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Detectlets for Better Fraud Detection

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Presentation on theme: "Detectlets for Better Fraud Detection"— Presentation transcript:

1 Detectlets for Better Fraud Detection
Conan C. Albrecht, PhD Marriott School of Management Brigham Young University

2 Today’s Presentation Give a few fraud stories Outline the Detectlet vision and Picalo Architecture Show example code and working products Describe future research directions and solicit help

3 Fraud on behalf of an organization
Two Types of Fraud Fraud on behalf of an organization Financial statement manipulation to make the company look better to stockholders Also called management fraud Fraud against an organization Stealing assets, information, etc. Also called employee or consumer fraud

4 ACFE Report to the Nation Occupational Fraud and Abuse
2 1/2 year study of 2608 Frauds totaling $15 million Fraud costs U.S. organizations more than $400 billion annually. Fraud and abuse costs employers an average of $9 a day per employee The average organization loses about 6 percent of its total annual revenue to fraud and abuse admitted to by its own employees

5 Ernst & Young Fraud Study 2002 (Europe)
One in five workers are aware of fraud in their workplace 80% would be willing to turn in a colleague but only 43% have Employers lost 20 cents on every dollar to workplace fraud Types of fraud Theft of office items—37% Claiming extra hours worked—16% Inflating expenses accounts—7% Taking kickbacks from suppliers—6%

6 Cost of Fraud Fraud Losses Reduce Net Income $ for $
If Profit Margin is 10%, Revenues Must Increase by 10 times Losses to Recover Affect on Net Income Losses……. $1 Million Revenue….$1 Billion Revenues $ % Expenses % Net Income $ % Fraud Remaining $ 9 To restore income to $10, need $10 more dollars of revenue to generate $1 more dollar of income.

7 Fraud Cost….Two Examples
Automobile Manufacturer $436 Million Fraud Profit Margin = 10% $4.36 Billion in Revenues Needed At $20,000 per Car, 218,000 Cars Large Bank $100 Million Fraud Profit Margin = 10 % $1 Billion in Revenues Needed At $100 per year per Checking Account, Million New Accounts

8 A Recent Fraud Large Fraud of $2.6 Billion over 9 years
Year 1 $600K Year 3 $4 million Year 5 $80 million Year 7 $600 million Year 9 $2.6 billion In years 8 and 9, four of the world’s largest banks were involved and lost over $500 million Some of the organizations involved: Merrill Lynch, Chase, J.P. Morgan, Union Bank of Switzerland, Credit Lynnaise, Sumitomo, and others.

9 Every Person Has A Price
Abraham Lincoln once threw a man out of his office, angrily turning down a substantial bribe. “Every man has his price”, explained Lincoln, “and he was getting close to mine.”

10 Examples of Data-Based Detection

11 Workers were logging hours on two timecards for simultaneous jobs
Superhuman Workers Summed all hours (normal, OT, DT) per two week period, regardless of invoice or timecard) Workers were logging hours on two timecards for simultaneous jobs One search summed all hours worked by employees within two week periods. It ignored which project it was on, which plant it was at, what type of work it was, etc. We found people that were working over 100 hours per week. This could perceivably happen once or twice, but many workers did this consistently, month after month (as seen in the trend above). Investigation into these employees showed that they were clocking in under two time cards at different locations in the plant, effectively doubling their hours each week.

12 Work Orders Authorized By Purchaser
The Family Business The next few slides show the results of a specialized search. We stratefied the data by the amount of work orders that purchasers authorized during each period. As can be seen, purchaser F authorized considerably more work than other purchasers. Work Orders Authorized By Purchaser

13 Invoice Charges Authorized By Purchaser
The Family Business Purchaser F is again shown in this spreadsheet, which is now stratefied by invoice charges. Again, he is authorizing considerably more charges. Invoice Charges Authorized By Purchaser

14 Work Orders Given To Contractor Crew
The Family Business The picture became clearer as we stratefied by contractor crew. The company subcontracted with third-party companies for this type of work, and it is obvious which crew is getting the majority of the work. See the totals across the bottom. Work Orders Given To Contractor Crew

15 Tip stated that kickbacks were occurring with a certain company
The Family Business Tip stated that kickbacks were occurring with a certain company We researched the company and determined which purchaser authorized the work A contractor crew and company purchaser were family When we investigated these people on both sides of the transaction, the same last name was found on each side. The individuals came from the same immediate family, and the purchaser was funneling work to his family’s company.

16 Systematic Increases In Spending
These next few slides show some sample data patterns that researchers can look for. They are not all-inclusive, but are just examples of what to look for and one way to visualize it. The above time engine results show employee (with names grayed out) trends in spending. The shown trend is increasing regularly.

17 Systematic Increases In Spending
This slide shows another increase in spending. Note how the time engine flags the suspicious data points in red.

18 Unexpected Peaks In Spending
This slide shows an unexpected peak in spending. The employee had normal spending until one month where he or she spent significantly more than expected. It is important to understand why this occurred.

19 Increases In Only Part Of A Trend
This data pattern illustrates how subtrends need to be analyzed. A simple average (or regression equation) of this trend would be very normal. However, a problem trend is flagged when only the first five data points are considered. The time engine ran repeated analyses on all parts of a trend.

20 Caught by his Pool…

21 Research Background

22 Accounting History 1940 SEC Statement: “Accountants can be expected to detect gross overstatements of assets and profits whether resulting from collusive fraud or otherwise” (Accounting Series Release 1940) 1961: “If the ten (auditing) standards now accepted were satisfactory for their purpose we would not have the pleas for guidance on the extent of (auditors’) responsibility for the detection of irregularities we now find in our professional literature.” (Mautz & Sharaf 1961) SAS 82 SAS 99 Expectation Gap

23 Historical Fraud Research
Excellent literature review by Nieschwietz, Shultz, & Zimbelman (2000) Who commits fraud Red flags Expectation gap Auditor expectations Game theory between auditors and management Auditor-client relationships Risk assessment, decision aids Management factors affecting fraud

24 FS Fraud using Ratio Analysis
Hansen, et. al (1996) developed a generalized qualitative-response model from internal sources Green and Choi (1997) used neural networks to classify fraudulent cases Summers and Sweeny (1998) identified FS fraud using external and internal information Benish (1999) developed a probit model using ratios for fraud identification Bell and Carcello (2000) developed a logistic regression model to identify fraud Current work by McKee and by Cecchini and by Albrecht None have found the “silver bullet” in using external information to identify fraud Management (FS) fraud is very difficult to find

25 Each firm seems to have different groups working on fraud detection
What are the Big 4 Doing? Each firm seems to have different groups working on fraud detection No best practices model has emerged IT auditors perform control testing on company systems, not fraud detection Meeting with Bill Titera of EY

26 Why Don’t “They” Find Fraud?
Limited time Our most precious resource is our attention History Heavy use of sampling - lack of detail Lack of historical fraud detection instruction Lack of fraud symptom expertise Lack of fraud-specific tools Lack of analysis skills Lack of expertise in technology Auditors do find percent of fraud ACFE 2004 Report to the Nation

27 Isn’t there a better way?
Reasonable time requirements Integrate AI and auto-detection Within reach of most auditors (highly technical skills not required) Integrate easily into different database schemas Cost effective

28 A small “manual” about frauds
Initial Thoughts A small “manual” about frauds Cliff notes about different types of fraud Describes the scheme Describes the indicators of the scheme Worldwide repository wth contributions from many different industries Primary focus was training

29 Input is one or more table objects Output is one or more table objects
Detectlets A detectlet encodes: Background information on a scheme Detail on a specific indicator of the scheme Wizard interface to walk the user through input selection Algorithm coded in standard format “How to interpret results” follow-up Input is one or more table objects Output is one or more table objects

30 Detectlet Demonstration
Bid rigging where one person prepares all bids

31 Potential Supporting Platforms
MS Access ACL or IDEA Build ground up application Allows total control over platform Stays with open source rather than tying the program to a particular platform For example, consider PowerBuilder Supports Windows, Unix, Linux, Mac Allows embedded use within a greater platform Personal preference was Python

32 Picalo: The Supporting Platform

33 Central Detectlet Repository

34 How Detectlets Address the Problem
Limited Time: Detectlets provide a wizard interface for quick execution; they can be chained and automated into a larger system High Cost: Detectlets are based in open source software, putting them within reach of small and large accounting firms; they also create a community environment for fraud detection

35 How Detectlets Address the Problem
Lack of fraud symptom expertise: Detectlets provide a large library of available routines to both train and walk auditors through the detection process Lack of fraud-specific tools: Picalo provides an open solution that we can improve over time; it puts a fraud-specific toolkit in the hands of auditors

36 How Detectlets Address the Problem
Lack of analysis skills: Detectlets encode full algorithms and code, allowing the auditor to stay at the conceptual level rather than the implementation level Lack of expertise in technology: Detectlets provide a wizard-based solution that are easy to use; Picalo provides an Excel-like user interface

37 Picalo Level 1 API

38 Data Structures The Table object is the basic data structure. Nearly all routines both input and return tables, allowing them to be chained. Its methods include sorting, column operations, row operations, import/export from delimited text and Excel formats. Column types include Boolean, Integer, Floating Point, Date, DateTime, String, etc.

39 Simple Module Provides joining, matching, fuzzy matching, and selection. col_join, col_left_join, col_right_join, col_match, col_match_same, col_match_diff, compare_records, custom_match, custom_match_same, custom_match_diff, describe, expression_match, find_duplicates, find_gaps, fuzzysearch, fuzzymatch, fuzzycoljoin, get_unordered, join, left_join, right_join, select, select_by_value, select_outliers, select_outliers_z, select_nonoutliers, select_nonoutliers_z, select_records, soundex, soundexcol, sort, etc.

40 Benfords Module calc_benford: Calculates probability for a single digit get_expected: Calculates probability for a full number analyze: Analyzes an entire data set and calculates summarized results

41 Crosstable Module pivot: Similar to Excel’s pivot table function
pivot_table: Pivots and keeps detail in each cell pivot_map: Pivots and keeps results in a dictionary rather than a grid pivot_map_detail: Pivots and keeps results in a very detailed fashion using a dictionary

42 Database Module OdbcConnection: Connects to any ODBC-compliant database PostgreSQLConnection: Connects to PostgreSQL MySQLConnection: Connects to MySQL Also includes various query helper functions, such as query creation, results analysis, etc.

43 Financial Module Calculates various financial ratios to help in financial statement analysis: Current ratio Quick ratio Net working capital Return on assets Return on equity Return on common equity Profit margin Earnings per share Asset turnover Inventory turnover Debt to equity Price earnings

44 Grouping Module Stratification gives the details behind SQL GROUP BY. It keeps the detail tables rather than summarizing them. stratify: Stratifies a table into N number of tables stratify_by_expression: Stratifies a table using an arbitrary expression stratify_by_value: Stratifies on unique values stratify_by_step: Stratifies based on a set numerical range stratify_by_date: Stratifies based on a date range Summarizing is similar to SQL GROUP BY, but it allows any type of function to be used for summarization (GROUP BY generally only allows sum, stdev, mean, etc.) This can by done in the same ways as stratification.

45 Trending Module Various ways of analyzing trends and patterns over time. cusum, highlow_slope, average_slope, regression, handshake_slope

46 Python Libraries Powerful yet easy language with a significant online community Full object-oriented support (classes, inheritance, etc.) Text maniuplation and analysis routines Web site spidering routines analysis routines Random number generation Connection to nearly all databases Web site development and maintenance Countless libraries available online (almost all are open source)

47 Research Directions

48 Level 1 Research Foundation routines for fraud detection
Development, testing, empirical use, field studies Connections to production software Standard SAP, Oracle, Peoplesoft, JD Edwards, etc. modules Application of CS, statistics, other techniques to fraud detection Time series analysis Pattern recognition for fraud detection

49 Level 2 Research Studies about detectlet presentation, user interface Creation and testing of detectlets for industries, data schemas, etc. Detectlets for financial statement fraud detection Testing of detectlet vs. traditional ACL-type fraud detection Patterns of detectlet development, best practices

50 Automatic mapping of field schemas to a common schema
Level 3 Research Automatic mapping of field schemas to a common schema Application of expert system, learning models for automatic detection Decision trees Classification models Meta-detectlets to combine various Level 2 detectlets into higher-level logic

51 Group-oriented processes for the central repository
Other Research Group-oriented processes for the central repository Searching, categorization Testing, rating systems Marketplaces for detectlets Development of Picalo itself

52 In 5 years we’ll have a large repository of detectlets to:
My Hope In 5 years we’ll have a large repository of detectlets to: Support both external and internal auditors Teach students in fraud classes Conduct theoretical and empirical research


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