Continuous Assurance and Audit Data Analytics

Transcription

1 Continuous Assurance and Audit Data Analytics Miklos A. Vasarhelyi, Rutgers Business School 37WCARS Gold Coast, Australia 9/14/2016

2 Data Analytics Continuous Assurance and Audit Data Analytics Outline The CarLab Evolving Towards Continuous Assurance The Audit Ecosystem Analytics in Continuous Assurance The Regulators Dilemma What could the future hold?

3 THE CARLAB

4 4

5 Supply Chain Inventory PPP Credit Card Insura nce A/P Sigma Bank HC A Process Mining P& G Itaú- UniBanco KPM G Verizon IDT CDA CA Technologies HP GL KPIs/KRIs Duplicate Payments AIC PA FCPA Sales Commission Audit Automation P&G: Order to Cash Auditor Judgment Siemens- AAS Automation AICPA ADS / APS CRMA AT &T D& B Duratex J+J A/P FCPA Met- Life Claims Wires American Water / Caseware Itaú- Unibanco P& G CCM Inventory Dashboard Talecris / ACL PPP Insuran ce Siem ens Continuous Control Monitoring Audit Methodologies Multidimensional Clustering Process Mining Continuity Equations Predictive Auditing Visualization Analytic Playpen 5

6 The CarLab Choosing apps Predictive Analytics with Weather data Audit data analytics and EDA Envisaging the future of audit and Big Data Text Mining Monitoring Unibanco s branches Visualization Process Mining at Gamma Bank Expert System for P- Card Logit regression for control risk assessment Exceptional Exceptions Client Retention Project Litigation prediction Fraud Risk Assessment using EDA Detecting duplicate records Continuity equations Predictive Audit Credit card Default prediction Insurance Analytics Multidimensiona l clustering for fraud detection Rule-based selection for transitory accounts Continuit Equations at HCA XBRL Insurance Analytics 6

7 Expert System Ratio Analysis Descriptive Statistics Audit Software (ACL&IDEA) With Basic Data Analysis Data Visualization (TABLEAU&QLIKVIE W) Audit Analytics Coso Framework Enterprise Risk Management Text Mining Neural Network and Association Analysis Regression Cluster Analysis Big Data Basic Data Analytics Audit Data Standards Risk Control KRIs Information Risk Management Special Topics in Audit Analytics Process Mining Hypothesis Testing Exploratory Data Analytics Analytics in Continuous Auditing Enterprise Cases of Continuous Risk Monitoring and Assessment Implementation Risk Management Tools

8 EVOLVING TOWARDS CONTINUOUS ASSURANCE

9 A Progressive Automation Scenario Audit plan Assertions: Existence Completeness Valuation & allocation Classification & understandability Ratio Analytic Query Dashboard Trend Data matching Classify Query Dashboard Audit evidence Exception Benchmark Confirmation Tracing Relationship 9

10 The Audit Data Standard Architecture 10

11 THE AUDIT ECOSYSTEM

12 Cloud points of entry Cooperating agent (discriminant Function) Krons & Daemons Single function (agents) Cloud points of exit Activity Monitoring Corp.databases Corp.databases Exception Selection methods Auditor Exception evaluation Preventive audit filters Big data Inventory Assurance Ecosystem Processes sale Collect cash Receivables Treasury Cash 12

13 ANALYTICS IN CONTINUOUS ASSURANCE

14 The Regulators Dillemma orevenue Three Way Match orevenue segregation of duties opredictive Analytics oclustering

15 Data Analytics Illustration 1 Revenue Three Way Match (cont.) Entity ABC has revenue of 125 million generated by 725,000 transactions. The three way match procedure is executed with the following results: Amount ( 000) % Number of Transactions % No differences 119, , Outliers: Quantity differences 3, , Pricing differences 2, , Note: Materiality for the audit of the financial statements as a whole is 1,000,000.

16 Data Analytics Illustration 2 Revenue Segregation of Duties (cont.) For entity ABC, an analysis of segregation of duties was executed with the following findings: Number of users Amount ( 000) Number of transactions Population of sales , ,000 Instances in which same individual created sales invoice, sales return or credit note and applied cash Instances in which same individual executed sales order processing, dispatched goods (delivery document) and applied cash Note: Materiality for the audit of the financial statements as a whole is 1,000, , ,692 46,903

17 Data Analytics Illustration 3 Predictive Analytic (cont.) Clustering Using Store Sales by Peer Group

18 Data Analytics Questions Are predictive analytics relevant to audit data analytics? Are predictive analytics a risk assessment procedure, substantive procedure, or both? What procedures are necessary to validate non-traditional external data (e.g., social media, weather, traffic patterns)? Is it appropriate to reduce or eliminate other substantive tests with predictive analytics? (ISA 330 paragraph 7) Should there be guidance of an acceptable level of variance? Should there be an experimentation period to create guidance for predictive analytics? What predictive methodologies would be acceptable?

19 owhat COULD THE FUTURE HOLD?

20 Data Analytics What could the future hold? Machine Learning / Cognitive Computing As an alternative to statistical and optimization methods, human decision models can be built either by capturing human behavior and models (expert / cognitive computing) or by using machine learning methods to develop these rules. Typically supervised machine learning methods such as decision trees, logistic regression, and vector machines will be used. Possibility that in the future, the volume of data may exceed human capacity to audit that data. Expert Knowledge Elicitations in a Procurement Card Context at PG Deniz Applebauum and Abdullah Al-Awadhi Credit card default and lawsuit prediction at Itau-Unibanco Miklos A. Vasarhelyi, Eduardo Miyaki, Qi Liu, Jun Dai, Fei Qi Huan, Pei Li, and Basma Modarrat

21 Data Analytics What could the future hold? (cont.) Some of the Challenges that Arise Machine Learning / Cognitive Computing Are machine learning techniques relevant to audit data analytics? Is it reasonable to reduce or eliminate other substantive tests with machine learning? What impact might this reduction have? Level of guidance needed on methods? Should there be a new method of separate review of the technologies used if these methods are used? Should there be an experimentation period to create guidance for machine learning? What predictive methodologies would be acceptable?

22 Data Analytics What could the future hold? (cont.) Continuous Control Monitoring formalization of controls evaluation Continuity Equations structural modeling in continuous auditing Process Mining in Auditing audit Logs in Enterprise Resource Planning systems Exceptional exceptions choosing exceptions from huge data Evidence from Big Data electronic logs everywhere Audit Data Standard normalizing data to facilitate analytic applications Visualization for continuous assurance monitoring creating auditor dashboards

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