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Predictive audit: transactions status prediction
Abstract
In a new business era, economical storage and inexpensive processing cost expand the
need of data assurance and the application of analytics in the audit field. Continuous auditing
facilitates auditors to perform more frequent audit with full population, not just sampling. Thus,
it could fulfill business needs. Furthermore, auditors are required by SAS 99 to assess the risk of
material misstatement due to fraud. Continuous auditing and continuous monitoring could alert
auditors to irregularities found in a live stream of economic data. This allows management and
auditors to investigate any potential problems before they escalate. The objective of this paper is
to identify anomalous business transactions and provide an alert beforehand. Predictive audit
could identify the area that has a high possibility of anomalies or fraud. One of the major issues
in sales and revenue cycle is channel stuffing. Employees may try to boost sales to raise their
performance and compensation. At the time of sales, a number of variables, such as sales
employees’ performance and product’s attributes, can be used to predict the status of that sales
transaction in the future. If the sales transaction has particular characteristics, it has a potential to
be either a normal transaction or a problematic one. Several machine learning techniques,
including decision trees, logistic regression and support vector machines, are applied to the sales
transactions data. A set of indicators that predict whether that sales transactions will be cancelled
in the future is identified and the predictive ability of models are compared.
1. Introduction
In a real time economy, businesses operate 24/7, and companies extensively adopt
integrated software and modern technologies (Vasarhelyi et al. 2010). Internal auditors and
external auditors have to adapt themselves accordingly. Audit timing, nature and extent are more
rigorous. In traditional audit, auditors have to sample data or document testing and generalize
results to the population. This is due to limited access, limited resources, and lack of technology.
Additionally, the role of the auditor has changed overtime. In addition to financial statements
audit, auditors have to monitor internal controls and company’s core activities and operation
processes, for example, to serve SOX requirements. As businesses become more complex and
continue expanding, auditors face a vast amount of complicated data to be processed and
analyzed. The emerging of continuous auditing and continuous control monitoring could greatly
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facilitate audit works in real time economy. Auditors could do audit testing more frequently with
the entire population and in a more timely basis. Continuous auditing is able to process full
populations ofl data without resorting to sampling. Transaction verification components in the
continuous audit are able to filter the exceptional transactions and bring in auditor and
management attention (Kogan et al, 2010).
Apart from the conventional audit with a pile of paper documents, a number of manual
testing and a periodic review, internal auditors now can utilize more technology enabled tools to
support their wors. Automated audit testing began in the 1960s with embedded audit module
methodology. In 1980s, computer assisted audit techniques (CAATs) were deployed for
substantive tests on a large electronic data set (Coderre, 2006). Vasarhelyi and Halper (1991)
developed a continuous auditing and continuous control monitoring system to monitor a large
paperless real-time billing system at AT&T Bell laboratories. In 2000, Glover et al. conducted a
survey of software usage trends of internal auditors worldwide with 2,700 Institute of Internal
Auditors (IIA) members. They found that the usage of software to extract and import data is
rapidly increased from those of 1998 survey. Almost half of the respondents use continuous
monitoring software to find trends, to create exceptional report, to detect fraud and to locate
duplicate transactions. This revolution shows that technology has an important role to assist in
many types of audits. Continuous auditing and continuous control monitoring changes the audit
paradigm to more frequent review and automated audit where possible. It also facilitates auditors
to cover full population testing, not just a sample and can produce more timely report to support
management decision and concern. According to an audit maturity model developed by
Vasarhelyi et al. (2010, working paper), a mature continuous audit stage has critical meta-control
structure and benchmark, and auditors will execute the audit only by exception. The system will
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operate on an assured mode and have a warning or an alarm to call attention of auditors and
management in case that irregular activities or transactions are found.
Applying technology to the audit work would facilitate internal auditor especially in the
data rich modern age. Correctly understanding and proper analysis of date would uncover
interesting pattern, trend, weakness or irregularity of data and processes. The objective of this
paper is to identify anomalous business transactions and provide an alert beforehand. This
“predictive audit (Vasarhelyi et al, 2011)” could help auditors and management to block a
problem before it spreads. It is better to look forward to a potential problem, and maybe block it,
than to just look back at erroneous historical data. At the time of sale, indicators can be used to
predict the status of sales transactions. The sales transaction particular characteristics, determine
its potential to be either normal or problematic.
2. Background
More timely auditing and monitoring would help the company detect and resolve errors,
problems or anomalies before they go beyond control. It would be of great help if auditors and
management got timely warning of suspicious activities. With continuous auditing and
continuous monitoring, the systems will create alarms that alert internal auditors or relevant
persons to the deviation of control from the baseline. The alarm and warning system could be
implemented in any business process to assist auditors and management in monitoring the
systems. This paper illustrates the application of this feature to a front office operation such as
sales activities in a revenue cycle.
In recent years, fraud is a growing concern for companies and regulators. SAS 99
emphasizes this importance by requiring auditors to assess the risk of material misstatement due
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to fraud. The Enron case, for example, showed that untimely fraud discovery could allow the
company to fail. Vasarhelyi et al. (2002) suggested that continuous auditing would be able to
detect the abnormal nature of Enron’s special purpose entities and could alarm auditors and
management in a more timely. Business processes that encompass money are more prone to
fraud than other areas. Sales in a revenue cycle is another important area in business processes
that is closely involved with money. Many companies have incentive programs with cash
compensation based on sales. Thus, there is a possibility that employees may try to fraudulently
increase their sales to enhance compensation. Sales transactions could properly be cancelled and
reimbursed afterwards if customers for many reasons. These cancellation and reimbursement
may be the illegitimate ones if sales employees try to unethically boost sales for reaching targets
or cash bonuses. One form of sales exaggeration for a period is of channel stuffing. Sales people
of the company in this study get compensation based on their total number of sale transactions.
Thus, they may try to increase a number of sale transactions by several ways. For example, sales
employees may sell a product which has low monetary value for many transactions and let
customers cancel later after a certain period. Customers can cancel the transactions and get
money back or they can complain about the products and request for reimbursement. These
cancelled sales are not debited from the bonuses or group targets. Thus, the company incurs in
extra costs. These could be avoided if there is some form of predictive screening.
This study aims to predict the sales transaction status at the time of customer purchasing.
When a customer purchases a product, as soon as information entered into the system, it could
predict the future status of that sale transaction, in particular, whether it will be cancelled or not.
Each sale transaction has different characteristics including the properties of the transaction
itself, seller characteristics, and buyer qualifications. Accordingly, by examining all relevant
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factors together, if there is any suspicious transaction, determined by a pre-determined numeric
“suspicion threshold” the system could block its processing and pass it over to audit/ operational
personnel for approval and include it on a warning or alarm report. Several classification
techniques in machine learning are used to create prediction models to predict the outcome of the
sale transaction.
Research questions development
Audit by exception allows auditors to plan and execute work based on internal control
evaluation, and to focus more on specific areas that need attention. Generally, the system is
considered materially correct until an alarm arise (Vasarhelyi et al. 2004). The sooner
management or auditors acknowledge ae problem, the earlier the problem can be resolved at
reduced cost to the organization. AT&T Bell laboratories implemented continuous assurance
model called Continuous Process Auditing System (CPAS) to monitor a real time billing system.
It triggers
alarms which will be selectively escalated to auditors and management when
transactions exceeds a predefine threshold or certain events occur (Vasarhelyi and Halper, 1991).
An ability to predict or estimate conditions facilitates management and auditors’ works.
Management could operate business or implement internal control on a preventive manner, while
auditors could execute audits as a predictive audit rather than a detective basis. Several
researchers examine how decision support system helps estimating potential risk, especially in
auditing area, such as audit risk and client risk. Bell and Smith (2002) use a procedure to
evaluate client risk for external audit work on several factors. This would help auditors to
determine whether they will accept the potential client. If risk likelihood can be identified in
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advance or be predicted, it could aid management, auditors or relevant personnel for decision
making or dealing with potential problems.
Most of prior work studied the indicators of potential risks and problems in aggregate
view, very few deal at the transaction level (Kogan et al, 2011). These studies, for example, tried
to predict client risk to aid decision making on client acceptance and to identify audit risk for the
engagement. At the transaction level, this paper proposes the prediction models to forecast
business transaction outcomes. Considering a number of transaction’s attributes, the models will
determine whether the transaction will succeed or fail. The result of the prediction models could
warn management to pay attention to those transactions that have negative results. This leads to
the research question:
What prediction model(s) will more accurately forecast business transaction outcomes?
Prediction models are proposed using several machine learning techniques. Each
prediction model has advantages and disadvantages that have to be traded off. The accuracy of
the prediction result is one of the most important factors to consider the quality of the model. The
precise forecast would benefit auditors and management decision.
3. Literature review
Sales forecast studies
The revenue cycle is one of the most important business areas. Every for-profit company
is very concerned with its sales numbers. The major source of revenue of business is from sales
activities. Thus, sales forecasting is critical for business planning, strategy, supply chain and
more. A number of prior studies examine various perspectives about sales forecasting using
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different techniques and variables. Winters(1960) most classic work, introduces the moving
average exponential model with the seasonal and trend smoothing technique and states that the
desired characteristics of the forecast are quick, cheap and easy. Thus his model includes only
past sales history but does not include any external factors.
Nowadays, more factors are included in the sales forecast models. Current research
concentrate on developing new models and finding more efficient algorithms for sales forecast.
Fisher and Raman (1996) use the historical data of previous products mixed with expert opinion
to do the sales prediction. They propose a new model to estimate demand densities of the fashion
skiwear of the manufacturing company. The success and failure of new launch products is
studied by Garber et al (2004). It is difficult to obtain enough sales data for a new product to
enable reliable sales prediction. The authors include spatial data to render a better prediction.
This spatial information is available since the product was launched and sold. The cross entropy
is calculated as a measure and logistic regression is run to classify the cases. When plotting the
graphs of entropy, a successful product and a failed product apparently have different patterns.
As a result, the model successfully predicts 14 out of 16 products. Fashion products are
challenging to forecast the demand because they have short product lifetimes, long lead time and
fluctuate demand. Moreover, historical demand information is not available.
Chang and Wang (2006) employ fuzzy back-propagation network (FBPN) to forecast
monthly sales in Printed Circuit Board (PCB) industry. FBPN is the integration of fuzzy logic
and artificial neural network algorithms. The stepwise regression analysis and fuzzy Delphi
methods were applied to select variables related to the sales forecast from three domains: market
demand, macroeconomics, and industrial production. The authors found that both stepwise
regression and fuzzy Delphi methods have better performance when include a tendency factor,
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and the fuzzy Delphi technique outperforms the stepwise method. Finally, FBPN is compared
with other three methods, which are Grey forecasting, multiple regression analysis and Backpropagation network. They conclude that among four models, FBPN is the best model with
97.61% prediction accuracy.
Cadez et al. (2001) propose probabilistic modeling to make inferences about individual
behavior (profile) given transaction data from a large data set of individual over a period of time.
The behavior here focuses on the likelihood that individual will purchase a particular item. In
this paper, a model-based approach is applied to the profiling problem. A flexible probabilistic
mixture model for the transactions is proposed and compared with baseline modes based on raw
or adjusted histogram techniques. The data are separated into two time periods for training and
testing. The log-probability (logp score) of the transactions is used to evaluate predictive power
of the models. Customers with relatively high logp score per item are the most predictable ones.
This score can be used to identify interest and unusual purchasing behavior of customers as well.
Data from newly registered automobiles in Germany (1992-2007) is used to test the sales
forecast model by Bruhl et al. (2009). Yearly, monthly, and quarterly data are compared using
multiple linear regressions (MLR) for the linear trend estimation, and support vector machine
(SVM) for the non-linear trend estimation. The results show that the non-linear (SVM) model
outperformed the linear model and quarterly data has the lowest prediction error. The problems
in the yearly model are the very small data set and information content. The major problem for
the monthly model is that most of the exogenous variables used in the model are not collected
monthly, so substitute or average values are used.
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Sales forecast with Machine learning techniques
Machine learning is a computerized technique to learn from the sample of data or
historical information and uses the discovered patterns to predict a new data. Morwitz and
Schmittlein (1992) use the segmentation methods to improve the accuracy of sales forecasting
based on the purchase intention theory. They segment heterogeneous individual groups into
homogeneous subgroups with a hypothesis that consumers are heterogeneous in purchase
intention and the realization of the intentions. Their segmentation methods are a priori, CART,
discriminant analysis and k-mean cluster analysis. The results show that after segmenting
consumers into similar groups the average forecast errors are reduced, and more accurate sales
forecasts are obtained. In this study, when using discriminant analysis as a segmentation method,
the average percentage in forecast error was reduced most.
New and unique products like songs, movies and books usually do not have past
information about sales and availability of the relevant data is not known in advance. In this case,
the data of diverse prior products could be used for preliminary sales forecasting and then update
the forecast later when the data of this product becomes available. Lee et al (2003) use a
hierarchical Bayesian model of the logistic diffusion model to forecast prelaunch weekly sales of
individual song albums and update post launch when sales data becomes available using
sampling/importance resampling algorithm in Bayesian. In the hierarchy, the first level of sales
prediction model is at the album level and the second level is the underlying characteristics of
artists and albums. The study finds that the prelaunch of the album forecast with album
characteristics has a better result than the one without album characteristics, and the forecast
result significantly improves after the first week of sales with the updating data to the model.
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Thomassey and Fiordaliso (2006) develop the hybrid model based on clustering and
decision trees to forecast mid-term sales for a textile industry. The prediction is processed in two
stages. First, clustering is applied to produce sales profiles. Next, each new product is assigned to
a sales profile by decision tree. This methodology uses sales behavior of past products to identify
a possible pattern of the new product, which has no historical data. Using k-means clustering, a
number of clusters are set between 2 and 20. Then, a decision tree, C4.5, is applied to each
different set of cluster results and the absolute error is computed to select the number of cluster
that produces the most accurate classification.
Even though there are several studies in the area of sales forecasting, there seems to be
none that predicts the status of the sale especially at the transaction level applied in auditing
field. Moreover, the main objective of this study is not to predict the future income of the
company, but the sales transaction status, and use the result of sales prediction as an alarm for
internal auditors and relevant business staff to further investigate the irregularity of the sales
transactions and employee performance. There is no prior work that uses sales prediction as a
warning indicator in continuous auditing environment.
4. Data
The data sets are from one of the largest banks in the world. The data consists of sales
related information which are sales and cancellation transactions of a special saving account of
the company and sales employee records. A special saving account offers customers to deposit
an equal amount of money to the bank every period, usually on a monthly basis. Customers can
select how much money (installment) they want to deposit and how many periods in a contract.
An installment could be very small or big amount upon customers’ preference, but this money
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could not be withdrawn before the contract ends. If a customer cancels the contract before the
contract end, the bank will return money to customer at a discounted rate. However, after buying
a special saving account, a customer can cancel, suspend or reimburse a purchase if he or she
does not satisfy with the product. If the bank gets a complaint from a customer, it may decide to
refund those payments.
The data sets are transactions during November 2009 to April 2010. These transactions
are from all branches country-wide. The main sales transaction table (Base_table) has 607,189
records. The data set also includes additional information related to sales transactions in
separated tables. Those tables are;
1. Registration of employees: Personal information about each employee
2. Complains: Customers can make complain to the company if they are not
satisfied with the product after purchase.
3. Reimbursements: When customers are not satisfied with the product, they can ask
to cancel those purchases. In some case, the company gives them the
reimbursement for their purchase. This table contains seven months of
reimbursement data from October 2009 to April 2010.
Even though the total special saving account cancellation transactions during 6 months
period are 6.67% of total sales, the preliminary analysis of cancellation summary by date shows
that they are increasing over the period from approximately 110 transactions per day at the
beginning of the period to 470 transactions per day at the end of period (Graph 1).
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5. Model Development
Channel stuffing is a malpractice to inflate the sales figures of a company by distributing
products to the distributors far more than they can actually sell to customers. This makes a
company look healthier than it really is because of the increasing sales figures and accounts
receivable numbers. Normally, the company will offer long term credit or return condition to the
distributors to make them accept the large amount of goods. In this sales prediction study,
employees may push sales to increase a number of sales transactions and let customers cancel or
reimburse those later. Therefore, the selected variables for sales status prediction are related to
total number of sales, cancellation and reimbursement transactions.
The important criterion to select attributes is that all the selected attributes have to be
known at the time of prediction. For example, a status of the current sale transaction is not
known at the time a customer make a purchase and this is the outcome that will be predicted by
the model. Thus, the current sales transaction and its status cannot be included in the prediction
variables, unlike value and numbers of installment of the current transaction that are known at
the time of sale. Other attributes those available at the time of purchase are past performance of
sales employees, for example, total sales transactions that an employee sold in the past and total
sales transactions with complaint that an employee ever had. The attributes selected as variables
for prediction are as follow.
1. Ratio of sale cancellation by sale employee (D_CANC_RA)
2. Ratio of sale cancellation and reimbursement by sale employee (D_RESS_RA)
3. Ratio of matched sale by sale employee (D_CASA_RA)
4. Ratio of sale to inactive account by sale employee (D_INAT_RA)
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5. Ratio of sale with complaint by sale employee (D_RECL_RA)
6. Ratio of sale to another employee by sale employee (D_FUNC_RA)
7. Number of sales transaction by employee (C_A_SALE)
These attributes are known variables at the time of sales, so they are good candidates for
the analysis. Most of the variables are historical sales information of each employee. These
variables are normalized by calculated as a ratio. This is to avoid bias and to make data
comparable.
Several algorithms are applied to the data set to predict the status of the sale transactions.
Only algorithms suitable for nominal or categorical outcome value were selected. They are
classification tree, logistic regression, and support vector machine. The validation method used
is 10-fold cross-validation.
6. Results and Analysis
To compare the results among algorithms, several measurements are considered. They
are percentage that the model could correctly classified instance, error rate, specificity, recall,
precision and false alarm rate.
For the accuracy of the models, the first run of the analysis gave very high percentage of
correctly classified instances- more than 90 percents, for all algorithms. This is a signal of some
abnormality because the results are too good for all algorithms. Thus, the data was re-evaluated
and found that it was suffered from the unbalanced data problem. From total 607,189 records,
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566,753 (93.34%) records are non-cancel sales transactions, while another 40,436 (6.66%)
records are cancel sales transactions.
There are two approaches to deal with the unbalanced data problem. One approach is to
weight the data by making both cancel and non-cancel transactions more reasonably equal
weight and to add a penalty for incorrectly classify the result. Another approach is to select a
balance sub-sample by selecting the same amount of canceled and non-canceled transactions. In
this case, both approaches were tried and different results obtained.
In the data weighted approach, the weight was assigned to a smaller portion of data:
cancelled sales transactions. There is no specific method to calculate the right weight for the
data, but several numbers could be tried to find the best result. The ratio between cancel and noncancel data is 1:14, thus this is the first tried weight value. With the data weighted approach, the
result of the classification tree, J48, is considerably dropped to 64.23% correctly classified
instances, and got a large number of false negative. With the logistic algorithm, after adjusted the
weight, the result is 70.16% correctly classified instances, which is better than J48 algorithm
result. However, it still generated a large number of false negative results. The support vector
machine algorithm presented the best result among all three algorithms. The model can correctly
classify 79.36% instances and has a much smaller number of false negative than other models.
The summary results of data weighted approach with all algorithms are shown in table 1.
Another approach to deal with the unbalanced data problem is by selecting a balanced
sub-sample from the data set. The cancel and non-cancel sales transactions are randomly selected
from the population in the same volume; 30,000 records were selected from cancelled sales
transactions and another 30,000 records were selected from non-cancel sales transactions. The
classification tree, J48, correctly classified instances at 67.54% Logistic regression algorithm
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correctly classifies instances at 65.29% and also has a large number of false positive instances.
Support vector machine correctly classified instances at 64.64% and has a large number of false
positive. The summary results of sub-sample data approach with all algorithms are shown in
table 2.
The preliminary results show that each approach has both pros and cons. The data
weighted approach has higher accuracy rate, lower error rate, higher precision rate and higher Fmeasure, while the sub-sample approach has higher specificity rate, higher recall rate, lower false
alarm rate and lower F-measure. These results could imply that overall result of data weighted
approach is better. However, it is a decision to trade-off the cost and benefit to investigate
suspicious transactions. If auditors and management prefer not to investigate too many
transactions to avoid the interruption of the process or any other reasons, they can select the
prediction method that have lower type I error.
7. Conclusion
An alarm or a warning system in continuous auditing is a helpful characteristic that calls
the attention of auditors and management to the problem. The ideal situation is that a problem is
identified and automatically solved as soon as possible before it propagates into other processes.
The sales activities and the compensation of sales employees, which based on the sale
transactions, are front office processes with inherent risk. The volumes of sales are enormous and
continuous by nature. Prediction models using machine learning techniques are created to predict
the status of each sales transaction. The results could alert auditors and management for possible
fraud or irregularities of transactions. Predictive audit will let them monitor the controls and
detail transactions in a preventive basis.
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0
11/4/20…
11/9/20…
11/14/2…
11/19/2…
11/24/2…
11/29/2…
12/4/20…
12/9/20…
12/14/2…
12/19/2…
12/24/2…
12/29/2…
1/3/2010
1/8/2010
1/13/20…
1/18/20…
1/23/20…
1/28/20…
2/2/2010
2/7/2010
2/12/20…
2/17/20…
2/22/20…
2/27/20…
3/4/2010
3/9/2010
3/14/20…
3/19/20…
3/24/20…
3/29/20…
4/3/2010
4/8/2010
4/13/20…
4/18/20…
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4/28/20…
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Graph
Graph 1: Special saving account cancellation transaction summary by date
700
600
500
400
300
200
100
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Tables
Table 1: Models comparison of data weighted approach with 1:14 ratio
Model/
Accuracy
Measurements
Error
Specificity
Recall
Precision
rate
False
alarm
(%)
rate
J48
64.23
35.77
51.72
65.12
94.98
48.28
Logistic
70.16
29.84
50.30
71.58
95.28
49.70
Support vector 79.36
20.64
37.20
82.37
94.84
62.80
machine
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Table 2: Models comparison of balanced sub-sample approach
Model/
Accuracy
Measurements
Error
Specificity
Recall
Precision
rate
False
alarm
(%)
rate
J48
67.54
32.46
63.69
71.39
66.29
36.31
Logistic
65.29
34.71
54.02
76.56
62.47
45.98
Support vector 64.64
35.36
47.70
81.58
64.64
52.30
machine
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