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© 2016 IBM Corporation
Managing ALM / Liquidity Risk with BigData to Achieve Breakthrough Performance
Leo Armer and Luis Matias
IBM Watson Financial ServicesRisk & Compliance Innovation Forum
London
24 May 2017
Big Data – where should banks be focusing investments now?
Source:http://cib.db.com/docs_new/GTB_Big_Data_Whitepaper_(DB0324)_v2.pdf
AssetLiabilityManagementNewregulationsrequireliquidityplanningandoverallassetandliabilitymanagementfunctionstobefundamentallyrethought.Point-in-timeliquiditypositionspresentlyprovidedbystaticanalysisoffinancialratiosarenolongersufficient;instead,anearerreal-timeviewisbeingrequired.Baselrequirementsforreal-timeoratleastsame-dayviewsofliquidityriskalsocallforcurrentdatafeedsandanalytics.Hence,manyfirmsaremovingtothereal-timemonitoringofcounterpartyexposure,limitsandotherriskcontrols
BanksareprioritizinginvestmentsinBigDatadrivenriskmanagement,becauseextractingnewbusinessinsightswillhelpbanksdirectlymitigatetheprofitabilityimpactsofnewregulations.Givenregulatorydemandsforsame-dayviewsofliquidityrisk,improving assetliabilitymanagementisatoppriorityformanybanks.
Big Data – where should banks be focusing investments now?
1)Source:http://bit.ly/2daHQfg,page8
McKinseyresearchdeterminedthatbankswhichoptimizetheiranalyticaltoolsforassetliabilitymanagementcanhelpthebankimprovereturnonequityby50to400basispoints,whilestillfulfillingallregulatoryrequirements
Collaborateforbalance-sheetoptimization“Givenregulatoryconstraints,balance-sheetcompositionisarguablymoreimportantthaneverinsupportingprofitability.Theriskfunctioncanhelpoptimizetheassetandliabilitycompositionofthebalancesheetbyworkingwithfinanceandstrategyfunctionstoconsidervariouseconomicscenarios,regulation,andstrategicchoices.Howpreparedwouldthebankbe,forexample,iftheloanportfoliowerecontractedorexpanded?Suchanalyses,optimizedwithanalyticaltools,canhelpbanksfindwaystoimprovereturnsonequityby50to400basispoints,whilestillfulfillingallregulatoryrequirements.”1
Big Data Advantages
Client examples of ALM & LR bottlenecks
DataProspectiveEuropeanclientwouldliketoperformIRRBBcalculationson10millionrecords;imposingnodatapoolingasrequirement.
UKClienttakes12+hourstoproducereportsontheirmonthlyALMrunsSouthAfricanclienttakes23+hourstoreportALMmeasures
NorthAmericanclientreportsthatthelargestclusterthebankholdistoruntheirALMsystem
NorthAmericaninvestigatesBigDatatomigratefrommonthlyLCRcalculationstodailyrunsAnalytics
Costs
North American Client POC
I need better BI
I need daily LST
I need more data granularity
I need additional analytics
I need clearer data lineage
IBM has succeeded in helping clients to dramatically improve ALM/LR analysis with Big Data technology. Below are actual results from a client pilot project which analyzed over 15 times more data in less than one-fifth the previous runtime
Data granularity 350K pooled positionsProcessed to reduce granularity from 6M source positions
6M source positions
Interactivity with reporting results
Limited drill-downLiquidity risk reporting generated as part of the overall batch run is limited, where drill down into the underlying data requires other interfaces
Full drill-down to sourceReporting generated supports drill down and slice and dice – all the way down to interpreting Algo One RiskWatch inputs, outputs and log files
“What-if” Analysis Full re-run requiredAdding new “What-if” stress scenarios requires a re-run of the batch process
Incremental updatesNew “What-if” stress scenarios can be computed by the user adding additional scenarios to the scenario table
Runtime 2 hours with 25 cores to generate the cash flows for liquidity stress testing
20 minuteswith 96 cores from the full cluster
IBM Algo One + Big Data technology
IBM Algo One + Traditional Data Management
European Client POC Results
00:00:00
00:07:12
00:14:24
00:21:36
00:28:48
00:36:00
00:43:12
00:50:24
00:57:36
01:04:48
01:12:00
Loading Partitioning Simulation Total
00:46:00
0:…
0:07:01
01:10:31
00:05:0600:08:06 00:06:42
00:19:54
AlgoOne(3Tier) BigData
• NPVBankingbookcalculation• Twoscenarios• Onetimestep
• Actualtestedvolumes:• 1.35millionpositions• 200millionlines
Enhancedwithcognitive
Identifynewopportunitiesforcognitivesolutionstoautomateand
augmentriskmanagementpractices
Accessedatthespeedofbusiness
Extractnewbusinessinsightsfromhighervolumesofmoregranular
data
TrustedriskanalyticsIntegratingmorecomprehensiveandefficientIBMmethodologieswithprovenriskmanagement
approaches
Deliveredtomoreusersinnewways
Usecost-efficientcloudandmobilechannelstooffermoreriskanalyticsservicesthatmeetgrowingbuy-sidedemands,andoffersell-sidefirms
moreoptions
Bankswhichmakeconsistentinvestmentsinanalyticswillbetterunderstandtheirownoperationsandthedemandsoftheirclients,enablingthemtostayaheadoftheircompetitors,andthegrowingdemandsofregulators
Big Data + IBM Analytics = Efficient and trustworthy insights
Big Data and Cognitive ideas for smarter ALM & Liquidity
Pre-PaymentsInsightintoerrors
PeriodAnalysis
Insightfulcomparison
Periodonperiodanalysis…
Explainingchangesinresultsfromoneruntothenextintermsof:-Positiondatachanges-Marketdatachanges-Assumptionchanges
Findingouthowpre-paymentoccurbasedonthehistoricaldata..
Erroranalysisofarun– beingabletoconnectlogdatatoinputs,parameterisationandreporting.
CompareP&Landexplaintheresultsthatyouareactuallyseeing.
Insightfulerroranalysis… ComparisonofprojectionstoP&Lactuals…
Pathofpre-payments…
TheIdea:
CognitiveBenefits:1. Gaininsightsintowhatdriversare
inthemarket2. Accesstoallavailablemarket
sources3. Cutsdownmanualtime
1. Explainingwhereanerrorhascomefrom
2. Suggestedresolutionbaseduponpreviouslearning.
TheIdea:
CognitiveBenefits:
TheIdea:
CognitiveBenefits:1. Automationofamanuallengthy
process.2. Gainsmartinsightintodatafor
humanconsumptionandaction.
TheIdea:
CognitiveBenefits:1. Automationofamanuallengthy
process.2. Gainsmartinsightintodatafor
humanconsumptionandaction.
Short/MediumTermRoadmap:
üRevampinteractionwithbusinessusers
üIRRBB&Liquidityenhancedanalytics
üIntegratedBehavioralModellingwithSPSS
üPowerofcognitiveanalytics
Dec 2016Big data
Technology
Q2 2017New User Interface
Enhanced ALM & Liq Analytics
Watson
ALM/LR– ProductDirection
ALM
Market Risk
FTP
LiquidityRisk
Capital Planning
Budgeting
Hedge Accounting
LookingbeyondLiquidityRisk:
üAssetLiabilityManagement
üLiquidityRisk
üMarketRisk
üCreditSpread&DefaultRisk
üMarginManagement/Budgeting
üLiquidityPlanning- ILAAP
üCapitalPlanning– ICAAP
üHedgeAccounting
üFundsTransferPricing
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Acknowledgements and Disclaimers
Thank you