Using Data Analytics to Detect Fraud. Other Data Analysis Techniques

Transcription

1 Using Data Analytics to Detect Fraud Other Data Analysis Techniques

2 Qualitative Data Analysis Most data analysis techniques require the use of data in the form of numbers. Qualitative data analysis is the process used to collect and analyze unstructured data. It requires extreme organization, as data sets tend to be quite large. It is best used in conjunction with quantitative data analysis.

3 Textual Analytics Analysis of unstructured, text-based data for keywords that reveal patterns, sentiments, and relationships indicative of fraud Uses linguistic technologies, statistical techniques, and scoring algorithms to categorize and analyze textual data Requires: Sophisticated software Thorough understanding of the legal issues regarding employee rights and workplace searches

4 Textual Analytics Data Sources Voice files News feeds Instant messages User documents Social media posts Apps Presentations Video Sales and marketing

5 Textual Analytics Emotive tone derogatory, surprised, secretive, angry, worried, or confused communications Ethical behavior harassing, secretive, cursing Concept, entity, relationship, or event extraction Text link analysis Social network analysis

6 Developing a List of Keywords Keywords depend on industry, relevant fraud risks, and data set. Use fraud triangle as reference. Deadline Quota Short Problem Concern Reasonable Deserve Borrow Therefore Override Write off Adjust Discount

7 Visual Analytics Some data sets are so vast or complex that they are difficult to analyze using traditional means. Visual analytics can help identify patterns or anomalies in such data.

8 Word Clouds Risky Creative jobs Uncertainty unbalanced Weak delay moderate Liability overstate critical audit accrual strategy

9 Heat Maps

10 Tree Maps Accounting Accounts Payable Information Technology Financial Misstatement Penalties and Fines Thefts of Assets Fraudulent Disbursements System Failures Reputation Risk Research and Development Financial Misstatement Sales Financial Misstatement Fraudulent Disbursements Theft of Assets Natural Disasters Administrative Reputation Risk Penalties and Fines Natural Disasters System Failures Theft of Assets Fraudulent Disbursements Reputation Risk Theft of Assets Fraudulent Disbursements

11 Link Analysis

12 Geospatial Analysis

13 Timeline Analysis Highlight key times, dates, and facts. More readily determine a sequence of events. Analyze multiple or concurrent sequences of events. Track unaccounted-for time. Identify inconsistencies or impossibilities in data.

14 Conflict Checking Use of specialized software to identify potentially problematic relationships: Conflicts of interest on a company s board of directors Relationships between potential new hires and business units Vendors that operate from addresses that previously have been associated with fraud Vendors that are not listed in any online commercial database or that are listed on government excluded party lists

15 Log-In Monitoring Software Used to identify anomalies in log-ins to company system: Unusual times or days Unusual locations Excessive log-ins Unauthorized log-ins

16 Employee Risk Ranking Brings together big data both structured and unstructured data to identify and monitor highrisk employees Aggregates all tests and analytics into dashboard Similar analysis can be performed for vendors or customers Data privacy and employee rights are integral concerns

17 Employee Risk Ranking Example 1. Keywords Percentage of keywords around pressure, opportunity, and rationalization in and IM communications Scaling: 3 2. T&E analysis Ranking of T&E out-of-compliance hits and overall scoring Scaling: 3 3. Sales activity Ranking of sales activity, field notes, and sales returns and allowances Scaling: 4 4. User activity 5. Third-party risk 6. Alias clustering 7. Emotive tone Percentage of instances within period where custodian sends or receives information involving those outside of peer group, as identified through hierarchies Instances where employee is linked to high-risk third parties (e.g., customers, vendors, state-owned entities, etc.) in , T&E, or sales activity Percentage of instances within that week where custodian sends or receives ESI involving at least one of their identified communicative aliases Percentage of instances where the employee sends or receives ESI with negative emotions (angry, frustrated, secretive, etc.) identified through linguistic analyses Scaling: 2 Scaling: 2 Scaling: 3 Scaling: 5 Custodian C1 C2 C3 C4 C5 C6 C7 Scaling C1 Scaling C2 Scaling C3 Scaling C4 Scaling C5 Scaling C6 Scaling C7 Score A, Week A, Week

18 Transaction Risk Scoring Brings multiple analytics together Monitors and scores transactions based on number of breaches: Round-dollar amounts Incomplete invoice Keyword in payment description field Can apply weights to certain tests to influence the overall risk score