Seeing The Storm Ahead Predictive Risk Intelligence
Seeing the storm aheadPredictive Risk Intelligence#DOpsRisk
Seeing the storm ahead Predictive Risk Intelligence How prepared is yourorganization to sense and discoversignificant emerging risks?Predictive Risk Intelligence (PRi)provides you with advance noticeof emerging risks, knowledge ofpotential loss and risk exposures,and increased awareness of theexternal threats to your companyor industry that could affectthe decisions you make foryour organization.02
Seeing the storm ahead Contents Bringing PredictiveRisk Intelligence toyour organizationThis paper introduces the concept of PRi, defines various monitoring methods, anddescribes what you can do to help your organization stay ahead of emerging risks.Executive summary04Risk monitoring strategies06Moving the needle09The role of technology10Industry applicability12Value proposition1303
Seeing the storm ahead Executive summaryExecutive summaryWith limited information, time and tools available, C-suite executives areexpected to manage emerging risks every day.Boards, shareholders, regulators, customers andbusiness partners alike not only request transparency,but also demand that companies demonstrate theability to execute on risk management decisions usingestablished and emerging risk intelligence methods andtechnologies. Stakeholders are expecting risk functionsto provide insights on what went wrong in the past, anaccurate and real-time view of what is happening now,and confidence with information on what could go wrong.Advancements in predictive risk intelligence areincreasingly recognized as cornerstones to effectiverisk monitoring programs, but few organizations candemonstrate the effective adoption to predictive riskintelligence to monitoring emerging risks and trends.Forbes Insights, on behalf of Deloitte ToucheTohmatsu Limited, surveyed more than 300 seniorstakeholders around the world This paper introduces the concept of PRi, definesthree strategies of risk monitoring, and describes howimplementing a PRi program can apply a forward-lookinglens on emerging risks, with information on potentiallosses and trends that could affect your organization.Risk intelligence has evolved beyond the use of riskindicators and management programs to focus oncapturing emerging risks with the use of analyticaltools. Stakeholders expect risk monitoring to provideintelligence that supports strategic decision-makingsuch as investment in products and technologies, newbusiness models, and the development of advanced riskstrategies. These insights can equip business leaderswith the knowledge that drives more informed andcompelling decisions.1. Deloitte Risk Advisory: Taking Aim at value 2017, https://i.forbesimg.com/forbesinsights/deloitte risk and value/Taking Aim at Value.PDF04
Seeing the storm ahead Predictive Risk Intelligence Deloitte insightsRespondents to the Deloitte &Forbes Insight Survey1 reportedthat risk management programshelp them: Increase operational resiliencyPredictive Risk Intelligence in actionPower & Utilities Case StudyMany Utilities companies have animmediate need to use predictive riskanalytics to determine the best way totarget replace and repair priorities fortheir aging infrastructure. Often times,these efforts take many years to complete.PRi applied to aging infrastructureStep 1—Identify internal and external data Realize the value of newtechnologiesStep 2—Data analytics Improve cost effectiveness Accelerate time to marketIdentify &compile dataInternalclientand thirdparty dataHistoricaltemperaturesand sIdentify compileand integrateIdentifyrecordsstatisticallyvalid correlations Optimize return on capital Improve stakeholderconfidenceThe fact that only half of surveyrespondents acknowledged thatthey leverage comprehensiverisk analytics to make strategicbusiness decisions is furtherevidence of the need for amore holistic risk monitoringmethodology.The bottom lineMany organizations monitorrisks without harnessingadvanced analytics methods—powerful and effective tools thatcould help them stay ahead ofcurrent risks and improve howthey manage and respond toemerging risks.Visualization ofkey risk factorsSelectvariablesDevelopheat mapUse modelto calculatecompositerisk scoreTrendanalysisIdentify lowand highrisk areasCrossdatabasepatternrecognitionCleanse andprepare dataStep 3—GeographicInformationSystem (GIS)location ofrisk factorsStep 4—Apply predictive risk analyticsIdentify riskdriversIdentify riskscenariosDetermineprobabilitiesCalculaterisk valuesDefineaccountabilitiesPRiUnderstanding of repair prioritizes for an aging infrastructure.A more resilient, reliablesystem, capable ofdelivering cleanerenergy in less timeFewer “reliability” issuesdue to the complexprocess of rotating innew energy sourcesand retiringinfrastructureImproved ability to meetconsumer demand forhigher quality powerenabled by constructionof a more moderninfrastructure05
Seeing the storm ahead Predictive Risk Intelligence RiskmonitoringstrategiesRisk monitoring occurs throughoutthe risk management lifecycleand can be organized into threecategories: Reactive risk monitoring,integrated risk monitoring, andpredictive risk monitoring.06Reactive risk monitoringReactive risk monitoring is the initialmonitoring mechanism where theorganization tracks and reports lossevents after they happen. Process ownersmay report these incidents as losses occurring duringthe normal course of business or discover incidentssuch as fraud during an audit or the assessment of aparticular business process. Central to this technique isthe ability to respond post-event with a remediation planand the ability to prevent recurrence of similar events inthe future.Integrated risk monitoringIntegrated risk monitoring is riskmonitoring as a discipline, process,or initiative that an organization hasassimilated with overall business strategy.It is the next stage of monitoring that utilizes passiveand active risk, performance, compliance, and controlindicators to objectively report on risk performancethresholds periodically, or in near real-time. The primaryemphasis of this technique is the timely report-out onrisks given identified assessment criteria, the statusof established benchmarks, and interpretation ofrisks deviating from performance standards such asorganizational risk appetite.
Seeing the storm ahead Predictive Risk Intelligence Predictive riskmonitoringPredictive riskmonitoring isa techniquethat helps organizationsdiscover potential risks andthreats, including types of risknot covered by existing riskindicators. Risk monitoringapplies analytics to currentand historical information frominternal and external datasources to identify emergingrisks with a short cycle toimpact. Such a capability helpsmodernize an establishedrisk management frameworkfrom periodic risk reportingto real and near-real time riskreporting. This is PRi.Predictive Risk Intelligence in actionBanking & Securities Case StudyPRi applied to conduct riskStep 1—Describe current bank cultureRegulatoryscrutinyPRi-enabledrisk resChronologyof eventsStep 2—Define unstructuredand structured dataComplexsystemsReputationalriskInternal &external tionContract icsEmployeeconcernsvia emailor chatStep 3—Identifypotentialtrends andcorrelationsStep 4—Understanding of behavioral root causesWeakcontrolsDisparatesubcultures“Growth at allcosts” modelProductlifecycleLack ofaccountabilityPRiUnderstanding of overall firm culture, and visibility intoareas across the business deviating from policyUnderstanding ofbehavioral patternswithin the organizationAbility to re-evaluate thebusiness model andbalance growth withpotential affects to desiredcultural valuesImproved techniquesfor managing humanresources and incentives07
Seeing the storm ahead Predictive Risk Intelligence Figure 1. Operational risk monitoring strategy types: Reactive, Integrated, and PredictiveReactiveIntegratedPredictiveCaptures operational losses andidentifies near-miss historical events.Reactive risk monitoring developsbaseline information that quantifies theimpact of risk event losses, reports onthe status of current risks, and tracks theprogress of ongoing corrective actions.Provides a mechanism to objectivelymeasure risk performance by facilitatingthe development of KRIs, KPIs, KCIs, andassociated threshold measures. Enablesdescription of risk exposure by providinga holistic risk view from across theorganization.Accumulates and aggregates internaland external risk information to providereporting alerts in near real-time.Describes trends, potential emergingrisks, and utilizes reactive and integratedrisk monitoring inputs to generate PRiwith applied analytics and internal andexternal data sources. Generates near real-time risk alerts Capture a comprehensive, 360-degreeview of risksBenefits Synthesizes information to understandpast losses Aggregates information for a view ofoverall impact Provides a baseline for loss forecasting Helps in capital and risk appetiteallocations Provides actionable lessons learned Identifies remediation plans Conducts historical trend analysis Initiates root-cause analysis to preventfuture incidents Reduces subjectivity in riskperformance reporting Helps contain specific loss exposures Reduces potential for overall lossexposure and threshold breaches Predicts potential risks before theymaterialize into threats Identify and monitor emerging risks Recognize emerging risk trends Provides intelligence to augmentbusiness decisions Helps reduce the occurrence of ‘zerotolerance’ incidents Reports on policy and procedurenon-compliance Automates analyses through cognitiveintelligence and applied robotics Generates an aggregated view ofrisk exposure Allows for prompt escalation andremediation through integratedrisk analysis Can be automated Prepares for “long-tail” risk eventsChallenges Uncertainty of emerging risks Loss of opportunities to contain therisk impact Information is dated and difficult tomanipulate No visibility of emerging riskdimensions Unable to provide a comprehensiveunderstanding of external risk Requires reliable and comprehensiverisk and performance data Relies heavily on data governanceand integrity Reliance on data governance andintegrity measures Information is usually dated (byreport) and is often manual, difficultto reproduce, and may be laden withinaccuracies Subject to predictive modeling errors Cannot anticipate potential riskappetite breaches2. Key Risk Indicators (KRIs), Key Performance Indicators (KPIs), and Key Compliance Indicators (KCIs)08 Cannot replace periodic riskassessment process
Seeing the storm ahead Predictive Risk Intelligence Movingthe needle,introducingPRiPRi can help turn risk, controls,and performance information intopreventative and actionable insights,preparing organizations for a refinedunderstanding of emerging risks.4. Develop static and self-learning predictivealgorithmsThrough combined analysis of internal and externalprecursor information, a predictive analytics algorithm (adata-driven statistical model) is selected for fit and appliedto predict or detect the heightened occurrence andlikelihood of a risk event. Data mining and machine learningcapabilities allow these models to be carefully maintainedand/or evolve with ongoing improvements to accuracy.5. Initiate PRi generationRisk governance functions start collecting the baselinedata for each risk category and apply risk predictivealgorithms to generate emerging risk alerts andnotifications. Results are reported and continuouslyevaluated against actual results to determine thesuccess rate of the models and enhance the accuracy ofinsights and outcomes. Formal reports are generated todescribe the emerging risk environment for C-suite andboard decisioning.Figure 2. Internal and external data sourcesExplaining the PRi process:Internal datasourcesExternal datasources1. Define PRi scopeManagement and risk governance teams identifyprioritized risk events to better track and monitor on acontinual basis.Assets and machinerydata (IoT) and machinesensorsSocial media and newssources2. Identify precursors of risk eventsEach risk identified within scope is analyzed to identifyindicators or incidents that precede risk events andprovide reliable indication of an event occurrence. Forexample, product quality failures may result from aninternal process failure or a supplier failure.3. Identify data sourcesEach risk event precursor is prioritized and mapped tointernal and external data sources which can supplythe baseline data required for analysis and predictivemodeling—see Figure 2 for example data sources.Business systems andapplicationsPerformance data fromprocess managementtoolsInternal electroniccommunications [e.g.,Voice over InternetProtocol (VOIP), chat,emails]Past performance dataGovernment alertsand regulatory changenotificationsExternal informationdatabasesOffice of EmergencyManagement (OEM)communicationsProduct changenotifications (PCNs)Industry research reportsand industry loss dataInternal loss data09
Seeing the storm ahead Predictive Risk Intelligence The role of technologyApplying technology across the PRi lifecycle generates faster, morereliable, risk information while creating a risk monitoring process that ismodern, effective, and self-evolving.The following are examples of the important role technology plays at each stage of PRi:Data collectionApplies Robotics Process Automation (RPA)to collect data on a real-time or near realtime basis. RPA is popular for it’s ability tohandle unstructured and nonlinear relationships in data.Data sources may include transactional data, third-partydata, and data collected through character recognitionand natural language processing from websites, blogs,and social media platforms. With more data collectedover time, risk analysis techniques such as regressionand event tree analysis improve in accuracy.10Data standardization and aggregationRPA and cognitive intelligence assimilate,cleanse, standardize, and aggregate variousformats and types of data procured frominternal and external data sources. As large datavolumes are collected by RPA and cognitive techniques,data will need to be prepared for predictive modelingtechniques. This requires persons experienced in thedata management and predictive technologies whoalso understand the complexity of operational risk incontext of the business.
Seeing the storm ahead Predictive Risk Intelligence Predictive riskmodelling andanalysis‘Big data’ predictiveanalytics and algorithms areexecuted by artificial intelligenceand machine learning to putthe collected data and modelsdeveloped to work. Based onthe risks identified, analyticalmodels interpret outcomesand confirm model parametersto generate PRi. Assumptionsand parameters which affectthe analytical models may bechanged or updated based onthe PRi received.Automatedremediation andreportingAutomatesremediation plans throughfront-line applications andbusiness systems to developintegrated, rules-basedreporting capabilities. Riskreporting dashboards andmobile smart technologiesprovide alerts and notificationson threshold breaches, andprescriptive risk mitigationtechniques including correctiveactions, templates, training, andsupport from management.Predictive Risk Intelligence in actionLife Sciences Case StudyPRi applied to insider threat, bribery and corruptionStep 1—Define scope of business relationsStep 2—Trace business activityScope ofinteractionPayments,Gifts andPoliticalPayments totransactions Entertainment, activity andthird partyand dealstravel andcontributionsprovidersexpensesPRi-enabledrisk analyticsAnalysis of voicetranscripts andchat forumsMonitoringof policybreachesDeterminetrends,patterns andrelationshipsData-matchingto identifysmaller partiesand tax havensTraces ofpotentialcollusionDetectpatterns ofsuspicioustransactionsInternalNews, socialpayments andmediacommunicationplatforms,dataand watch listsMine citizenreports, newsmedia, andcensus dataStep 3—Identify redflags, patternsof collusionand falseinformationStep 4—Identify projects and persons “at risk” for collusionConflicts vulnerability election activity inadequaciesPRiUnderstanding of regulatory and reputational risk ofForeign Corrupt Practices Act (FCPA) noncomplianceEnhanced ability toavoid situations wherehigh risk individualsare concernedUnderstanding ofgeo-political climateand countries with ahigh perceptionof corruptionImproved techniquesfor holding businessaccountable for involvingboth internal andexternal relationships11
Seeing the storm ahead Predictive Risk Intelligence PRi industryapplicabilityPRi can help solve complexchallenges across industries:Industrial technology andoperational failuresPRi concepts can be applied toAutomotive, Retail & Distribution, andTechnology companies to provideactionable risk information on critical infrastructurecomponents and products. For example, PRi couldidentify products or supplies that have the potential to bediscontinued for support by equipment manufacturers.PRi can predict the source, type, and frequency of lossevents affecting the availability and performance of theinfrastructure and equipment extending beyond itsrecommended life. Internal and external data sourcesmight include IoT, PCNs, and social media channels.Early warning of potential employeemisconductWithin the Financial Services Industry,there has been no shortage of wellpublicized and damaging misconductscandals over the past decade. Improving conduct is atthe top of everyone’s agenda in banking and securities,and it is well established that innovation is disrupting theindustry to improve regulatory compliance outcomes.3PRi could provide intelligence on changes in employee12behaviors indicative of potential conduct lapses, changesin employee sentiment, and policy breaches that indicatepotential conduct and compliance risks (e.g., insidertrading, data loss, etc.). Internal and external datasources could include chats, emails, VoIP communication,employee financial changes, and behavioral analyticsconducted on social media channels.Alerts and warnings on workplacesafety incidentsPower & Utilities entities could apply PRi toreduce unexpected and adverse outcomeswithin the organization that would improveworkplace safety. Workplace safety places heightenedconcerns about quality, security, privacy, and controlswithin the organization. Nuclear operators, for example,struggle to remain competitive by striking a balanceamong safety, reliability, and economic performance.In this instance, PRi could employ internal loss data,safety breaches, maintenance information, and externaleconomic performance data to identify critical trends forthese entities to implement efficiencies and identify criticalimprovements to policies, programs, and processes.Complex outsourcing and vendormanagementLife Sciences entities interact withhundreds of third parties, includingvendors, contractors, and other serviceproviders. Through layers of outsourcing, contractmanufacturing, and alliances, these organizations mustalso meet strict regulatory requirements and productionschedules. In this instance, PRi can utilize external datasources such as news, government publications, andpublicized incident reports to provide visibility into thirdparty operations that often lack transparency.3. Deloitte Center for Regulatory Strategy: Managing conduct riskAddressing drivers, restoring trust 2017 l-services/articles/managing-conduct-risk.html
Seeing the storm ahead The value proposition pays for itself The valuepropositionpays fori
reliable, risk information while creating a risk monitoring process that is modern, effective, and self-evolving. Data collection Applies Robotics Process Automation (RPA) Seeing the storm ahead Predictive Risk Intelligence Predictive risk data
May 02, 2018 · D. Program Evaluation ͟The organization has provided a description of the framework for how each program will be evaluated. The framework should include all the elements below: ͟The evaluation methods are cost-effective for the organization ͟Quantitative and qualitative data is being collected (at Basics tier, data collection must have begun)
Silat is a combative art of self-defense and survival rooted from Matay archipelago. It was traced at thé early of Langkasuka Kingdom (2nd century CE) till thé reign of Melaka (Malaysia) Sultanate era (13th century). Silat has now evolved to become part of social culture and tradition with thé appearance of a fine physical and spiritual .
On an exceptional basis, Member States may request UNESCO to provide thé candidates with access to thé platform so they can complète thé form by themselves. Thèse requests must be addressed to esd rize unesco. or by 15 A ril 2021 UNESCO will provide thé nomineewith accessto thé platform via their émail address.
̶The leading indicator of employee engagement is based on the quality of the relationship between employee and supervisor Empower your managers! ̶Help them understand the impact on the organization ̶Share important changes, plan options, tasks, and deadlines ̶Provide key messages and talking points ̶Prepare them to answer employee questions
Dr. Sunita Bharatwal** Dr. Pawan Garga*** Abstract Customer satisfaction is derived from thè functionalities and values, a product or Service can provide. The current study aims to segregate thè dimensions of ordine Service quality and gather insights on its impact on web shopping. The trends of purchases have
Chính Văn.- Còn đức Thế tôn thì tuệ giác cực kỳ trong sạch 8: hiện hành bất nhị 9, đạt đến vô tướng 10, đứng vào chỗ đứng của các đức Thế tôn 11, thể hiện tính bình đẳng của các Ngài, đến chỗ không còn chướng ngại 12, giáo pháp không thể khuynh đảo, tâm thức không bị cản trở, cái được
Le genou de Lucy. Odile Jacob. 1999. Coppens Y. Pré-textes. L’homme préhistorique en morceaux. Eds Odile Jacob. 2011. Costentin J., Delaveau P. Café, thé, chocolat, les bons effets sur le cerveau et pour le corps. Editions Odile Jacob. 2010. Crawford M., Marsh D. The driving force : food in human evolution and the future.
Leo Aronson. My hope is that your wonderful capacity for empathy and compassion will help make the world a better place. —E.A. To my family, Deirdre Smith, Christopher Wilson, and Leigh Wilson —T.D.W. To my children, Genevieve and Everett —B.F. To my mentor, colleague, and friend, Dane Archer —R.M.A.
