Dr. Robert K. MinnitiDBA. CPA, CFE, Cr.FA, CVA, CFF, MAFF, CGMA, PI, MBA

President, Minniti CPA, LLC

Using Data Analytics in a Fraud Investigation

Dr. Robert K Minniti

DBA – Doctor of Business AdministrationCPA - Certified Public AccountantCFE – Certified Fraud ExaminerCrFA – Certified Forensic AccountantCFF – Certified in Financial ForensicsCVA – Certified Valuation AnalystMAFF – Master Analyst in Financial ForensicsCGMA – Charted Global Management AccountantPI – Licensed Private Investigator

Objectives

Upon completing this class you will be able to:

Identify various types of data analytics that can be used in a fraud investigation

Changing Technology in Business

Increased reliance by businesses on technologyIncreased use of electronic data storageIncreased use of electronic communicationsIncreased access to dataBetter tools for analyzing dataLarger databasesIncreased use of data in making business decisions

Changing Technology in BusinessNew & changing technologies:Cloud computingBlockchainArtificial intelligenceSmart businesses Smart homesRoboticsVirtual realitySelf-driving trucksBiometricsBotnets of things

Internet of Things (IoT)Quantum computersSocial Media Interactive TVOn-Demand Services Internet SalesCryptocurrenciesXBRLStreaming DataBusiness Intelligence (BI)

Data Terminology

Data VolumeThe amount of data determines value and potential of dataData VelocityThe speed at which data is generated, collected, and processedData VarietyIndicates the different types of data that are availableData VeracityIndicates how good or valid the data is

How Much Data Do You Have?Description Size

Byte = 8 bits

Kilobyte = 1,000 bits

Megabyte = 1,000,000 bits

Gigabyte = 1,000,000,000 bits

Terabyte = 1,000,000,000,000 bits

Petabyte = 1,000,000,000,000,000 bits

Exabyte = 1,000,000,000,000,000,000 bits

Where is the Data Located?

ServersApplication databasesStorage devicesCloud storageMobile devicesBackup filesInternet

Data Recovery

Corrupt partitionsFile system errorsPartition table errorsOverwritten dataLogical bad sectorDeleted dataSlack spacePassword protected filesCamouflaged files

Data Formats

Physical documents

Electronic informationStructured dataTransactional dataAudit trailsDatabasesSpreadsheets

• Unstructured data•Email•Documents•Social Media•Demographics•Geographic•Internet

Data Integrity

Physical data integrity must be maintained forCollection of DataUse of DataDissemination of DataStorage of DataDestruction or Removal of Data

Logical data integrityAccuracyConsistencyCompleteness

Examples of Data Management Software

• IBM ECM•DocStar ECM•ViewCenter•FileHold•Docuware•Bolste•Tresorit• Imaging Made Simple• infoRouter•FileCenter•Digital Drawer

Types of Data Analysis

Exploratory PredictiveDescriptiveInferentialConfirmatoryText or Number SearchesCausal

Common types of analytical procedures

Trend analysisRatio analysisNonstatistical predictive modelingDescriptive statisticsMeanModeMedianStandard Deviation

Common types of analytical procedures

Regression analysisMost complex type of analytical procedureVarious statistical measures:R2 (that is, coefficient of determination)T-statisticsStandard error

CorrelationsHypothesis testing Internal Control Testing

Normal Distribution of Data

Binomial Distribution of Data

Binomial distribution occurs when a series of tests are conducted with two possible answers such as yes/no, true/false, or correct/incorrect.

In accounting and auditing this type of testing is usually conducted to determine if internal controls are effective.

Random Distribution of Data

Random distribution occurs when the data does not have a discrete pattern.

Examples of Data Analysis Software

•Tableau•Diver Platform• Inzata• Infotools Harmoni•Zoho Analytics•Chartio•Klipfolio•OpenText Analytics Suite•EasyMorph•Scoreboard KPI Management•MarketSight•Analytics2Insights

•AnswerRocket• JPM Statistical Software•Monarch• IntilliFront BI•Cluvio•AnswerDock•XLSTAT•Minitab•Stata•Alteryx•QueryStorm•Zap Audit

•OriginPro•SAS/STAT•Python•ESM+Strategy•WinSQL•Nvivo•DataPlay• IDEA•ATLAS.ti• InfoZoom•Putler•Compass

Caution

When collecting and analyzing Personal Information (PI) or Personal Health Information (PHI) care must be taken to ensure the information is not compromised or otherwise disclosed.

With the GDPR and various Federal and State laws on data security, organizations are required by law to take precautions when collecting, transmitting, using, storing, or destroying personal data that is considered to be confidential.

Any organization collecting or analyzing PI or PHI needs to have written internal controls for the data processes which are reviewed or audited on a regular basis.

Data AnalysisQuantitative AnalysisPerformed on numerical dataAccounting dataPerformance data

Qualitative AnalysisPerformed on unstructured data Information from questionnairesCustomer, employee, and vendor surveysEmail, text messages, etc.

Types of Data Analytics

Descriptive Analytics (Past Performance)Diagnostic Analytics (Causes)Predictive Analytics (Future)Prescriptive Analytics (Best Options)

Performing Data Analysis

Statistical AnalysisDescriptive StatisticsInferential Statistics (Samples)Data MiningData Visualization

Data Mining

Data mining uses algorithms to identify data in large data bases. There are many types of algorithms, some of the most common are the following:

Classification AlgorithmsRegression AlgorithmsSegmentation AlgorithmsAssociation AlgorithmsSequence Analysis Algorithms

Performing Electronic Data Analysis

Retrieve Values

Filter Data

Compute Derived Values

Find Extremes

Sort Data

Determine Ranges

Characterize Ranges

Find Anomalies and Outliers

Cluster Data

Correlate Data

Contextualization (Relevance to User)

Performing Data Analysis

• Applying sound and repeatable methodologies• Using practices that have historically withstood challenge• Using data analysis software that generally is accepted in the

profession• Using academically accepted data analytic software• Using methods that yield reproducible results• Developing comprehensive documentation

Designing Procedures for Data Analysis

Is the data complete?Is the data accurate?Is data conversion necessary?Does the data need to be normalized?Does the data need to be cleansed?Does the data contain confidential information?

Data Analysis Examples

You can test for data validityProduced on Arbutus Data Analytics Software

Data Analysis Examples

You can normalize addresses and test for duplicate addresses in your database

Produced on Arbutus Data Analytics Software

Data Analysis Examples

You can test for duplicate payment amounts and duplicate invoice numbers, including similar items.

Produced on Arbutus Data Analytics Software

Data Analysis Examples

You can test for gaps in checks, purchase orders, invoice numbers, etc.

Produced on Arbutus Data Analytics Software

Data Analysis Examples

You can test for changes in vendor information, including credit limit changes.Produced on Arbutus Data Analytics Software

Things to consider when using graphics or tablesLevel of detail in a graphicScaling of axes on a graphPrimary focus of the graphic

Revenue Revenue

Data VisualizationTimelinesPie ChartsBar GraphsLine GraphsGenogramsLink AnalysisFlow ChartsFishbone Diagrams Scatter PlotsData TreesHistogramsBox PlotsArea Graphs

Time Series PlotTernary PlotPictograph Stem and Leaf PlotVenn DiagramFrequency DistributionsGantt ChartsArc Diagrams Sankey DiagramsRose ChartsAlluvial DiagramBubble Clouds Spider Charts

Candlestick ChartsWaterfall ChartsDendogramsRadial TreesWedge Stack GraphPartition GraphHive PlotTube MapDependency GraphDasymetric MapCartogramChoroplethsProportional Symbol Map

Data Visualization Example

Data Visualization Example

Data Visualization Example

Data Visualization Example

Data Visualization Example

Data Visualization Example

Data Visualization Example

Data Visualization Example

Validity considerations• External Validity Concerns:

• Pre-testing• Auditor interaction• Interventions• Generalization

• Internal Validity Concerns• Credibility• Causality• Instrument design

• Statistical Validity Concerns• Sample size• Maturation

Categories of Bias

There are two (2) categories of bias

• Conscious Bias• Unconscious Bias (Implicit Bias)

Examples of Conscious Bias

Family RelationshipsPersonal RelationshipsFinancial RelationshipsWork Relationships (Current or Former)Business Relationships (Vendors or Customers)Business OwnershipPast ExperiencesReligious or Social Group Membership

Examples of Unconscious Bias

Affinity Bias (Similar to you)Confirmation Bias (You are right)Bounded Awareness (Did not confirm)Priming (Influenced by other people or data)Anchoring (Auditor is convinced a number is correct)Availability Bias (Deciding based on most recent data)Group ThinkRush to Solve (Must meet deadlines)

Examples of Unconscious Bias

Negativity Bias (Extra weight to negative data)Ambiguity Effect (Just doesn’t care)Blind Spot Bias (Don’t recognize issues)Empathy Gap (Allowing emotions to control decisions)Focalism (Overreliance on first data collected)Framing (Different conclusions on the same data depending on who

presents the data)Ostrich Effect (Ignoring data)

Any Questions?