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RARC / CarLab


         Research Center Presentation

            Miklos A. Vasarhelyi
KPMG Professor of AIS, Rutgers Business School
         Director of RARC / CarLab
Outline
•   Structure
•   Funding
•   Advisory Board
•   Continuous Assurance
    – Continuous data assurance
    – Continuous control monitoring
    – Continuous risk management and assessment
• Evolving towards the future


                                                  2
Funding
• Very limited
• About 250K a year
• Currently
  –   KPMG
  –   Itau Unibanco
  –   Annual conference
  –   Small donations




                          3
Advisory Board
•   Mr. Robert Elliott –Advisory Board Chairman - Partner (Retired) KPMG
•   Alan W. Anderson, CPA - ACCOUNTability Plus, LLC - President
•   Dr. Gerard Brennan, CAFÉ - Risk & Internal Control Officer – USA Siemens Corp.
•   Stuart V. M. Campbell - KPMG, National Managing Partner - Assurance and
    Advisory Services Center - KPMG LLP
•   Michael P. Cangemi, CPA, CISA - President & CEO - Cangemi Company LLC
•   Saty Ghosh - Senior Vice President – Internal Audit - IDT Corporation
•   Sam Parker - Director, Management Systems Division - AT&T
•   Amy Pawlicki - Director – Business Reporting Assurance & Advisory Services and
    XBRL - AICPA
•   Bill Titera - Partner - Ernst & Young - Chairman – Assurance Services Executive
    Committee
•   Lily Shue - Director - WISS Business Solutions LLC
•   Joe Steakley - Senior VP Internal Audit - HCA – Hospital Corporation of America
•   John Verver - Vice President, Services and Product Strategy - ACL Services. Inc.

                                                                                4
The CarLab Research Efforts
• Siemens
   – Continuous control monitoring (CCM)
   – Audit automation
   – Authorization and control structure project
• Continuity equations at HCA
• Itau Unibanco
   – Branch monitoring and analytics
   – Transitory Accounts
   – Product sales monitoring
• Insurance company
   – Forensics as CA -> the wires project
   – Claims
                                                   55
6
The CarLab Research Efforts (2)
• P&G
   – KPI project
   – Order to cash project -> selective automation
   – Vendor / payments project
• KPMG projects
   – Technology adoption at CPA firms
   – Technology adoption at IA departments
   – The future of audit

   – Rapid Prototyping Environment for data research (RPE)
   – Dashboard / visualization / Advanced Analytics Representation
     (DVAA)

• Telecom and Real Estate Co (TRE)
   – Duplicate Payments detection                                7
Carlab Research Efforts (3)
• AICPA – ASEC
  – Common Data cube
      • Visionary effort for providing a common auditor data platform and a
        multiplicity of apps
      • Partnership with many vendors
  – ERM assurance
      • Assurance on the processes of risk management
  – Continuous audit
      • Rewrite of red book and conceptualization and principles and criteria for
        continuous audit




                                                                                    8
RARC and Our Teaching Mission
• The VPA effort
   – Automatic course recording experiment just started
   – A vision of free unencumbered educational stream for less
     developed nations
• Clickers
   – Experimentation with response system uses in different contexts




                                                                  9
Out of the box
• Standards Formalization: issuing standards in autocode




                                                           10
AAA Impact of Research Taskforce 2009

• Perhaps the most important contribution of
  accounting information systems research to practice
  in the auditing and assurance domain is in
  continuous assurance. The work of Vasarhelyi and
  his colleagues on continuous assurance
  demonstrates the application of strong theoretical
  foundations to the practical problems of the auditor;
  in this case the internal auditor.

                                                     11
Innovations in Continuous Auditing
• Modeling: continuity equations (HCA)
• Discrepancy detection -> multidimensional clustering
• Process mining -> at international bank in Holland
• Remote audit conceptualization – Siemens, P&G, Itau
  Unibanco
• Automatic taxonomy creation
• Multiple multivariate applications




                                                         12
Monitoring and Control: 5 levels of activity
Continuous Reporting
Continuous Assurance
          Transaction assurance, Estimate assurance, Compliance assurance,
          Judgment evaluation
Analytic monitoring level
    KPIs: Marketing/Sales Ratio                                      •Drill Down
           Inventory turnover                                              •History
           Intra-company transfers                                         •Distribution
Relationship level
   Sales change = Incremental Marketing cost * 2.7 +- 12%            •Drill Down
   E-Care queries = number of sales * 4.1                                  •History
   Delay relationships                                                     •Distribution
Data level
                     •Product      •Collection
    •Investment      detail        •Aging of       •Inventory        •Drill Down
    •Regions         •Regions      receivables     •Distribution           •History
    •Clients         •Clients      •Clients        •Ownership              •Distribution
    •Dynamics        •Dynamics     •Dynamics       •Dynamics
Structural level
                                     Cash          Inventory



          Marketing     Sales
                                       A/R        Bad Debts    Provisioning   E-Care 13
                                                                                      5
Continuous Assurance




                       14
An Evolving Continuous Audit
  Framework
                            Continuous Risk
                            Monitoring and
•Automation                  Assessment
•Sensoring     Continuous                 Continuous
                 Data                      Control
•ERP
                 Audit                    Monitoring
•E-Commerce



                        Continuous
                          Audit

                                                       15
Continuous Data Assurance




                            16
Alerts,
                                        Causal linkage,
                                         Confirmatory
            Follow the life-
CPAS effort   cycle of the
                                          extranets,
                      Advanced             CRMA
              application,
                  analytics linked to
• This methodology will change
          different timing for
                      processes,
            different cycles
                    data rich, new
  the nature of evidence,    Audit by exception
                       methods
  timing,
  procedures and
  effort involved in audit work.




                                                          17
Continuity Equations / Long Distance Billing
reservations
                                                           flights



                                                           Billing                                 colections




R e c e iv in g C a ll    C re a tin g d a ta s e ts   S p littin g c a ll   P o s tin g f ro m o n e        R a tin g e a c h
d e ta il d a ta fro m     o n e -to -o n e            d e ta il in to       b ille r file to a c c o u n ts B illa b le
in d e p e n d e n t       m a n y -to -m a n y        file s to b e         in s e v e ra l b illin g       C u s to m e r
te le p h o n e            o n e -to -m a n y '        p o s te d to         c y c le s
c o m p a n ie s in                                    d iffe re n t
m a g . ta p e s                                       b ille rs


         1                       2                             3                          4                     5


                                                                                                                                 18
CPAS OVERVIEW
                                                                          System

                                                                               System Operational Reports

                          Workstation

                                               DF-level 2
                                                            Operational              Operation
                                                              Report                    al
                                                                                      Report


DF-level 1                DF-level 1           DF-level 1
                                                                     Operational
                                                                       Report



                                                                           Filter



                                               Alarm
             DF-level 0
                          Data Flow Diagrams
                                                                          Database
     Reports                 Analytics            Metrics
Lessons from HCA project
• Intricate processes can and must be monitored
• This may be done at the transaction levels, in
  addition to more aggregate levels
• Models are necessary that are adaptive and can
  react to current circumstances
• Errors may be automatically corrected
• A tool may be derived from the performed work
  that could be superior to existing tools


                                                   20
Metlife
• Data stream of over 200K wire transfers
• Data only currently available for the wires and the records
  possess little information
• Little context knowledge of the major feeding streams
• No fraud training data available
• Worked during the audit supplementing the audit team work
• Developed a series of data filters relating to specific
  conditions and trends
• Working on an aggregate weighting model
• Need in the field verification of picked data


                                                                21
Metlife (Project 2-3)
• Usage of clustering techniques to extract
  aberrations in data in parallel to the above
  discussed effort

• Usage of clustering techniques in the evaluation
  of exceptions in life insurance claims




                                                 22
Visualizing combination
of attributes, we will be
able to see similarity
and differences among
claims
60.00%                                            Cluster by Insured information

 50.00%


                                                                                                                                 POPULATION
 40.00%
                                                                                                                                 663
                                                                                                                                 14000
 30.00%
                                                                                                                                 15500
                                                                                                                                 31000
 20.00%                                                                                                                          106103
                                                                                                                                 1023000
 10.00%


  0.00%
              cluster0     cluster1        cluster2       cluster3        cluster5       cluster6       cluster7      cluster8

    Population               22.60%       47.05%       7.21%      17.19%        3.43%        0.64%       1.55%       0.33%
    663                      28.89%       44.02%       3.84%      20.54%        1.35%        0.45%       0.68%       0.23%
    14000                    19.02%       50.92%       1.23%      26.99%        1.23%        0.00%       0.00%       0.61%
    15500                    14.40%       56.27%       2.11%      23.12%        2.38%        0.13%       0.53%       1.06%
    31000                    56.22%       19.40%       3.98%      16.92%        1.99%        0.00%       1.00%       0.50%
    106103                   43.27%       35.67%       0.00%      20.47%        0.58%        0.00%       0.00%       0.00%
    1023000                   4.49%       52.65%       0.82%      39.59%        2.45%        0.00%       0.00%       0.00%
                         Cluster 0    Cluster 1    Cluster2    Cluster3    Cluster5     Cluster6     Cluster7    Cluster8

We can cluster claims using different groups of attributes and flag the claims
from specific groups in specific clusters.
Several clustering of different groups of attributes can make up the score.
P&G (work with the audit innovation team)

• KPI projects
• Automating order to cash
• Vendor files / duplicate
  payments
• Risk dashboard
                                            25
KPI project
• Company has facilities in over 160 countries
• Some facilities are manufacturing, some are pure distribution
  and sales
• Content is local and world sourced
• Substantive part of the work is building models for inventory
  and sales flow and trying to understand / model the level and
  flow variables
• The objective is to detect out of the normal events both of
  business and exception nature (errors and fraud)
• There are 4 large ERPs feeding the data / data is extracted in
  ACL and modeled in SAS
• 16 different models have been developed and are being
  tested                                                        26
Order to Cash Project -> Selective
Automation
• This project aims to selectively automate parts of the audit
  using order to cash as the context
   –   Audit action sheets
   –   Taxonomization of protocols
   –   Change of nature of evidence
   –   Classification of automation level
        •   Manual
        •   Deterministic
        •   Table comparison
        •   Historical / stochastic
   – Architecture of the Structure
   – Prototyping of selected models
                                                                 27
Continuous Control Monitoring




                                28
Siemens Projects
• Focused on audit automation
  – First project looked at automating CCM
    in SAP
  – Second project focused on a wider
    scope of automation
  – A third project would think about
    reengineering the audit action sheets
  – The fourth project aims at formalizing
    SOD, activities, and control structures
                                              29
The Siemens Project Learnings
• ERPs are very opaque
• Ratings schema are used and desirable
• 20-40% of the controls may be deterministically
  monitored
• Maybe other 20-40% may be convertible to be
  monitorable
• New form of alarm evidence that we do not know
  how to deal
• Continuous risk management and assessment
  needed for weighting evidence and choice of
  procedures
                                                    30
Continuous Risk Monitoring and Assessment



    Assurance on Risk Management



                                       31
• CRMA is a real time based integrated risk assessment
  approach, aggregating data across different functional tasks
  in organization to assess risk exposure, providing reasonable
  assurance on firm’s risk assessment.
• CRMA continuously computes key risk indicators (KRIs) with
  firm’s cross-functional data from its key business processes,
  as well as from external sources and validates them against
  whether they are linked to the firm’s risk exposure.



                                                                  33
•   KRI provides early warning systems to track the level of
    risk in the organization
•   KRI can be identified through analysis of key business
    activities
    – 6 steps for KRI identification (Scandizzo, 2005)
•   Well identified and computed KRIs provide a reliable
    basis for computing the riskiness of firm for specific risk,
    such operational risk, liquidity risk, as well as the overall
    riskiness of firm.
    – f(KRI(i),KRI(ii),…KRI(n))= Risk exposure
    – External risk factors may be mapped manually into the
      computation of KRIs and risk exposure.



                                                                    34
CRMA
                                                    KRI
      CDA                               CMM
                           f(KRIs) =
                        Risk exposure               KRI

             Enterprise System



Accounting      Process 1               R&D


HR              Process 2               Marketing

Purchasin       Process 3               Warehouse
g

                                                          36
The CarLab Research Efforts
• Siemens
   – Continuous control monitoring (CCM)
   – Audit automation
   – Authorization and control structure project
• Continuity equations at HCA
• Itau Unibanco
   – Branch monitoring and analytics
   – Transitory Accounts
   – Product sales monitoring
• Insurance company
   – Forensics as CA -> the wires project
   – Claims


                                                   3737
Process Mining


         Mieke Jens (Hasselt University)
             Michael Alles (Rutgers Univ.)




                                             38
What is Process Mining of Event Logs?
• The basic idea of process mining is to extract knowledge from
  event logs recorded by an information system. Until recently,
  the information in these event logs was rarely used to analyze
  the underlying processes. Process mining aims at improving
  this by providing techniques and tools for discovering process,
  control, data, organizational, and social structures from event
  logs. Fuelled by the omnipresence of event logs in
  transactional information systems… process mining has
  become a vivid research area.
•   http://is.tm.tue.nl/staff/wvdaalst/BPMcenter/process%20mining.htm




                                                                        3939
An Example of An Event Log of an Invoice
         Invoice number 003                        Invoice number 003




                                                                                                Input data
         Supplier: AT&T                            Supplier: AT&T
         Posting date: Feb 10th 2010               Posting date: Feb 10th 2010
         120 USD                                   120 USD
         Description: internet services Jan 2010   Description: internet services Jan 2010
         ‘Signature of John’                       ‘Signature of John’
         ‘Signature of Pete’                       ‘Signature of Pete’

                                                                    PLUS

                                                   - ‘Create Invoice’
                                                      Timestamp: Feb 12th 2010; 08:23 AM
                                                      Originator: Mike
                                                      Fields: supplier: AT&T, posting date:
                                                      02-10-2010, value: 100 USD,
                                                      Description: internet services Jan 2010




                                                                                                Meta-data
                                                   - ‘Change’
                                                      Timestamp: Feb 12th 2010; 08:43 AM
                                                      Originator: John
                                                      Field changed: Value
                                                      Value old: 100 USD
                                                      Value new: 120 USD

                                                   -‘Sign’
                                                     Timestamp: Feb 12th 2010; 08:44 AM
                                                     Originator: John




       Figure1: Visualization of Input Data and
       Event Log Data of an Invoice                                                                          4040
Research Team (all in AIS department
    but multidisciplinary background)
•    Miklos A. Vasarhelyi, Prof. II (Continuous Auditing)
•    Alex Kogan, Prof. – (Computer Scientist)
•    Michael Alles, Professor –( Economics)
•    Helen Brown (Auditing)
•    Don Warren, Professor –( Audit Practice)
•    Trevor Stewart, Professor (Retired D&T partner)
•    Silvia Romero – faculty Montclair University
•    Yong Bum Kim – PhD Student (Statistics)
•    Daewoon Moon – PhD Student (Risk Management)
•    Ryan Teeter – PhD Student (Computer Science)
•    Qi, Liu– PhD Student
•    Hussein Issa– PhD Student
•    David Chen– PhD Student (Knowledge Engineering)
•    Danielle Lombardi - PhD student
•    JP Krahel - PhD student
•    Karina Chandia - PhD Student
                                                            41
Rapid Prototyping Environment for Data
Research
RPE Overview
•Client Challenge: External audit work and audit-related advisory services progressively
incorporate advanced analytics and knowledge bases.
•Area of Research: Evolved application of analytic methods
•Potential Use: Building on CAR-Lab experience from client projects (general ledger,
transitory accounts, claims, wires, audit automation, loans, credit, with representative
datasets for experimentation and demonstration) and employing existing public domain
packages (such as WEKA or D), perform the following:
     –   Create masked datasets for demonstration of different techniques.
     –   Focus on technologies to deal with real-time and archival data feeds, in particular
         screening and automatic pattern recognition. A wide set of management problems falls
         into this domain which is being changed by the constant updating of data.
     –   The software tools, data, and models available will allow for testing the adequacy and
         demonstration of capabilities of advanced analytics.
•Example of Output: Use continuity equations, automatic error correction, delayed time
contingent equations, and other linear methods to represent HCA transaction flow

*See CAR-Lab-related qualifications and experiences on following pages
RPE New Analytic Methods for Data Research
Develop applications for continuous transaction monitoring, continuous
controls monitoring, and continuous risk assessment/monitoring using:

    – Continuity equations                    – Rule based systems
        • Predictive modeling                    • Pure rules
        • Automatic data correction              • Hybrid
        • Time lag / time series / cross-        • Forensic ex-ante settings
          sectional modeling                  – Clustering technology
    – Machine learning                           • Fraud identification
        • Trained / untrained                    • Identification of related
        • Neural networks                           processes in a BI context
    – Genetic algorithms                         • Graphic demonstration / item
    – Logic / probit / and other                    identification / semantic domain
      regression techniques                         usage demonstration
Dashboard / Visualization / Advanced
Analytic Representation
DVAA Overview
•Client Challenge: Develop dashboards for risk monitoring / develop other visualization
prototypes for continuous assurance (CA), continuous monitoring (CM), continuous risk
measurement and assessment (CRMA), and continuous controls monitoring (CCM)
•Area of Research: Risk and transaction monitoring and human-computer interaction
•Potential Use:
          • Creation of KPI- and KRI-based views of the world in a continuous audit environment
            including status-quo, projection, and interpretations
          • Management routines / internal audit response to continuous assurance alerts /
            alarm events
          • KPIs and KRIs for selected major business processes and/or regulated industries
            such as financial services or energy (trading businesses)
DVAA Overview (2)
•Examples of Output:
         • Tied to the management of unobservable controls at a large ERP in an industrial setting ->
           special artifacts for the representation of different controls, alarms for control deficiencies,
           indices of control coverage, other graphics action objects
         • Aimed at controlling the different dimensions of risk measurement encompassing direct
           links to quantitative models as well as arbitrary qualitative items
         • Tied to the management of large volumes of data in the understanding, management and
           transaction investigation in a transitory account setting within a financial institution
DVAA At-a-Glance
•Estimated Hours to Accomplish Project:                                        730 Hours
•Expected completion date:                                                     April 2011
•Key Activities:
a)Design scenarios of audit support
b)Survey for existing products and research (EWP, EIS)
c)Understand key decisions supported
d)Match to analytical techniques
e)Create demonstration prototypes for 3 key dashboards (audit operations, KPIs and KRIs)
f)Understand interactions between dashboards and management / audit reaction
g)Create control representation in dashboards
h)Test procedures
i)Write up results
     Deliverables:
•CAR-Lab dashboard and visualization prototypes
•White Paper on the usage of dashboards as the working papers of the future
Evolving Towards the Future




                              49
Opportunities for Monitoring and Audit
• Creating Control system measurement and monitoring
  schemata (control dashboard)
• Creating standards for Business Process Monitoring and
  Alarming (to be able to detect variance)
• Exclude not time sensitive variables (e.g. depreciation, airport
  fees, etc)
• Creation of alternative real-time audit reports for different
  compliance masters




                                                                 50
• http://raw.rutgers.edu
 – A wide range of presentations / videos and papers from
   the multiple CA and CR conferences promoted by Rutgers

• http://raw.rutgers.edu/GEACS3



                                                            51

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Rutgers Research Center

  • 1. RARC / CarLab Research Center Presentation Miklos A. Vasarhelyi KPMG Professor of AIS, Rutgers Business School Director of RARC / CarLab
  • 2. Outline • Structure • Funding • Advisory Board • Continuous Assurance – Continuous data assurance – Continuous control monitoring – Continuous risk management and assessment • Evolving towards the future 2
  • 3. Funding • Very limited • About 250K a year • Currently – KPMG – Itau Unibanco – Annual conference – Small donations 3
  • 4. Advisory Board • Mr. Robert Elliott –Advisory Board Chairman - Partner (Retired) KPMG • Alan W. Anderson, CPA - ACCOUNTability Plus, LLC - President • Dr. Gerard Brennan, CAFÉ - Risk & Internal Control Officer – USA Siemens Corp. • Stuart V. M. Campbell - KPMG, National Managing Partner - Assurance and Advisory Services Center - KPMG LLP • Michael P. Cangemi, CPA, CISA - President & CEO - Cangemi Company LLC • Saty Ghosh - Senior Vice President – Internal Audit - IDT Corporation • Sam Parker - Director, Management Systems Division - AT&T • Amy Pawlicki - Director – Business Reporting Assurance & Advisory Services and XBRL - AICPA • Bill Titera - Partner - Ernst & Young - Chairman – Assurance Services Executive Committee • Lily Shue - Director - WISS Business Solutions LLC • Joe Steakley - Senior VP Internal Audit - HCA – Hospital Corporation of America • John Verver - Vice President, Services and Product Strategy - ACL Services. Inc. 4
  • 5. The CarLab Research Efforts • Siemens – Continuous control monitoring (CCM) – Audit automation – Authorization and control structure project • Continuity equations at HCA • Itau Unibanco – Branch monitoring and analytics – Transitory Accounts – Product sales monitoring • Insurance company – Forensics as CA -> the wires project – Claims 55
  • 6. 6
  • 7. The CarLab Research Efforts (2) • P&G – KPI project – Order to cash project -> selective automation – Vendor / payments project • KPMG projects – Technology adoption at CPA firms – Technology adoption at IA departments – The future of audit – Rapid Prototyping Environment for data research (RPE) – Dashboard / visualization / Advanced Analytics Representation (DVAA) • Telecom and Real Estate Co (TRE) – Duplicate Payments detection 7
  • 8. Carlab Research Efforts (3) • AICPA – ASEC – Common Data cube • Visionary effort for providing a common auditor data platform and a multiplicity of apps • Partnership with many vendors – ERM assurance • Assurance on the processes of risk management – Continuous audit • Rewrite of red book and conceptualization and principles and criteria for continuous audit 8
  • 9. RARC and Our Teaching Mission • The VPA effort – Automatic course recording experiment just started – A vision of free unencumbered educational stream for less developed nations • Clickers – Experimentation with response system uses in different contexts 9
  • 10. Out of the box • Standards Formalization: issuing standards in autocode 10
  • 11. AAA Impact of Research Taskforce 2009 • Perhaps the most important contribution of accounting information systems research to practice in the auditing and assurance domain is in continuous assurance. The work of Vasarhelyi and his colleagues on continuous assurance demonstrates the application of strong theoretical foundations to the practical problems of the auditor; in this case the internal auditor. 11
  • 12. Innovations in Continuous Auditing • Modeling: continuity equations (HCA) • Discrepancy detection -> multidimensional clustering • Process mining -> at international bank in Holland • Remote audit conceptualization – Siemens, P&G, Itau Unibanco • Automatic taxonomy creation • Multiple multivariate applications 12
  • 13. Monitoring and Control: 5 levels of activity Continuous Reporting Continuous Assurance Transaction assurance, Estimate assurance, Compliance assurance, Judgment evaluation Analytic monitoring level KPIs: Marketing/Sales Ratio •Drill Down Inventory turnover •History Intra-company transfers •Distribution Relationship level Sales change = Incremental Marketing cost * 2.7 +- 12% •Drill Down E-Care queries = number of sales * 4.1 •History Delay relationships •Distribution Data level •Product •Collection •Investment detail •Aging of •Inventory •Drill Down •Regions •Regions receivables •Distribution •History •Clients •Clients •Clients •Ownership •Distribution •Dynamics •Dynamics •Dynamics •Dynamics Structural level Cash Inventory Marketing Sales A/R Bad Debts Provisioning E-Care 13 5
  • 15. An Evolving Continuous Audit Framework Continuous Risk Monitoring and •Automation Assessment •Sensoring Continuous Continuous Data Control •ERP Audit Monitoring •E-Commerce Continuous Audit 15
  • 17. Alerts, Causal linkage, Confirmatory Follow the life- CPAS effort cycle of the extranets, Advanced CRMA application, analytics linked to • This methodology will change different timing for processes, different cycles data rich, new the nature of evidence, Audit by exception methods timing, procedures and effort involved in audit work. 17
  • 18. Continuity Equations / Long Distance Billing reservations flights Billing colections R e c e iv in g C a ll C re a tin g d a ta s e ts S p littin g c a ll P o s tin g f ro m o n e R a tin g e a c h d e ta il d a ta fro m o n e -to -o n e d e ta il in to b ille r file to a c c o u n ts B illa b le in d e p e n d e n t m a n y -to -m a n y file s to b e in s e v e ra l b illin g C u s to m e r te le p h o n e o n e -to -m a n y ' p o s te d to c y c le s c o m p a n ie s in d iffe re n t m a g . ta p e s b ille rs 1 2 3 4 5 18
  • 19. CPAS OVERVIEW System System Operational Reports Workstation DF-level 2 Operational Operation Report al Report DF-level 1 DF-level 1 DF-level 1 Operational Report Filter Alarm DF-level 0 Data Flow Diagrams Database Reports Analytics Metrics
  • 20. Lessons from HCA project • Intricate processes can and must be monitored • This may be done at the transaction levels, in addition to more aggregate levels • Models are necessary that are adaptive and can react to current circumstances • Errors may be automatically corrected • A tool may be derived from the performed work that could be superior to existing tools 20
  • 21. Metlife • Data stream of over 200K wire transfers • Data only currently available for the wires and the records possess little information • Little context knowledge of the major feeding streams • No fraud training data available • Worked during the audit supplementing the audit team work • Developed a series of data filters relating to specific conditions and trends • Working on an aggregate weighting model • Need in the field verification of picked data 21
  • 22. Metlife (Project 2-3) • Usage of clustering techniques to extract aberrations in data in parallel to the above discussed effort • Usage of clustering techniques in the evaluation of exceptions in life insurance claims 22
  • 23. Visualizing combination of attributes, we will be able to see similarity and differences among claims
  • 24. 60.00% Cluster by Insured information 50.00% POPULATION 40.00% 663 14000 30.00% 15500 31000 20.00% 106103 1023000 10.00% 0.00% cluster0 cluster1 cluster2 cluster3 cluster5 cluster6 cluster7 cluster8 Population 22.60% 47.05% 7.21% 17.19% 3.43% 0.64% 1.55% 0.33% 663 28.89% 44.02% 3.84% 20.54% 1.35% 0.45% 0.68% 0.23% 14000 19.02% 50.92% 1.23% 26.99% 1.23% 0.00% 0.00% 0.61% 15500 14.40% 56.27% 2.11% 23.12% 2.38% 0.13% 0.53% 1.06% 31000 56.22% 19.40% 3.98% 16.92% 1.99% 0.00% 1.00% 0.50% 106103 43.27% 35.67% 0.00% 20.47% 0.58% 0.00% 0.00% 0.00% 1023000 4.49% 52.65% 0.82% 39.59% 2.45% 0.00% 0.00% 0.00% Cluster 0 Cluster 1 Cluster2 Cluster3 Cluster5 Cluster6 Cluster7 Cluster8 We can cluster claims using different groups of attributes and flag the claims from specific groups in specific clusters. Several clustering of different groups of attributes can make up the score.
  • 25. P&G (work with the audit innovation team) • KPI projects • Automating order to cash • Vendor files / duplicate payments • Risk dashboard 25
  • 26. KPI project • Company has facilities in over 160 countries • Some facilities are manufacturing, some are pure distribution and sales • Content is local and world sourced • Substantive part of the work is building models for inventory and sales flow and trying to understand / model the level and flow variables • The objective is to detect out of the normal events both of business and exception nature (errors and fraud) • There are 4 large ERPs feeding the data / data is extracted in ACL and modeled in SAS • 16 different models have been developed and are being tested 26
  • 27. Order to Cash Project -> Selective Automation • This project aims to selectively automate parts of the audit using order to cash as the context – Audit action sheets – Taxonomization of protocols – Change of nature of evidence – Classification of automation level • Manual • Deterministic • Table comparison • Historical / stochastic – Architecture of the Structure – Prototyping of selected models 27
  • 29. Siemens Projects • Focused on audit automation – First project looked at automating CCM in SAP – Second project focused on a wider scope of automation – A third project would think about reengineering the audit action sheets – The fourth project aims at formalizing SOD, activities, and control structures 29
  • 30. The Siemens Project Learnings • ERPs are very opaque • Ratings schema are used and desirable • 20-40% of the controls may be deterministically monitored • Maybe other 20-40% may be convertible to be monitorable • New form of alarm evidence that we do not know how to deal • Continuous risk management and assessment needed for weighting evidence and choice of procedures 30
  • 31. Continuous Risk Monitoring and Assessment Assurance on Risk Management 31
  • 32. • CRMA is a real time based integrated risk assessment approach, aggregating data across different functional tasks in organization to assess risk exposure, providing reasonable assurance on firm’s risk assessment. • CRMA continuously computes key risk indicators (KRIs) with firm’s cross-functional data from its key business processes, as well as from external sources and validates them against whether they are linked to the firm’s risk exposure. 33
  • 33. KRI provides early warning systems to track the level of risk in the organization • KRI can be identified through analysis of key business activities – 6 steps for KRI identification (Scandizzo, 2005) • Well identified and computed KRIs provide a reliable basis for computing the riskiness of firm for specific risk, such operational risk, liquidity risk, as well as the overall riskiness of firm. – f(KRI(i),KRI(ii),…KRI(n))= Risk exposure – External risk factors may be mapped manually into the computation of KRIs and risk exposure. 34
  • 34. CRMA KRI CDA CMM f(KRIs) = Risk exposure KRI Enterprise System Accounting Process 1 R&D HR Process 2 Marketing Purchasin Process 3 Warehouse g 36
  • 35. The CarLab Research Efforts • Siemens – Continuous control monitoring (CCM) – Audit automation – Authorization and control structure project • Continuity equations at HCA • Itau Unibanco – Branch monitoring and analytics – Transitory Accounts – Product sales monitoring • Insurance company – Forensics as CA -> the wires project – Claims 3737
  • 36. Process Mining Mieke Jens (Hasselt University) Michael Alles (Rutgers Univ.) 38
  • 37. What is Process Mining of Event Logs? • The basic idea of process mining is to extract knowledge from event logs recorded by an information system. Until recently, the information in these event logs was rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs. Fuelled by the omnipresence of event logs in transactional information systems… process mining has become a vivid research area. • http://is.tm.tue.nl/staff/wvdaalst/BPMcenter/process%20mining.htm 3939
  • 38. An Example of An Event Log of an Invoice Invoice number 003 Invoice number 003 Input data Supplier: AT&T Supplier: AT&T Posting date: Feb 10th 2010 Posting date: Feb 10th 2010 120 USD 120 USD Description: internet services Jan 2010 Description: internet services Jan 2010 ‘Signature of John’ ‘Signature of John’ ‘Signature of Pete’ ‘Signature of Pete’ PLUS - ‘Create Invoice’ Timestamp: Feb 12th 2010; 08:23 AM Originator: Mike Fields: supplier: AT&T, posting date: 02-10-2010, value: 100 USD, Description: internet services Jan 2010 Meta-data - ‘Change’ Timestamp: Feb 12th 2010; 08:43 AM Originator: John Field changed: Value Value old: 100 USD Value new: 120 USD -‘Sign’ Timestamp: Feb 12th 2010; 08:44 AM Originator: John Figure1: Visualization of Input Data and Event Log Data of an Invoice 4040
  • 39. Research Team (all in AIS department but multidisciplinary background) • Miklos A. Vasarhelyi, Prof. II (Continuous Auditing) • Alex Kogan, Prof. – (Computer Scientist) • Michael Alles, Professor –( Economics) • Helen Brown (Auditing) • Don Warren, Professor –( Audit Practice) • Trevor Stewart, Professor (Retired D&T partner) • Silvia Romero – faculty Montclair University • Yong Bum Kim – PhD Student (Statistics) • Daewoon Moon – PhD Student (Risk Management) • Ryan Teeter – PhD Student (Computer Science) • Qi, Liu– PhD Student • Hussein Issa– PhD Student • David Chen– PhD Student (Knowledge Engineering) • Danielle Lombardi - PhD student • JP Krahel - PhD student • Karina Chandia - PhD Student 41
  • 40. Rapid Prototyping Environment for Data Research
  • 41. RPE Overview •Client Challenge: External audit work and audit-related advisory services progressively incorporate advanced analytics and knowledge bases. •Area of Research: Evolved application of analytic methods •Potential Use: Building on CAR-Lab experience from client projects (general ledger, transitory accounts, claims, wires, audit automation, loans, credit, with representative datasets for experimentation and demonstration) and employing existing public domain packages (such as WEKA or D), perform the following: – Create masked datasets for demonstration of different techniques. – Focus on technologies to deal with real-time and archival data feeds, in particular screening and automatic pattern recognition. A wide set of management problems falls into this domain which is being changed by the constant updating of data. – The software tools, data, and models available will allow for testing the adequacy and demonstration of capabilities of advanced analytics. •Example of Output: Use continuity equations, automatic error correction, delayed time contingent equations, and other linear methods to represent HCA transaction flow *See CAR-Lab-related qualifications and experiences on following pages
  • 42. RPE New Analytic Methods for Data Research Develop applications for continuous transaction monitoring, continuous controls monitoring, and continuous risk assessment/monitoring using: – Continuity equations – Rule based systems • Predictive modeling • Pure rules • Automatic data correction • Hybrid • Time lag / time series / cross- • Forensic ex-ante settings sectional modeling – Clustering technology – Machine learning • Fraud identification • Trained / untrained • Identification of related • Neural networks processes in a BI context – Genetic algorithms • Graphic demonstration / item – Logic / probit / and other identification / semantic domain regression techniques usage demonstration
  • 43. Dashboard / Visualization / Advanced Analytic Representation
  • 44. DVAA Overview •Client Challenge: Develop dashboards for risk monitoring / develop other visualization prototypes for continuous assurance (CA), continuous monitoring (CM), continuous risk measurement and assessment (CRMA), and continuous controls monitoring (CCM) •Area of Research: Risk and transaction monitoring and human-computer interaction •Potential Use: • Creation of KPI- and KRI-based views of the world in a continuous audit environment including status-quo, projection, and interpretations • Management routines / internal audit response to continuous assurance alerts / alarm events • KPIs and KRIs for selected major business processes and/or regulated industries such as financial services or energy (trading businesses)
  • 45. DVAA Overview (2) •Examples of Output: • Tied to the management of unobservable controls at a large ERP in an industrial setting -> special artifacts for the representation of different controls, alarms for control deficiencies, indices of control coverage, other graphics action objects • Aimed at controlling the different dimensions of risk measurement encompassing direct links to quantitative models as well as arbitrary qualitative items • Tied to the management of large volumes of data in the understanding, management and transaction investigation in a transitory account setting within a financial institution
  • 46. DVAA At-a-Glance •Estimated Hours to Accomplish Project: 730 Hours •Expected completion date: April 2011 •Key Activities: a)Design scenarios of audit support b)Survey for existing products and research (EWP, EIS) c)Understand key decisions supported d)Match to analytical techniques e)Create demonstration prototypes for 3 key dashboards (audit operations, KPIs and KRIs) f)Understand interactions between dashboards and management / audit reaction g)Create control representation in dashboards h)Test procedures i)Write up results Deliverables: •CAR-Lab dashboard and visualization prototypes •White Paper on the usage of dashboards as the working papers of the future
  • 47. Evolving Towards the Future 49
  • 48. Opportunities for Monitoring and Audit • Creating Control system measurement and monitoring schemata (control dashboard) • Creating standards for Business Process Monitoring and Alarming (to be able to detect variance) • Exclude not time sensitive variables (e.g. depreciation, airport fees, etc) • Creation of alternative real-time audit reports for different compliance masters 50
  • 49. • http://raw.rutgers.edu – A wide range of presentations / videos and papers from the multiple CA and CR conferences promoted by Rutgers • http://raw.rutgers.edu/GEACS3 51