IDEA - Info Sys. Exam 2

5) Accounts Receivable are invalid or incorrectly stated.

Accounts could be entirely or partly invalid. Partially invalid accounts may be the result of delays in processing transactions or errors in applying credits and payments to accounts. Fictitious accounts could be due to fraud. Existence, Validity, Accuracy

Overall, what did we learn about our AP Controls (e.g., authorization of vendors, authorization of payments, cancellation of documents)? Which of these Controls are operating effectively?

payments to vendors not on the approved list payments above the threshold --> increases amount of substantive testing we need to do

6) Items are not recorded in the correct period

Financial statements, i.e., inventory and cost of goods sold, could be materially misstated. Cut-off

Detecting Pay and Return Schemes

Check for duplicate payments Check for unusual fluctuations in payments to legitimate vendors Year over year payments to existing vendors

Auditors sometimes provide management letter comments or suggestions to improve operations. What is a suggestion you would make based on the results of your analytics?

Consider making inventory reserve higher - remove obsolete inventory from calc Price movements: - big variance in price from prior year (some were from new products, but other ones should be pointed out to management) <- have you considered the market conditions causing this change?

4) Invoices are paid late.

Delays in processing Accounts Payable approvals can result in a loss of available discounts for timely remittances and understatement of liabilities for a specific period. Cut-off, Completeness

4) Customers are double billed

Double billing can negatively affect customer satisfaction. Also, revenues and receivables would be overstated. Existence, Validity, Valuation

6) Invoices are processed twice.

Duplicate payments can result from the failure to cancel documents to prevent re-use or processing errors in Accounts Payable such as restoring a backup file twice. Existence, Validity

Pay and Return Schemes

Fraudster double-pays, overpays, or "mistakenly" pays a legitimate vendor Fraudster asks the vendor to return the extra amount to them Fraudster pockets the returned payment

Shell Company Schemes

Fraudster forms a fake company and a bank account in the fake company's name (fraudster is a signer) Fraudster submits fake invoices from the fake company Fraudster approves the fake invoice or "tricks" someone into approving it for them Company submits payment to the fake company's bank

What was wrong with the AR file the client gave you? How did you fix it? How did IDEA help you keep a good audit trail?

Problems: - Items in the database went past the cutoff date - invoices that had been paid were still in the AR database Fix: - ran an extraction to create a "child database" with the cleaned data you actually wanted (once you cleaned up the data, it reconciled with the financial statements) Good audit trail: - it automatically tracks a history of everything you did - creating the "child database" keeps all the original data without deleting anything, but still creating a cleaned database

Stock-Outs

Not having enough inventory to fulfill orders Timing of when to order more inventory is an important operational consideration for retailer and distributors A "value-added audit" might provide management with insights on this and other operational issues Auditors would share their thoughts in a management letter.

What would make us question the inventory reserve?

Old inventory Slow moving inventory Excess Inventory

2) Payments are made to individuals or employees.

Payments made to individuals or employees could represent a diversion of company payments, indicating fraud. Existence, Validity

Detecting Shell Company Schemes

Payments to unauthorized vendors Payments just below known approval thresholds Payments that look unusual - residential address, to individuals, on weekends, round numbers, unusually quick payments

7) Payments are made in a way to be undetected by audits.

Perpetrators of fraud may arrange payments to avoid detection. For example, large amounts may be split into several smaller payments to coincide with a perpetrator's transaction approval limits or avoid limit checks on large payments. Existence, Validity

What is Benford's Law and why was it useful in this simulation?

some naturally occurring numbers have a property where the leading digit has certain probabilities of being in that leading digit spot in the simulation: there are an unusual number of payments in the $70,000's this is curious because the threshold for approval is $80,000

What concern did we identify with the client's automatic reordering system? Would you classify this concern as an operational risk or a risk to the reliability of the financial statement? Why?

There is both a operational and financial risk we found inventory levels below min and above max - the lower quantities seem to be an operational issue: risk of stockout - inv. above max potentially relates to the obsolete inventory which could be a financial risk it can be manually overridden: bad for controls - are they even using manual re-order? - we should recommend that management digs into this a bit deeper

We calculated a revised (or alternative) inventory allowance. In doing so, we subtotaled by depot. What is a possible reason why we subtotaled by depot?

They wanted to see if different depots (locations) have more obsolescence. maybe the obsolete items are congregated in a certain area/location

IDEA made it easy for us to select a sample for confirmation. Did we select a "smart" sample? If no, what could we have done differently?

*good things about the sample:* - starting point was randomly generated and eliminated human bias in the sample selection process (a truly random sample is necessary if you want to make a statistic conclusion) *bad things about the sample:* - we really only got part of the piece of the puzzle to get a statistical conclusion: couldn't enter sample size? - we might have wanted to get a stratified sample (i.e. higher value items) - alternative to selecting invoices for confirmation: selecting a sample of customers and asking them the amount they owe

Analysis

- Age inventory by date of receipt. - Compute the number of months each inventory item is held based on either sales or purchases. Produce a summary of this information. - Stratify balances by value bands. - Analyze gross profit. - Analyze price adjustment transactions.

Matching and Comparison Tests

- Compare files at two dates to identify new or deleted inventory lines or to identify significant fluctuations in cost or selling price. - Compare cost and selling price and identify items where cost exceeds net realizable value. - Compare holdings and inventory turnover per product between stores.

Matching and Comparison Tests

- Compare the balance on an account with its turnover. - Match the sales transactions to the Customer Master information to identify sales to new or unauthorized customers and those with exceeded credit limits. - Compare to Accounts Payable for possible contra accounts.

Exception Tests - Existence and Valuation

- Identify and total inventory held longer than maximum and minimum inventory levels. - Identify and total obsolete or damaged inventory (identified as such in the database). - Identify balances greater than a reasonable usage period that are probably obsolete. - Identify items past their shelf life (if a sell by date or bought date is present on the system). - Identify any items with excessive or negligible selling or cost prices. - Identify differences arising from physical stock counts. - Test for movements with dates or reference numbers not in the correct period (cutoff). - Identify balances that include unusual items (i.e., adjustments). - Identify work in progress that has been open for an unreasonable period. - Identify inventory acquired from group companies.

Exception Tests - Existence and Valuation

- Identify old items (i.e., greater than three months old). - Identify large balances individually or compared to turnover. - Select accounts for which no movements have been recorded in a set time. - Report credit balances. - Identify unmatched cash or credits. - Compare balances with credit limits and report exceptions (i.e., accounts with balances above their credit limits or accounts with no credit limits etc.). - Test for items with invoice dates or numbers outside the expected range. - Identify partial payments of debts. - Identify invalid transaction types. - Identify customer addresses that are "care of" or flagged not to be sent out.

Exception Tests - Existence and Validity

- Identify payments to unauthorized suppliers by matching the payments and authorized suppliers list. - Search payments file for payees without suffixes such as "Inc", "Ltd", or "Co" in their name to identify payments to individuals. - Test for large discounts. - Test for duplicated invoices using value and supplier code as the key fields for one test and purchase order number for another. The second processing of invoices can be used to establish a value on the Profit/Loss (P/L) to make a fraudulent payment. (This will also pick up accidental duplication.) - Identify payments made on Sundays or other days/dates that are not valid. - Examine to see if amounts are being approved at or just below break points in authority level by a value distribution across the whole ledger. If approval authority is not directly available, perform subsidiary analysis by types of supplier or approving department (i.e., marketing). - Look for split invoices to enable approval to be kept by an individual. Extract all invoices within 90% of an approved limit (preferably for a suspected manager or department) and search for all invoices from that supplier. Sort by approving manager, department and date to identify possible split invoices or summarize payments by invoice number to determine how many partial payments have been made for each invoice. - Tests for total payments in year exceeding previous years by more than 25%. - Test for large one-off payments to suppliers. - Using the first five or six characters of the name, match supplier names against a list of employee surnames from a payroll or personnel file. - Test for similar supplier names. - Test for incomplete or unusual supplier details.

Analysis

- Profile debtors using a numeric stratification to see the number of large debts and what proportion of value is in the larger items. - Produce an aged debt analysis. Consider how to deal with unallocated cash and credit notes. IDEA, by default, ages these on their date rather than allocating against the oldest item or any other treatment. It is often worthwhile splitting the file into invoices, unallocated cash, etc. using multiple extractions, and then aging the individual files.

Sampling

- Select samples (random or specific) for functional testing and confirmation (and produce confirmation letters).

Analysis

- Stratify the size of payments and extract any exceptionally high payments. - Analyze payment days and identify suppliers with favorable payment terms. - If the computer system captures the approving authority for a transaction, examine the value distribution for each manager.

Gaps and Duplicates

- Test for duplicate invoices (both invoice number and customer/value). - Use duplicate exception testing for less obvious input errors, such as the same vendor ID assigned to two different vendor names, the same vendor name assigned to two different vendor IDs, payment of the same invoice number and amount to two different vendors, etc.

Gaps and Duplicates

- Test for missing inventory ticket numbers. - Test for missing transaction numbers. - Identify duplicate inventory items.

Mechanical Accuracy and Valuation

- Total the file, providing sub-totals of the categories of inventory. - Re-perform any calculations involved in arriving at the final stock quantities and values. - Re-perform material and labor cost calculations on assembled items.

Mechanical Accuracy and Valuation

- Total the file. It often pays to separate debits and credits. - Revalue foreign payables, if applicable. - Check transaction totals to the balance on each account.

Mechanical Accuracy and Valuation

- Total the file. It often pays to separate out debits and credits. - Revalue foreign debts, if applicable. - Check transaction totals to the balance on each account.

Potential Risks - Inventory

1) Inventory is not correctly recorded 2) Inventory management reports have inadequate supporting information. 3) Quantity of inventory is not maintained within the specific range 4) Obsolete inventory items are not identified 5) Differences between the physical inventory and the inventory on the system are not identified 6) Items are not recorded in the correct period 7) Gaps in sequentially numbered documents are not accounted for

Key AP Control risks

1) Payments are made to unauthorized suppliers. 2) Payments are made to individuals or employees. 3) Unauthorized premiums are given to suppliers. 4) Invoices are paid late. 5) Invoices are paid on irregular dates. 6) Invoices are processed twice. 7) Payments are made in a way to be undetected by audits. 8) Items (e.g., purchase orders, checks) are missing. -------- From PPT: Vendors are authorized. Payments are authorized. Documents are cancelled to prevent reuse. Documents are pre-numbered and the series is accounted for periodically.

Key AR Control Risks

1) The file is incorrectly consolidated or summed. 2) Foreign currency transactions are not translated correctly 3) Credit is granted to customers that are likely to default 4) Customers are double 5) Accounts Receivable are invalid or incorrectly stated. 6) Improper allocation of credits and payments. 7) Accounts Receivable is not properly aged. 8) A significant percentage of the receivables is concentrated in a few customers. 9) Improper classification of amounts.

6) Improper allocation of credits and payments.

As mentioned earlier, improper allocation of payments to accounts could affect the aging of the Accounts Receivable and this would affect management's ability to determine an effective course of action for handling the customer's account. For example, sales to customers could be blocked or the customer may be sent to a collection agency, even though the customer is current on their accounts. Improper allocation of payments to accounts could also be an indicator of fraud. Existence, Validity, Accuracy

Benefits of using IDEA over Excel

Automatically creates a log of everything you did No file size or record count limitations All original data is "read only" (you can't "fat finger" and mess up your data) Can important almost any type of data (e.g., even PDFs of contracts!) Has various "built in" data analytics tools

When uploading AR data to IDEA...

Care must be taken to ensure AR statements have been produced for all accounts by totaling all items and checking that this agrees to the General Ledger control account.

Using IDEA

IDEA is generally used for substantive testing. When there is a need to determine if items are in error, a problem needs to be quantified, or certain items identified, then IDEA should be used. IDEA can also help with checking procedures. An indirect way of checking procedures is to draw an inference about the effectiveness of procedures based on the results of substantive tests; e.g., it may be inferred that if there are no errors then the procedures must be working. Conversely, if there are errors then it may be inferred that procedures are being applied incorrectly and that controls are not effective. IDEA can also be used when checking computer-based controls by re-performing edit checks, matching and other computer-based procedures. use IDEA in audits where: - There is a core purpose or reason - There are a reasonable number of records - There is a depth of information on the items - The data transfer is technically feasible at a reasonable cost and volumes can be accommodated on computers or networks

9) Improper classification of amounts.

If a credit balance is classified as an Accounts Receivable (AR) instead of an Accounts Payable (AP), then it could distort the current ratio which could be part of a debt covenant. Presentation

7) Accounts Receivable is not properly aged.

If the aging of the Accounts Receivable is not correct, then management may fail to act on overdue accounts in a timely manner and permit sales to poor credit risks. Also, the calculation of the allowance for doubtful accounts and bad debts expense would be affected. Valuation

3) Quantity of inventory is not maintained within the specific range

If too much inventory is maintained then there is an increased risk of obsolescence and additional storage costs (i.e., for keeping the inventory). If too little inventory is maintained, then the company will not be able to meet the customer demand. Valuation

Why might regulators be concerned about this practice (of adding value)?

If we are too focused on adding value, we can go over the independence line

7) Gaps in sequentially numbered documents are not accounted for

Inventory tags, transaction numbers and other information may be missing and not accounted for. This would undermine the control that is provided by inventory tags. Completeness

5) Invoices are paid on irregular dates.

Irregular payments may reflect processing errors or fraud. Existence, Validity

What does IDEA's report reader tool do for auditors? What problem did report reader help us solve in this simulation?

It let us put text documents into the database and also lets us extract that data and put it into a usable format

The workbook noted a potential problem with interpreting average MONTHS (and with looking at averages more broadly). What is it and how did we help solve it?

It might be skewed by very high values in certain categories - average months was 9, but we know by talking to the CEO that inventory reaching 9 months is pretty old - the histogram we made shows: 80% of the items are actually 6 months old but the really old inventory is pulling our average up looking at the formula results and the histogram together will help account for variance and give you a better picture

1) The file is incorrectly consolidated or summed.

Items could be omitted, or the listed items may not be included in the totals reported in the financial statements. The Accounts Receivable could be overstated or understated depending on the direction of the error. Completeness Accuracy

2) Inventory management reports have inadequate supporting information.

Management may identify issues but may not be able to "drill down" and identify the root cause of issues. This, in turn, hampers management's ability to make effective decisions. Existence, Validity, Valuation

2) Foreign currency transactions are not translated correctly

Management may not be aware of the impact of transactions in foreign currency and may fail to take steps to manage currency risks. The Accounts Receivable could be overstated or understated depending on the direction of error. Accuracy

4) Obsolete inventory items are not identified

Management will not be able to make proper decisions on getting rid of obsolete items. From an audit point of view, obsolete items may need to be revalued if the market price is less than their cost. Valuation

1) Inventory is not correctly recorded

Management will not have accurate information to manage inventory (i.e., ordering) effectively. Inventory could be materially misstated on the financial statements Accuracy, Existence, Validity

5) Differences between the physical inventory and the inventory on the system are not identified

Management will not have reliable information to make inventory management decisions. Also, it may have a problem in terms of identifying shrinkage. From an audit perspective, the inventory could be materially misstated on the financial statements. Existence, Validity

Benford's Law

Mathematical algorithm that accurately predicts that, for many data sets, the first digit of each group of numbers in a random sample will begin with 1 more than a 2, a 2 more than a 3, a 3 more than a 4, and so on. Predicts the percentage of time each digit will appear in a sequence of numbers.

suggested *Audit Tests* when auditing an Accounts Payable system.

Mechanical Accuracy and Valuation Analysis Exception Tests - Existence and Validity Gaps and Duplicates

Potential Tests - Inventory

Mechanical Accuracy and Valuation Analysis Exception Tests - Existence and Valuation Gaps and Duplicates Matching and Comparison Tests

Potential Audit Tests when auditing AR System

Mechanical Accuracy and Valuation Analysis Exception Tests - Existence and Valuation Gaps and Duplicates Matching and Comparison Tests Sampling

most common type of fraud in an organization

Purchase/payments fraud is probably the most common type of fraud in an organization. It may be the simple submission of a false invoice, the reuse of another valid invoice, withholding of a credit note, or a more complex arrangement.

AP Fraud Schemes

Shell Company Schemes Pay and Return Schemes

8) Items (e.g., purchase orders, checks) are missing.

Since Accounts Payable are often not authorized for payment until there is a three-way match of purchase order, receiving document and supplier invoice, missing documents could result in Accounts Payable being understated. Completeness

Gaps and Duplicates

Test for missing items or gaps in the check number sequence.

The First Digit test

The First Digit test is the test of first digit proportions. The first digit of a number is the left-most digit in the number. Zero can never be a first digit. This is a high-level test. Analysts will not usually spot anything unusual unless it is blatant. This is a test of goodness-of-fit to see if the first digit actual proportions conform to Benford's Law. The First Digit test is an overall test of reasonableness. The upper and lower bounds are merely guidelines for the auditor. The First Digit graph could show a high level of conformity, but the data set could still contain errors or biases.

The First Two Digit test

The First Two Digit test is a more focused test. The first two-digit numbers are the left-most two digits. There are 90 possible two-digit combinations ranging from 10 to 99. This test is performed to find anomalies in the data that are not apparent from either the First Digit test or the Second Digit test when viewed on their own. A spike occurs where the actual proportion exceeds the expected proportion as predicted by Benford's Law. Positive spikes (above the Benford's curve) represent excessive duplication. One of the objectives of this test is to look for spikes that conform to internal thresholds, such as authorization limits.

8) A significant percentage of the receivables is concentrated in a few customers.

The business could be exposed to a combination of credit and liquidity risks if these large customers do not pay their debts in a timely fashion. Also, the company may be deemed to be economically dependent on the identified customers, and this may need to be noted in the financial statements. Presentation

3) Credit is granted to customers that are likely to default

The business will sell goods to parties from which they will not be able to collect cash. This has potential implications on liquidity and bad debt expenses. Valuation

Data-based Conditions for Benford's Law --> Description of the Same Object

The data must describe the same phenomenon. Examples are: - The population of cities - The surface of lakes - The height of mountains - The market value of companies quoted on the New York Stock Exchange - The daily sales volume of companies quoted on the New York Stock Exchange - The sales figures of companies

Data-based Conditions for Benford's Law --> Unlimited Data Space (Non-Existence of Minima and Maxima)

The data must not be limited by artificial minima and maxima. A limitation to exclusively positive numbers (excluding 0) is permissible if the figures to be analyzed do not move within a certain, limited range. This applies, for example, to price data (e.g., the price of a case of beer will generally always range between 15 and 20 dollars) or fluctuations in temperature between night and day.

Data-based Conditions for Benford's Law --> No Systematic Data Structure

The data must not consist of numbers following a pre-defined system, such as account numbers, telephone numbers and social security numbers. Such numbers show numerical patterns that refer to the intentions of the producer of the number system rather than to the actual object size, represented by the number (e.g., a telephone number starting with a 9 does not mean that this person possesses a bigger telephone). Basically, data complies best with Benford's Law if it meets the rules mentioned above, namely that the data consists of large numbers with more than 4 digits and the analysis is based on a sufficiently large data supply. A large data supply is necessary to come as close to the expected numerical frequencies as possible. For example, the expected frequency of the digit 9 in any data supply is 0.0457. If the data supply consists of only 100 numbers, the numbers which have a 9 as their first digit may be 5% of the data supply. Thus, in the case of a small data supply, there may be an over-proportional deviation from Benford's Law. In large data supplies, the numerical distribution is increasingly closer to the expected frequencies. If the data supply has, or just roughly has, the characteristics mentioned above it can be analyzed based on Benford's Law. However, the results of the Benford analyses are not interpretable based on Benford's Law. As stated before, the expected frequencies according to Benford's Law often represent, in the practical use, nothing more than a type of benchmark for the observed frequencies. Since the observed frequencies will only be compared with the legality discovered by Benford, not interpreted accordingly, it is not necessary that all conditions mentioned above be met. In fact, the analysis results will help the auditor interpret the personal expectation of the auditor, without including the reference value according to Benford in the argumentation. If, for example, the personal expectation of the user is that the starting digit 4 must occur twice as often in the analyzed data than the starting digit 2, the results of the analyzed values must not be compared with the expected frequencies according to Benford but with the individual expectation of the user. The application of Digital Analysis and the Benford Module is also permissible in the framework of Data Mining when certain distinctive facts in a data supply are measured against the personal expectations of the user and interpreted according to them. In this case it is not necessary for the data that is to be analyzed, to create a Benford Set in a strict sense. In fact, it is permissible under these circumstances to analyze the numerical distribution of the leading digits of each data quantity and to interpret it independent of Benford's Law.

Data-based Conditions for Benford's Law --> Geometrical Series

The mathematical pre-condition for the examination of a data supply based on Benford's Law is that the data supply is based on a geometrical series (i.e., it is presented as Benford Set). This condition is rarely met. Experience shows; however, that data must only partially meet this condition, i.e., the constant increase, percentage-wise of an element compared to the predecessor must only be met partially. Otherwise, this would mean that no number may occur twice which is quite improbable in the case of business data supplies, however, the pre-condition is that there is at least a 'geometrical tendency'.

We calculated "MONTHS" using an a conditional formula. Conceptually, what did this formula accomplish? Why did we save the formula?

Trying to get a measure of how many months of inventory they have on hand (assuming recent sales are indicative of future sales) @if(USAGE=0, @Age("20151231", DELDATE)/30, QTY/ usage/12)) If there is no usage, the formula takes the number of days between the delivery date and the year-end divided by 30 (days in a month). This calculates the number of months since the last delivery date. If there is usage, the formula divides quantity (inventory on hand) by the usage(amount sold in the year) divided by 12. This calculates the months of inventory on hand as a ratio of the quantity on hand to the monthly usage. (in both cases, higher months are indicative of higher obsoleteness) If there were no "0" usages, we could just use the last part of the formula: QTY/ (usage/12) Because this is a complex equation that may be used again or may require editing in the future, it is beneficial to save it for ease of re-use and to speed up future calculations requiring the same or similar output.

3) Unauthorized premiums are given to suppliers.

Unauthorized premiums may represent overpayments to suppliers in return for kickbacks. Existence, Validity

1) Payments are made to unauthorized suppliers.

Unauthorized suppliers could represent former suppliers that supplied goods or services of unacceptable quality and should have been removed from the list of suppliers; or they could be fictitious suppliers set up by dishonest personnel to receive automated payments. Payments made to unauthorized suppliers could therefore represent either error or fraud Existence, Validity

Related Inventory Assertions

Valuation

The First Three Digit test

Where there are 90 possible two-digit combinations, there are 900 possible three-digit combinations from 100 to 999. The First Three Digits test is a highly focused test that will give the analyst relatively smaller sections due to abnormal duplication and allow for a more narrowly focused analysis. This test is also valuable to look for spikes just below internal and psychological thresholds such as authorization limits. To make the most effective use of this test, the source data set should normally exceed 10,000 records.

One of the key tests is to perform for AR is...

a confirmation by choosing the required balances and sending out letters. This requires customer master information such as addresses and credit details (usually held in a master file) as well as the transactions.

Items of concern for AR are...

old invoices, unmatched cash, and large balances, particularly where customers are in difficulty. These can all be identified using exception tests.

What types of screens (or filters) did we apply to the population of payments? Did we "audit the whole population?"

filters: unusually high payments payments with "cash" in the payee name payments right under threshold amounts Sunday payments round dollar transactions transactions authorized by HMV did we "audit the whole population?": we defined some specific attributes

When auditing Accounts Receivable, the main objective is to...

form an opinion on the validity of the debt.

Why are auditors focused on "adding value?"

get an edge on competitors attract new business retain existing clients charge more

Are we suspicious of fraud? Could we prove to a jury that there is fraud?

intent is hard to prove we have some transactions that cause concern, but are not necessarily fraud each firm has a forensic division --> this might be a good time to involve them

Bonus: Based on the reading, are there any other data analytics you would have liked to perform to help diagnose if fraud is present?

more testing regarding employee info and supplier information i.e. email addresses, actual addresses comparing year over year

What did our "PAY_DAYS" calculation show us? Are there any innocuous explanations for what we found? Should we be concerned?

some payments were paid early some payments were paid before the invoice was even processed potential explanations: in some cases, vendors have payment terms that don't match the company'd terms Also, the company may deviate from their policy if there is a good discount incentive Early payment could be a prepayment

What is a major limitation of the analysis described in Question #4?

the master file and all of the documents were received from the client: AR database reconciled to the financial statements so we had more comfort with that Credit Master File: we have no idea if the credit limits were adequately set, we have no idea if it is a complete data set, etc.

We "slammed" two reports together - the Customer Master File and the AR database. What did we learn about the company's credit check control and what is the likely impact on our audit?

the sales to customers who were not in the master file and didn't have a credit limit were concerning the control should have been also verifying that customers are authorized to have credit to begin with impact on audit: if we can't rely on the control, we need to do more substantive testing