Financial FINDINGS AND OBSERVATIONS

Throughout this Report and its attachments the terms “Estate” or “HMC” refer to the entirety of the Estate of Health Maintenance Centers, Inc. within the context of the previously referenced matter, unless otherwise indicated.

The Findings and Observations section summarizes the salient results of the Forensic Accounting Analysis executed by financialforensics®. The results are categorized into Empirical and Experiential subject areas and are supported by the Report body.

Findings and Observations

The summarized results of this Report section are indicated below, and are supported by the details contained in corresponding remainder of this section. The results are categorized into Empirical and Experiential segments for ease of reference.

The Empirical results are driven by objective, quantitative and statistical indications, and the Experiential results are driven by experience-based benchmarks. The Experiential results reflect the conclusions that parties with similar skill, knowledge, experience, education and training would reach upon their review of HMC's financial dataset.

Empirical Results

The following Findings and Observations derived from empirical analysis that provided objective conclusions. They do not necessarily comprise the universe of findings, but are sufficiently illustrative for the Court’s consideration in order to reach conclusions consistent with the Receiver.

q Benford’s Law – The results of Benford’s Law is explained in this section of the Report. Benford’s Law is the DNA-equivalent technique for financial analysis of financial datasets. The results of the Benford’s Law analysis clearly indicate that a significant portion of HMC’s financial dataset contains artificial numbers. The artificial, or contrived, numbers are evidenced by the vast amount of duplicative and rounded entries.

q Numeric Tests – The Numeric Duplication Test and the Rounded Numbers Test are two additional tests that can be performed on numeric data sets. The Numeric Duplication Test is designed to identify abnormal duplications of certain numbers. The Rounding Test is designed to identify abnormal occurrences of rounded numbers and multiples. Due to the crossover apparent during the initial testing, the two tests were performed simultaneously. The results of these numeric tests are detailed in this section of the Report.

q Velocity of Funds – The velocity of funds through HMC was very high, thus funds received by HMC, e.g. from Investors was rapidly used, e.g. expenses and thus flowed out of HMC very quickly. Such observation is further supported by related findings, including:

§ Interest Income was very low relative to the funds flowing through HMC. Specifically, HMC recorded only $3,910 in Interest Income for the 6 years of its existence, despite handling more than $100,000,000 in cash inflows from all sources.

§ Interest Expense was very high relative to the funds flowing through HMC. Specifically, HMC paid $985,938 to lenders for the 6 years of its existence, despite handling more than $100,000,000 in cash inflows from all sources. Also, note that HMC borrowed over $2,500,000 and repaid very little principal during the 6 years of its existence.

§ Uncategorized Expenses – HMC classified $18,795,478 as Uncategorized Expenses, by management’s direction and/or default, thus indicating that insufficient time and attention was devoted to basic financial record keeping.

§ Intercompany Transfers – HMC recorded an enormous amount of Intercompany transactions including bank transfers and expenditures paid on behalf of other entities during its 6 years of existence, thus indicating a constant need to shuttle funds within/among the various entities to cover expenses. Often times expenses were paid from whichever bank account had sufficient funds at the time. The related inter-company liability was not recorded nor recognized until Pederson and Kim began reconstruction of the accounting records.

§ Hundreds of NSF Checks – HMC paid hundreds of NSF overcharges through its numerous bank accounts during the 6 years of existence, despite handling more than $100,000,000 in cash inflows from all sources. This indicates that insufficient time and attention was devoted to basic financial record keeping that could have avoided such charges.

§ NSF Payroll Checks – HMC experience numerous occurrences of NSF Payroll checks during its 6 years of existence, despite handling more than $100,000,000 in cash inflows from all sources. This indicates that insufficient time and attention was devoted to basic financial record keeping that could have avoided such occurrences.

Benford’s Law

Benford’s Law is an analytical technique identified in the late 1800s and developed during the 1920s by Frank Benford, a physicist at General Electric research laboratories. He noted that the first few pages of logarithm table books were more worn than the later pages. In those days, logarithm table books were used to accelerate the process of multiplying 2 large numbers by summing the log of each number and then referring to the table for the requisite integer.

Benford’s Law states that digits and digit sequences in a dataset follow a predictable pattern. The technique applies a data analysis method that identifies possible errors, potential fraud or other irregularities. For example, if artificial values are present in a dataset the distribution of the digits in the dataset will likely exhibit a different shape (when viewed graphically), than the shape predicted by Benford’s Law. Benford proved his theory by using 20 lists containing 20,229 numbers, and produced the statistical array that is still applied today.

The technique counts digit sequences of values in the dataset (21,238 payment records) and compares the totals to the predicted result according to Benford’s Law. Non-zero digits are counted from left to right.

Despite its origin in the 1920s, Benford’s Law was not recognized as an effective tool for audit and fraud analysis until the late-1990s. Based upon our analysis of HMC’s financial dataset it matches the data conformity necessary to apply Benford’s Law as summarized below:

q The data set represents the sizes of similar phenomena.

q The data set must preclude built-in minimum or maximum values.

q The data set does not represent assigned numbers.

The analysis of the output is based upon 4 Major Digital Tests. The output of the tests and resultant conclusions using HMC’s financial dataset are presented below.

Results of Applying Benford’s Law

Based upon the analysis of Benford’s Law applied against 100% of the foundational transaction entries within HMC it is clear that the transactions failed all 4 tests: First Digit, Second Digit, First Two Digits and First Three Digits. The implications of the failures lead to the following observations:

1. A significant proportion of HMC’s foundational transaction data appears to be contrived.

2. A significant proportion of HMC’s transactions containing “rounded” numbers appears to be excessive.

Major Digital Tests

The digital analytical tests applied through Benford’s Law are comprised of the following:

q First Digits Test - The first Major Digital Test is a test of the first digit proportions, a test for reasonableness. The first digit of a number is the leftmost digit with the understanding that the first digit can never be a zero. For example, the first digit of 7,380 is “7.”

q Second Digits Test - The second Major Digital Test is a test of the second digit proportions, also a test for reasonableness. The second digit of a number is likewise determined by its placement within the number, thus the second digit of 7,380 is “3.”

q First 2 Digits Test – This test is more focused than the 2 preceding tests and uses the first 2 leading digits, again excluding zeros. For example, the first 2 digits of 7,380 are “73” and the first 2 digits of 0.07380 are also “73.” There are 90 possible first-two digit combinations: 10 to 99 inclusive. This test finds anomalies in the data that are not readily apparent from either the first or second digits seen on their own.

q First 3 Digits Test – This test focuses on the 900 possible first 3 digit combinations: 100 to 999 inclusive. This highly focused test indicates abnormal duplications.

First Digits Test

The results of the First Digit Test are indicated by the graph below. Applying the testing criteria indicates that the variations from the predicted norm suggest that anomalies exist throughout HMC’s financial dataset

The preceding graph indicates (among other observations) that the numbers “1” and “2” both exceed the expected counts by 14% and 11%, respectively. Additionally, the numbers “4” and “6” fall below the predicted limit, thus suggesting that anomalies exist within HMC’s financial dataset.

Second Digits Test

The results of the Second Digit Test are indicated by the graph below. Applying the testing criteria indicates that the variations from the predicted norm suggest that anomalies exist throughout HMC’s financial dataset

The preceding graph indicates (among other observations) that the numbers “0” and “5” both exceed the expected counts by 110% and 61%, respectively, thus suggesting that anomalies exist within HMC’s financial dataset. For example, an inordinately large amount of payments contained “0” or “5” as a second digit such as 10,000 or 15,000.

First 2 Digits Test

The results of the First 2 Digits Test are indicated by the graph below. Applying the testing criteria indicates that the several significant variations from the predicted norm suggest that anomalies exist throughout HMC’s financial dataset.

The preceding graph indicates (among other observations) that the numbers “10”, “15”, “20”, “25”, “40” and “50” all exceed the predicted limit, thus suggesting that anomalies exist within HMC’s financial dataset.

First 3 Digits Test

The results of the First 3 Digits Test are indicated by the graph below. Applying the testing criteria indicates that the several variations from the predicted norm suggest that anomalies could occur throughout HMC’s financial dataset.

The preceding graph indicates (among other observations) that the numbers “100”, “200”, “150”, “250” and “500” all exceed the predicted limit, thus suggesting that anomalies exist within HMC’s financial dataset.

Numeric Tests

The Numeric Tests are comprised of 2 key examinations, e.g. a Numeric Duplication Test and a Rounded Numbers Test. Once any significant duplication has been identified, meaningful inferences can be drawn through further investigation.

The Numeric Duplication Test is used to identify abnormal recurrences of specific numbers. The objective is to draw attention to small groups of numbers that appear to be unusual.

The Rounded Numbers Test operates on the same premises as the Numeric Duplication Test. However, the objective is to identify abnormal recurrences of rounded numbers. Abnormal recurrences of rounded numbers are good indicia of estimation since people tend to estimate when they create contrived numbers.

Results of Applying Numeric Tests

The Numeric Tests were both applied against 100% of HMC’s foundational transaction entries. The implications of the results lead to the following observations:

1. There appears to be significant duplication of numbers.

2. There appears to be significant use of rounded numbers.

The results for both tests have been presented in a combined format on the following page. Only the numeric duplications deemed significant have been presented.

Presented below are some of the findings and observations resulting from the numeric tests and further investigation into the same.

q Many of the debits for $10, $15, $20, $25, $30, $40 and $50 are bank charges. A large sum of these bank charges are NSF fees, wire transfer fees and cashier check fees. From this we can derive that HMC lacked the capability to manage its cash flows, as illustrated by the amount of NSF fees, and that HMC transacted numerous wire transfers and cashiers checks.

q Upon further analysis, it was determined that many of the rounded transactions were cash withdrawals. In some cases, the withdrawals included the bank transaction fee. For example, there are 111 transactions for $301.50. The composite of most of these transactions are a $300 withdrawal with a $1.50 ATM fee. Note that $300 had been the ATM withdrawal limit established by many financial institutions.

q Several of the other rounded transactions, including some larger transactions, were payments made directly to or behalf of defendants and relief defendants.

q Many of the larger rounded numbers are actually inter-company bank transfers. It was commonplace for the individuals in charge to transfer large sums of money between the various corporate accounts.

HMC typically deposited investor funds into relatively few active bank accounts. The funds were then transferred from these accounts to other corporate operating accounts to pay various expenditures including payroll on an as-needed basis.

Such transactions are not necessarily unusual, but upon closer examination of these transactions, it is apparent that there was a lack of planning and accountability pertaining to these transfers. Further, since there were never any check registers kept for any of the entities, the corporate finances were coordinated through the balances in the bank.

q In some instances, the number of duplicate transactions for an amount may not be deemed significant. However, the aggregate value of these transactions has made them noteworthy. For example, there are only two transactions for $700,000 totaling $1,400,000. The aggregate value of these transactions is slightly less than one percent of all debit transactions for the company.

q There were four transactions for $1,000,000. Three of these transactions were payments in accordance with the TBG Development acquisition. The remaining transaction was a wire transfer to a former Znetix employee. The Receiver is currently investigating the underlying purpose of this and other related wire transfers.