad Reportig, ad i buildig large scalable distributed systems. Prior to joiig TCS, Alok was a Market Risk Aalyst for Iterest Rate Products with the Strategic Risk Maagemet divisio of a large Europea bak. Alok previously held the role of a Techical Lead for bakig systems related to Market Risk Maagemet, Equity Research i the U.K.

3 With the advet of Iteret scale processig, computig iovatios like Big Data have had far reachig impact o several busiess processes, ad hold great potetial for may others. Big data techologies offer eterprises the ability to maage a very large amout of data efficietly, ad deploy High Performace Computig ad Complex Evet Processig to ifer isights by aalyzig cotiuous data streams. These advaces hold particularly great potetial i several areas of eterprise wide fiacial risk maagemet, allowig istataeous respose to market chages, ear real time risk pricig, moitorig the impact of stress scearios, maagig the portfolio usig a large battery of live cotrols, frequet computig of the impact of market ad portfolio chages o capital, esurig secure operatios ad more. Although some techologies are still maturig, there are clear opportuities for orgaizatios to ehace their risk maagemet trade-offs. I this paper, we evaluate subprocesses of eterprise wide fiacial risk maagemet, ad idetify areas where Big Data ca deliver the greatest beefits. However, haressig the potetial of these techiques requires a elaborate balacig act to esure smooth performace eve as they support cotiuous metamorphosis of ifrastructure by leveragig a slew of ewly available optios. We also aalyze the curret Eterprise Fiacial Risk Maagemet ifrastructure ladscape, ispect how these systems deal with ew challeges ad, the potetial impact of Big Data techologies.

4 Cotets Itroductio 5 Risk Maagemet Life Cycle 6 Traditioal Risk Systems Architecture 7 Limitatios ad Deficiecies of the Existig Architecture 9 Applicatio of Big Data Techologies 9 Beefits from Big Data Applicatio to Risk Maagemet 10 Typical Busiess Use Cases 11 Coclusio 11

5 Itroductio Fiacial firms deal with a plethora of risks which ca impact the profitability of their busiess very quickly. These risks, classified as market or liquidity risks, credit risks ad operatioal risks, ca origiate from cotiuously evolvig market coditios, chage i the creditworthiess of their cotractual couterparties, ad systemic or huma errors i busiess operatios respectively. Large baks sped a lot of time ad effort i maagig these factors due to their potetial for curret ad future impact. Typically, a commo set of busiess processes is used to maage these risks systematically. Paradigm Shift Risk Systems Costraits Scalability of Processig What do Risk Maagers wat? Big Data Volume of Data Performace of Aalysis Regulatory Compliace => Large Body of Reports Reliable & Accurate Results => Elaborate Testig Variety of Data Competitive Edge => Shorter Timelies, Near Real Time Aalysis Deeper Isights => Large Volume/Variety of Data & Aalysis Source: TCS Research Figure 1: Relevace of Big Data i Risk Maagemet Improvemets i techology offer the much eeded power ad capability to address the challeges i Risk Maagemet. Risk Maagers are ot oly resposible for optimally ad reliably cotrollig the risk that frot office takes by usig deeper isights, but also esurig that the bak complies with the regulatory processes i a timely ad accurate fashio. They are ofte cofroted with balacig the eed for more accurate results, deeper isights ad better regulatory compliace i lesser time ad cost agaist complexities like the eed to scale up or scale dow processig eeds to cruch very large volumes of varied data. This is illustrated i Figure 1. The applicatio of Big Data techologies ca successfully address the curret systemic restraits that risk maagers deal with, resultig i a paradigm shift i this balacig act. The ext sectios examie how Big Data techologies ca impact ad ehace processes withi fiacial risk maagemet. 5

6 Risk Maagemet Life Cycle There are four mai busiess process areas i fiacial risk maagemet - risk idetificatio, risk assessmet, risk cotrol, ad risk reportig ad review. I risk idetificatio, risk factors that impact product positios are idetified, associated risks are modeled ad measures to capture a loss estimate at the required cofidece limit are defied. This process idetifies the hedges to mitigate the risks ad determies the risk parameters to calibrate models for specific busiess coditios. This process is performed periodically ad subsequet chages are oly icremetal ad less dyamic. There is thus scope for sigificat improvemets i this area that ca allow for capturig busiess dyamics o a ear real time basis with much greater detail ad accuracy. However, traditioal static risk systems aloe may ot be able to deliver such capabilities as this requires quick respose usig advaced predictive aalytics based o a large variety ad volume of iteral ad exteral data. Risk assessmet is geared towards the day to day updatig of portfolios i the curret busiess eviromet. It ivolves ruig routie umber-cruchig processes usig models ad parameters defied i the risk idetificatio process. These processes are automated i a umber of baks; however, there is scope to improve scalability, ad performace to better cope with exceptios ad errors. Implemetatio of these processes will probably be the first small step i the Big Data program of a bak. Risk Idetificatio Risk Assessmet Risk Cotrol Risk Reportig & Review Risk Factor Idetificatio Sesitivities Calculatio Sesitivity & Risk Limit Aalysis Regulatory Risk Reportig Products - Risk Modelig & Validatio Scearios Calculatio Sceario Aalysis Ecoomic Capital Reportig Risk Measures Modelig Risk Measures Calculatio Sub-Portfolio Limits Aalysis (e.g. Coutry) MIS Risk Reportig Hedgig Strategy Idetificatio P&L Attributio Correlatio Risk Aalysis P&L Historical Aalysis P&L Historical Aalysis Risk Based P&L Calculatio Specific Risk Aalysis Back Testig Low Medium High Source: TCS Research Figure 2: Risk Maagemet Value Chai (Key Processes) Big Data Heat Map 6

7 The Risk Cotrol busiess process area is cocered with computig ad aalyzig the impact of risk ad cotrollig its effect to miimize the chaces of a loss. Typically, these processes ivolve defiig risk thresholds ad aalyzig the chages i the utilizatio of these limits o a daily basis to esure that busiesses do ot take excessive risk ad are withi the set thresholds. Such aalysis also icludes estimatig the impact of various busiess eviromet scearios. Also, these aalyses may be ru o the local subset of the etire portfolio by partitioig it by coutry, sector or idustry, market, couterparty, currecy, ad busiess orgaizatios, amog other divisios. Such aalysis ivolves maually iterpretig results from a automated process, ad subsequetly divig deeper ito specific areas for detailed scrutiy. Further aalysis is ofte triggered maually ad ca icur a lot of wait time for risk aalysts who iterpret the results. The Risk Reportig ad Review busiess process area is most critical for esurig timeliess of reports. The reports are i a set format o a day to day basis, ad ru i batch processig mode triggered after data is approved by risk aalysts durig Risk Cotrol. Periodic processig is coducted for large sets of data across multiple busiess dates either for back testig, P&L historical aalysis, ad other processes required for chage cotrol of risk related busiess processes. A large team of aalysts esures that the data is accurate ad the aalysis is complete before the batch processes are ru. Also, ruig the periodic review processes for chage cotrol requires maual itervetio for iterpretig the results for the batch process. Such processes will deliver large beefits to the risk departmet if successfully implemeted i a Big Data processig eviromet. However, they require slightly more mature system with the capability to maage the data carefully for size ad cotext ad also deal with a wide variety of structured data to ru automatically. Traditioal Risk Systems Architecture A typical risk maagemet system cotais large data repositories icludig: Daily sesitivity ad sceario data based o which the portfolio is re-priced to reflect the impact of various risk factors Static Data that icludes trade details, busiess orgaizatio ad other hierarchical iformatio Referece Data that icludes historical prices, risk parameters, etc. Calculatio Results Repository that stores the history of risk aalysis related iformatio Traditioal risk systems use large extract-trasform-load (ETL) sub-systems for loadig data ito these data warehouses ad employ a battery of data processig rules to clea, stadardize, validate ad approve the required data. Such a eviromet icludes umerous automatic ad maually triggered workflows to move iformatio from oe stage to the ext. Large distributed clusters employ modular applicatios to determie various risk measures ad other report processig ifrastructure. These applicatios may employ load balacig for report processig ad grid compute ifrastructure for some calculatios ivolvig large amouts of simulatio. These applicatios are built i-house, ad developed ad ehaced by applyig additioal busiess processig logic over a period of time. 7

8 GUI applicatios ad other ed user compute ifrastructure help i maitaiig, queryig ad cosumig data. Figure 3 depicts a typical applicatio ifrastructure layout for risk maagemet. Frot Office Systems Risk Data Data Warehouse GUI System/ Reportig Portal Static Data ETL Subsystems Risk Reportig Ifrastructure Risk Results Repository Third Party Providers Referece Data Risk Aggregatio Egie Risk Calculatio Egie Mote Carlo Grid Source: Iteral Research Figure 3: Applicatio Ifrastructure Layout for Risk Maagemet Limitatios ad Deficiecies of the Existig Architecture The biggest limitatio of the existig architecture is that it caot scale efficietly ad easily to meet the icreasig requiremets of regulatio, strategy ad other busiess eeds. This is primarily because of the bottleecks i data warehouses, lack of availability of processig odes, delays i calculatig risk measures ad more. For istace, batch reportig processes routiely exted ito overight jobs ad high volume jobs such as those eeded for performig back-testig or parameter determiatio stretch ito weeks. Also, limitatios exist i the capability to process a large volume of varied data efficietly ad perform quick aalysis, especially whe tryig to drill dow ito importat chages i overight positio risk. The existig systems caot hadle semi-structured or ustructured data or icorporate ew iovative ways to idetify, assess or cotrol risk. 8

9 Applicatio of Big Data Techologies Visualizatio Data Aalytics Risk Cotrol Risk Idetificatio Data Maagemet Risk Reportig & Review Map/Reduce Framework Compoets Risk Assessmet Fuctios Scalability & Performace Structured Data Ustructured Data Predictive Aalytics Figure 4: Big Data Maturity Grid Source: Iteral Research The grid i Figure 4 shows applicability of Risk Processes (plotted i dark blue rouded rectagles) agaist the maturity of Big Data Systems i X & Y axis alog Fuctios ad Compoets respectively. The higher ad farther a Risk Process is placed i this grid, the more sophisticated the ature of Big Data systems required for its use. Also, the size of the Risk Process (i dark blue rouded rectagle) idicates the amout of risk aalyst time typically spet i that area. Big Data systems iclude iovatios to efficietly ru distributed processig o a very large scale. The ew techiques ca process structured data from a variety of traditioal relatioal database maagemet systems, ad parse ad digest iformatio from aotated (semi-structured) or eve plai text (ustructured) data. Most mature Big Data systems icorporate all these ad offer aalytics to help make statistical ad other umerical predictios. Also, from a Big Data compoet poit of view there are map/reduce egies which help schedule jobs across a large bak of processig odes. More mature systems help automatically maage space, time ad itelligetly schedule processig jobs. Further alog the compoet value chai are processes that provide a variety of data aalytics ad help visualize them coveietly i a cosole. While these processes use the power of the previous Big Data compoets, they provide the ability to ru higher value fuctios. 9

10 I the existig architecture, the largest impact i terms of user time would come from Risk Reportig ad Review. Several reportig processes ca be made more automatic if ot highly efficiet by utilizig a distributed cache, ad automatic recalculatio of risk measures for updated iformatio to make risk reportig faster, agile ad more comprehesive. This system would be able to process a variety of structured iformatio, itelligetly maage space, time ad processig resources. However, whe more aalytics capability ad ability to process ustructured data is also available, applicatio of Big Data techologies i the Risk Cotrol process area is eabled. I such a sceario, risk aalysis would be performed automatically o the occurrece of a ew market evet or durig periods of volatility, ad the ivestmet portfolio would be updated automatically to reflect the impact of such evets. Most processes i the Risk Assessmet area such as calculatio of sesitivities ad scearios are already ru i a automated distributed process. However, there are advatages of scalig up such processig quickly i respose to a chage i portfolio or, o itroductio of ew calculatios, whe this is doe i Big Data architecture. But eve the most mature Big Data system would impact the Risk Idetificatio processes by allowig risk aalysts to automatically moitor multiple scearios, ad eablig the system to look for ew or ukow scearios that will impact the portfolio. Usig visualizatio ad predictive aalytics will ot oly ease modelig complexities, but offer the required capabilities to model risk factors ad risk measures for may ew parameters that were previously iaccessible. Beefits from Big Data Applicatio to Risk Maagemet Some importat beefits from applyig Big Data architecture to Risk Maagemet would iclude: Ruig scalable, distributed & parallel processig jobs. Hadlig a very large volume of data ad multiple aalysis fuctios o several dimesios of the data. Ruig a lot of calculatios at a much faster speed. Ability to re-ru large aggregate aalysis o chages to smaller subset of data. Hadlig a vast variety of data from iteral ad exteral sources which may be structured or ustructured, i a meaigful way Quickly ru drill dow aalysis visually to see where the drivers or chages to the risk are comig from. 10

11 Typical Busiess Use Cases Some importat busiess scearios that may beefit from Big Data architecture iclude: Aalysis for ew reportig requiremets (e.g., liquidity risk requiremets, cost of fudig review) Aalysis of risk parameters ad risk methods Detailed historic ad tred aalysis for profitability, risk takig ad other busiess iformatio Calculatig retur o ivestmet, aalysis for busiess lies, regioally or for sectors Customer accout profitability aalysis Tradig ad other employees' busiess performace aalysis ad peer compariso Macro-ecoomic evaluatios of busiess portfolio agaist market ad ecoomic data Comparig iteral performace with idustry or observed idex bechmarks, etc. Coclusio There is cosiderable scope ad opportuity to leverage Big Data techologies to improve fiacial risk maagemet systems, ad derive greater busiess beefits. We examied various aspects of processig for the risk maagemet busiess, ad how they will be impacted by Big Data techologies. Although some of these techiques have bee implemeted i bits ad pieces i various systems used by baks, the wholesale applicatio of a itegrated distributed Big Data architecture will ope aveues for may ew ad iterestig use cases for them, ad automate existig systems to make them more efficiet. 11

12 About TCS' Bakig ad Fiacial Services Busiess Uit With over four decades of experiece workig with the world's leadig baks ad fiacial istitutios, TCS offers a comprehesive portfolio of domai-focused processes, frameworks, ad solutios that empower orgaizatios to respod to market chages quickly, maage customer relatioships profitably, ad stay ahead of competitio. Our offerigs combie customizable solutio accelerators with expertise gaied from egagig with global baks, regulatory ad developmet istitutios, ad diversified ad specialty fiacial istitutios. TCS raked #2 i the 2013 FiTech 100 rakigs of top global techology providers to the fiacial services idustry ad couts 12 of the top 20 global fiacial istitutios amog its clietele. From retail ad corporate bakig, capital markets, market ifrastructure, ad cards, to risk maagemet ad treasury, TCS helps orgaizatios achieve key operatioal ad strategic objectives. Cotact For more iformatio about TCS Bakig ad Fiacial Services Uit, visit: bfs.marketig@tcs.com Subscribe to TCS White Papers TCS.com RSS: Feedburer: About Tata Cosultacy Services (TCS) Tata Cosultacy Services is a IT services, cosultig ad busiess solutios orgaizatio that delivers real results to global busiess, esurig a level of certaity o other firm ca match. TCS offers a cosultig-led, itegrated portfolio of IT ad IT-eabled ifrastructure, egieerig ad TM assurace services. This is delivered through its uique Global Network Delivery Model, recogized as the bechmark of excellece i software developmet. A part of the Tata Group, Idia s largest idustrial coglomerate, TCS has a global footprit ad is listed o the Natioal Stock Exchage ad Bombay Stock Exchage i Idia. For more iformatio, visit us at IT Services Busiess Solutios Cosultig All cotet / iformatio preset here is the exclusive property of Tata Cosultacy Services Limited (TCS). The cotet / iformatio cotaied here is correct at the time of publishig. No material from here may be copied, modified, reproduced, republished, uploaded, trasmitted, posted or distributed i ay form without prior writte permissio from TCS. Uauthorized use of the cotet / iformatio appearig here may violate copyright, trademark ad other applicable laws, ad could result i crimial or civil pealties. Copyright 2014 Tata Cosultacy Services Limited TCS Desig Services I M I 07 I 14