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Tuesday, 18 August 2015

Public sector governance – part 2

Relevant to Paper P1 from December 2014
In this, the second of two articles considering the new content in the Paper P1 Study Guide on public sector governance, the final three outcomes are discussed. These are all from Study Guide section A9.
b) Describe, compare and contrast the different types of public sector organisations at subnational, national and supranational level.
Public sector organisations – those involved in delivering state services that cannot always be effectively delivered by the private sector – can exist at several different levels. Perhaps we usually think of these at the national level. National government is usually based in the national capital city and is divided into central government departments (all examples of national-level public sector organisations). The names given to these departments vary by country but most governments have a treasury, an interior department, a foreign office, a defence department, a health service, education, social services, etc. In many cases, these departments are led by a political minister from the governing political party. This is important in democratic countries because the policies adopted by these departments affect many people and it is important that they are subject to political change if the electorate changes a government at an election (this being a part of the social contract between the government and the governed).
In addition, national government policy is configured and coordinated centrally to ensure that strategic policies are pursued and that departments work together to ensure this. The head of government (not to be confused with the head of state) is responsible for national government policy and in a democracy, he or she can be re-elected or defeated based on his or her performance in the role.
In terms of governance, national government departments are nominally headed by a minister from the governing political party. This means that he or she issues instructions on how the department should formulate and implement policy to help achieve the government’s overall strategic objectives. There is also, however, a substantial structure of permanent government employees (sometimes called ‘civil servants’ or similar) whose role it is to advise the minister and help him or her to implement policy in the relevant department.
In addition to national government, public sector organisations also exist at the supranational and subnational levels. Below national level (‘subnational’), some countries are organised or subdivided into regional authorities, variously called regional assemblies, states, cantons, departments, municipalities, local authorities or similar. Some selected powers are devolved down to these subnational bodies by national government in the belief that these selected powers are either best handled by local people or that service delivery to the regions will be more efficient or cost effective if done so. Typical powers devolved down to the subnational level include planning (such as roads and new housing permissions), utilities (such as energy and water), local schools, housing, support of vulnerable communities, rubbish collection, etc. In many countries, for example, children’s education is one of the tasks devolved to local authorities. This is because local authorities are usually in possession of the particular statistics and needs analyses that are necessary for effective planning of education services. So if a large new housing project is planned, it will be typically be the local authority that ensures that a school is located nearby for children in that area to attend. Similarly, local demographic trends and particular educational needs may be better understood by subnational authorities than by national government. In such cases, individual schools must report to the local authority on selected metrics, which might include budgetary compliance, teaching quality and exam results.
In many cases, subnational public sector organisations are led by elected representatives in a similar way to national governments. These are supported by permanent officials in a similar manner to civil servants in national governments.
Supranational bodies are a little more complicated. When national governments form supranational bodies, they do so for a shared purpose and they are often subject to significant pressures as a result. This is often because national governments do not agree with each other, as each one is subject to pressure from its own people to prefer one outcome to another in the supranational organisation. Many European countries, for example, are members of the European Union, with offices in Strasbourg and Brussels, whilst similar bodies exist elsewhere in the world. The United Nations (UN), based in New York, expresses the collective opinion of many countries in different parts of the world, on a range of international issues. The UN employs many thousands of people both at its headquarters in New York and around the world through its various agencies (eg in the World Health Organisation, in its refugee agency, environment programme, etc). Similarly, the World Trade Organisation (WTO, formerly the General Agreement on Tariffs and Trade) meets every few years (in ‘rounds’ named after the city they meet in) in order to help with reducing barriers to international trade in terms of reducing or removing tariffs (import taxes) and quotas (limits on imports of certain goods and services).
c) Assess and evaluate the strategic objectives, leadership and governance arrangements specific to public sector organisations as contrasted with private sector.
In the previous article in this series, the issues of the nature of public sector organisations and their stakeholders were raised and discussed. Because of their nature and the ways they are funded, there are major differences between public and private sector organisations.

Strategic objectives

While most private sector organisations are independent in that they are ‘stand alone’ companies answerable to their shareholders, most public sector organisations are part of a larger public sector structure. A defence force, such as an army, cannot act alone and as it sees fit. Rather, it is funded by government and is tightly controlled in what it is asked to do and how it achieves its aims. Likewise, a school in the public sector will rarely have the freedom to do as it likes in terms of what and how it teaches, who it appoints and where it locates itself. In each case, the public sector organisation is helping to achieve and implement a set of higher government policy objectives.
This is not to say that individual public sector organisations do not have strategic objectives, however. Each one must work out how it will achieve what it is asked to do but the autonomy given to individual organisations varies. The ‘three Es’ framework encountered in the previous article is of help in understanding this. Each public sector organisation must be strategically effective in that it must achieve the objectives established for it in carrying out government policy. Because they are funded by public money, they must also be efficient and make the most of whatever resources they are provided with. Finally, they must also be economical in that they must work within specified budget and deliver desired outputs within that budget. Accordingly, there is an emphasis on value for money and service delivery. When public sector organisations are occasionally criticised in the media, it is usually because they have either overspent, underperformed, or both.

Governance arrangements

There is no single way in which public sector organisations are governed. Accountability is gained in part by having a system or reporting and oversight of one body over others. Because there is no market mechanism of monitoring performance (as there is with listed companies, for example), other ways must be found to ensure that organisations achieve the objectives and service delivery targets established for them.
In some cases, then, a head of service or a board of directors must report to an external body of oversight. The oversight body may be a board of governors, a council of reference, a board of trustees, an oversight board or similar. In each case, its role is to hold the management of the service to account for the delivery of the public service and to ensure that the organisation is run for the benefit of the service users. Because public sector organisations are not held to account by shareholders as with business companies, the oversight body is often put in place as a means of holding the management to account. In this respect, oversight bodies are acting in the interests of service funders (usually taxpayers) in making public sector organisations accountable.
Typical (and general) roles of oversight bodies include the following, although their roles do vary substantially depending on jurisdiction and government policy. Firstly, they are there to comply with government rules on whichever public sector governance applies. So a school may have a board of governors in order to comply with the local authority or education department rules on school governance. A hospital’s management may, likewise report to a superordinate body possibly overseeing several other hospitals at the same time.
Second, it is their role to ensure the organisation is well-run and meets the performance targets established for it by higher levels of government. It may receive internal or external audit reports to help achieve this or make visits and other interventions to ensure that the organisation is performing to expectation. Third, the oversight body may be involved in budget negotiations and then in monitoring performance against budget and any number of other agreed financial measures in a similar way that a management accountant might in a conventional business.
Fourth, it is likely to be involved in making senior appointments to the public sector body and in monitoring the performance of management on an ongoing basis. In many cases, boards of governors in schools or universities, for example, have the power to remove a senior manager (perhaps a head teacher) if they believe he or she is underperforming and not delivering the quality of services required. Finally, they are sometimes required to report upwards, perhaps to local or central authorities, on the organisations they have oversight over.
There is an increasing move in some situations to run some public services along similar lines to private companies. This means they may have an executive board and also some non-executive membership on the board also.
d) Discuss and assess the nature of democratic control, political influence and policy implementation in public sector organisations including the contestable nature of public sector policy'
One of the curious and fascinating features about public sector organisations is that there is sometimes a debate about how they should be operated and even whether they should exist at all. Because, in many democratic countries, public policy is debated in the public arena, there is a public debate about how the state sector should be constituted. This includes debates over the size of the state and the role of its institutions.
In a democracy, political parties argue over the nature of public policy and they do so from a particular set of underlying assumptions. Some of these underlying assumptions influence the way they argue for particular outcomes and the way in which they guide a government when they achieve political power. It tends to be the case (with exceptions) that left-leaning governments prefer a larger state sector, with more state spending and more public sector employment, while right-leaning governments prefer more to be achieved in the private sector and less by government.
In addition, changing policy objectives mean that some public sector organisations are required to change over time, both in size and in what they are asked to do. As governments change, some public sector organisations grow in size and become more important, and others become small and less important.
The debate is often intense and enduring. In the case of health services, for example, some believe that health should always be entirely within the public sector and entirely funded by the taxpayer. This means that, for the service user (the patient), everything is free at the point of use. Others strongly believe that this is a misuse of public funds and that people should pay for health services in other ways, such as through an insurance or subscription scheme. Likewise with university education: some believe it should be paid for by the state and others believe that students should pay. In each case, debates are complicated. If there were easy and convincing answers, there would be less debate, but public opinion is split on most areas of public debate and this fuels political debate and, in turn, how public sector organisations are configured in line with particular political influences.
One of the ways in which some countries have restructured their economies in recent years, is through the process of privatisation. This means taking a service that was previously delivered through public sector organisations and then allowing it to be provided by private sector organisations. In some cases, the previous public sector monopoly supplier of a service is transferred into the private sector by making it into a public listed company so that people can buy shares in it. Those in favour of privatisation tend to argue that services can be delivered more efficiently in the private sector where management have a profit motive and competition. This, in turn, delivers better value to the customer.
This process is not without its critics, however. Opponents of privatisation sometimes argue that some strategic services, such as utilities, water, etc, are too important to be subject to the market forces of private enterprise. Others believe, perhaps from a position of personal ideology, that the state should control much more of the economy rather than less. So transport and airlines should also be under state control, perhaps.
Privatised businesses, once transferred from the state sector, are often subject to a great deal of internal change including changes in culture, structure, and governance. Some Paper P1 questions have used privatised businesses as a case study, focusing in particular on issues of changing governance. In any event, however, we can see that changes in opinion influence public sector organisations in many ways.
These changes to the Paper P1 Study Guide represent a broadening of the study of governance into a wider range of organisations than previously. The public sector shares some features of governance with the private sector in terms of the need for strategic leadership, clear thinking and effective strategy implementation. The governance arrangements often differ, however, and it is important to study these in preparation for future Paper P1 exams.
Written by a member of the Paper P1 examining team

Public sector governance – part 1

Relevant to Paper P1 from the December 2014 exam session

There is a new section of the Paper P1 Study Guide for exams from December 2014 onwards. It is on public sector governance and is included as a new addition under section A9. The purpose of this article is to introduce these topics and give some pointers as to what the important themes are in terms of teaching and learning.
There are four new sections (A9a, b, c and d) with each covering an important element of public sector governance. In this article, and a subsequent one, these themes will be covered in the order that they appear in the Study Guide. We will begin with considering the new section A9a:
a) Describe, compare and contrast public sector, private sector, charitable status and non-governmental (NGO and quasi-NGOs) forms of organisation, including purposes and objectives, performance, ownership and stakeholders (including lobby groups).

What is the ‘public sector’?

In what economists call a ‘mixed economy’, there is a range of organisations. Some are business organisations and exist to make a profit; others are charitable or benevolent in their purpose, and another type is referred to as public sector. Not to be confused with ‘public companies’ (which describe the public availability of their shares), these are organisations that are, in some way, connected to, or deliver, public goods and services. This means that they help to, in some way, deliver goods and services that cannot be, or should not be, provided by ‘for profit’ businesses.
In most cases, public sector organisations are operated, at least in part, by the state. A state, not to be confused with a government, is a self-governing, autonomous region, often comprising a population with a common recent or ancient history. A state has four essential ‘organs’ without which it cannot fully operate: the executive (or government), the legislature, the judiciary and the secretariat (or administration). Because national constitutions vary, it is not possible to give general examples of how these ‘work’.
In the UK, by way of example, however, the head of state is the reigning monarch and the head of government is a different person (the prime minister). The head of government leads the executive, and the head of state is largely a ceremonial position, but in other countries, he or she also has a role in government. The legislature formulates and passes statute law, which the judiciary (the system of courts) interprets and enforces along with other non-statute laws called common laws. In a democracy, the legislature is largely elected and the judiciary is independent of government so that, if necessary, the judiciary can bring a legal case against the government or members of it.
The state’s secretariat or administration is by far the largest of the four ‘organs’ and is responsible for carrying out government policy and administering a large number of state functions. Again, the roles carried out by the secretariat depend upon the country’s constitution but these typically include education, health, local authority provision, central government, defence, foreign affairs, state pensions, tax collection and interior issues such as immigration, policing and prisons. For the most part, organisations such as these are funded by revenues from the state (mainly taxes) and they exist to deliver public services that cannot, or – in the opinion of the government – should not be provided by the private sector (the name given to businesses funded by private capital).
In most developed countries and in many developing countries, the public sector is very large. In the most developed countries, the state spends over 40% of the country’s domestic product and this figure is over 50% in some cases. In the UK, for example, the public sector accounts for around a quarter of all jobs. Accordingly, then, the public sector is very large and accounts for many different organisations delivering important services and employing, in many cases, thousands or even millions of people.

Agency in the public sector

One of the key concepts in corporate governance in the private sector is agency. This means that the people who manage a business do not own it, and in fact manage the business on behalf of their principals. It is said that management has an agency relationship with the principals in that they have a fiduciary duty to help the principals achieve the outcomes that they (the principals) seek. In a private or public incorporated business organisation, the principals are shareholders and, in most cases, shareholders seek to maximise the long-term value of their shares. This is usually achieved by profitable trading and having strategies in place to enable the company to compete effectively in its competitive environment.
It is slightly different for public sector organisations. Those employed in the public sector work just as hard as those in the private sector and have objectives that are just as clear (but are sometimes conflicting), but the principals are different. Whereas private and public companies have shareholders, public sector organisations carry out their important roles on behalf of those that fund the activity (mainly taxpayers) and those that use the services (perhaps pupils in a school, patients in a hospital, etc). Funders (ie taxpayers) and service users are sometimes the same people (for instance, taxpayers placing their children in state school) but sometimes they are not, and this can give rise to disagreements on how much is spent and on what particular provisions. Part of the nature of political debate is about how much state funding should be allocated to which public sector organisation and how the money should be spent.
In general, however, public sector organisations emphasise different types of objectives to the private sector. Whereas private companies tend to seek to optimise their competitive positions, public sector organisations tend to be concerned with social purposes and delivering their services efficiently, effectively and with good value for money.

A common way of understanding the general objectives of public sector organisations is the three Es: economy, efficiency and effectiveness.

Economy represents value for money and delivering the required service on budget, on time and within other resource constraints. It is common for public sector employees and their representatives to complain about underfunding but they have to deliver value to the taxpayers, as well as those working in them and those using the service.
Efficiency is concerned with getting an acceptable return on the money and resources invested in a service. Efficiency is defined as work output divided by work input and it is all about getting as much out as possible from the amount put into a system. It follows that an efficient organisation delivers more for a given level of resource input than an inefficient one.
Effectiveness describes the extent to which the organisation delivers what it is intended to deliver.

Forms of organisation

The entry in the Study Guide contrasts ‘public sector, private sector, charitable status and non-governmental (NGO and quasi-NGOs) forms of organisation’. The term ‘third sector' is sometimes used to refer to charitable and non-governmental organisations. The public and private sector are the first and second sectors, though the order of these – which is the first and which is the second – varies with who is writing. The third sector comprises organisations that do not exist primarily to make a profit nor to deliver a service on behalf of the state. Rather, they exist primarily to provide a set of benefits that cannot easily be provided by either profit-making businesses nor the public sector.
Organisations delivering international medical aid are a good example of non-governmental organisations (NGOs). Well-known NGOs such as Medicins sans Frontiers (‘doctors without borders’ in English) are large and well-structured organisations, delivering important medical aid in war zones and the like. Although supported by businesses and governments in their aims and activities, such NGOs are often mainly privately funded (eg by benevolent individuals) and do not operate under either a conventional business or public sector structure.
In such cases, NGOs and charities may have an executive and non-executive board, but these are subject to a higher board of trustees whose role it is to ensure that the NGO or charity operates in line with its stated purpose or terms of reference. In these cases, the agency relationship is between the NGO or charity, and its donors. When donors give to NGOs or charities, it is important for them to be reassured that their donation will be responsibly used for its intended purpose and the board of trustees help to ensure that this is what happens.
A question in the June 2011 Paper P1 exam gave an example of a poorly managed charity, the Horace Hoi Organisation (HHO), where one individual (Horace Hoi) misused funds donated to the charity for personal enrichment. An effective board of trustees could have helped to ensure that donated funds were used for their intended purposes (for HHO, it was to prevent animal suffering).
In some cases, NGOs can be funded by a government but remain semi-independent of the government in their activities. It might be, for example, that a government is seeking to provide a certain service (eg regional support of businesses) but wants to ensure, because of the importance of that service, that its delivery is free from – and seen to be free from – any political interference. If a government wants to be free from the accusation, for example, that a local business-support decision was based on political advantage for the governing party, it might give a publicly funded organisation effective autonomy in its decision making, even though it is helping to implement government policy.
These organisations are sometimes referred to as QuANGOs – quasi-autonomous non-governmental organisations. QuANGOs are sometimes accused of being unaccountable for their decisions because they only weakly report to the government (and the taxpayers) who fund their decisions. But that is partly the point of a QuANGO: it accounts to many principals including local stakeholders, central government and national taxpayers. QuANGOs can be politically awkward and, accordingly, their use in the public sector changes over time.
Public sector organisations themselves can take several forms. In each case, they are directly responsible for delivering part of a government’s policy and are, in most countries, under the control of the government. This means that they are under ‘political control’ in that people in government with a political agenda partly control their objectives and activities. In many countries, politics divides along a ‘left-right’ split while, in others, political divisions are more concerned with ethnicity, culture or religion. In some countries, for example, universities are funded mainly by governments, while, in others, they are mainly private institutions. It is similar with healthcare and schools – in some countries, these are under central government control and funding while, in others, they are privately funded and citizens must pay for services directly or through insurance.

Lobbying and lobby groups

In a democratic society, one in which political priorities are publicly debated and governments change with the collective will of voters, a range of external interests seek to influence public policy. In some cases, external interests coalesce around a certain opinion and it seems appropriate, to some, to campaign to influence government policy in favour of their particular vested interest. When organised specifically to attempt to influence government policy or the drafting of legislation (statute law), such interests sometimes ‘lobby’ politicians to try to get them to vote in the legislature in favour of their particular interest. These ‘lobby groups’ may attempt to influence in favour or against a wide range of issues and, although their activities are legal, some argue that they are not always helpful because it is thought by some that those that are the best funded will be the most likely to be heard. This can act against the public interest and in favour of sectional interests and this is thought to not always be helpful to the democratic process.

Stakeholders in the public sector

Several questions in previous Paper P1 exams have examined the complexities of stakeholders for a private sector (ie business) organisation. Public sector organisations have, in many cases, an even more complex set of stakeholder relationships than some private sector businesses. Because most public sector activities are funded through taxation, public sector bodies have a complicated model of how they add value. For a private business, revenues all come from customers who have willingly engaged with the business and gained some utility for themselves in the form of benefit from goods or services.
With a government, however, taxation is mandatory and may be paid against the wishes of the taxpayer. Citizens of a country might disagree with the levels of taxation taken by a government, especially when a taxpayer sees most of his or her tax being spent on causes or services that mainly benefit others (and not themselves) and with which they may disagree.
Political theorists have long discussed the importance of a social contract between the government and the governed. In this arrangement, those who pay for and those who use public services must all feel that they are being fairly treated and not being over-exploited nor badly served. Because there are so many claims to balance, then, the stakeholder pressures on a government are often very difficult to understand.
Furthermore, the claims of some stakeholders are assessed differently by different people according to their particular political stance. This means that some stakeholder claims are recognised by some but not by others, and this can make for a very complex situation indeed when it comes to deciding which stakeholder claims to recognise and which to reduce in weight or ignore. Some stakeholders have a very weak voice, while others have no effective voice at all in order to express their claim. Part of the debate in politics is the extent to which these weaker stakeholders are represented and how their assumed needs are met.
Part 2 of this article will consider the other new topics introduced under section A9 of the Paper P1 Study Guide.
Written by a member of the Paper P1 examining team

Caught by the numbers

Data analytics winnows out possible fraudster

John Giardino, CFE, CPA

March/April 2014

The bank comptroller used internal statistics to point the finger at an over-performing loan officer. Learn how you can use powerful data analytics tools to narrow your list of suspects for fraud examinations.

The case in this article, a composite of several similar cases involving data analytics and statistical analysis applied to fraud examinations, is designed to be a tutorial for CFEs. — ed.

Jeff Baker, controller for a large regional bank, entered the room hoping to get an admission of guilt from his suspect. He came prepared. Baker had spent considerable time carefully preparing his questions and planning his interview tactics, and he had the documentation to back him up. Above all, he was proud that he had identified a sizeable straw-purchase and kickback fraud scheme, which probably involved the bank employee in the interview room. Baker credited the identification of this scheme to effective analytical procedures that included some basic statistical methods.

Weeks before, when Baker reported a significant spike in defaults on mortgage loans through the second quarter of 2013, several of the bank’s board members expressed concern over a recently adopted growth strategy. In late 2012, the bank had eased underwriting requirements in an effort to increase market share of residential mortgage lending; a major component of these new underwriting practices was an across-the-board reduction in debt-to-income (DTI) requirements for borrowers.

Board members were worried about impact to the balance sheet. They wanted assurances that toxic, defaulted assets wouldn’t erode shareholders’ equity. Baker, however, wasn’t convinced that changes in underwriting guidelines were the root cause of the uptick in loan nonperformance. He was aware that the stalling economic climate within the bank’s operating footprint had exacerbated the moral hazard for mortgage fraud, so he had been following several high-profile prosecutions of straw-purchase schemes at other financial institutions throughout the region.
Despite his employer’s sterling reputation based on lending history, business practices and community involvement, Baker worried that the organization’s reluctance to break from the traditional risk management model created a blind spot to internal threats, weak controls and susceptibility to fraud schemes.

CORRELATION ANALYSIS: COMPARING TWO VARIABLES

Figure 1: The CORREL function

After buying some time from the board members by voicing his suspicions, Baker tested his theory that the easing of DTI requirements was unrelated to the spike in nonconforming loans. He calculated the correlation coefficient of nonperforming loans to DTI requirements. Baker obtained aggregated default rate data of loans made at various DTI requirements from internal management reports and performed his calculations in Microsoft Excel using the CORREL function. (CORREL is based on the mathematical formula, Figure 1, above.)

In this case, he compared two variables — DTI requirements and default rate. If the assumption is that default rate is correlated to DTI, then DTI is the independent variable and default rate is the dependent variable. A correlation coefficient value has a range between -1 and 1; values closer to -1 or 1 indicate a negative or positive correlation, respectively. In a negative correlation, the default rate would decrease as DTI requirements increase. In this particular case, the correlation coefficient was very close to 0, which indicates a loose correlation between required DTI and default rate. Baker was correct on his first assumption — the new growth initiatives weren't a significant driver in loan nonperformance.

IDENTIFYING WHAT SHOULD OCCUR: PROBABILITY DISTRIBUTIONS
In statistical terms, a probability distribution refers to a graph, table or formula, which illustrates the probability for each value of a random variable, such as household income, IQ or set of test scores. The normal probability distribution is perhaps the most widely known and is commonly referred to as a “bell curve” based on its appearance; the mean of the distribution is represented by the top of the bell curve, as it represents the expected value. The bell curve is symmetric, and a key related concept is variation from the mean, measured by the standard deviation. In a normal distribution nearly all of the possible values — 95 percent, in fact — fall within two standard deviations of the mean. The greater the standard deviation, the more possible values could occur naturally.

Baker determined the probability distribution of original loan values of the bank’s outstanding mortgage loans based on the mean and standard deviation values obtained from aggregated internal data. The distribution he identified is represented in Figure 2 below.

Figure 2: Probability distribution - original loan amounts

This chart indicates that the highest probability of original mortgage loan amounts that the bank originated will be close to the mean value of $213,157. Because 95 percent of all original loans issued by the bank will be within two standard deviations of the mean in either direction, almost all of the mortgage loans made by the bank have an original loan balance between $76,767 and $349,547. If nonperformance of loans occurred randomly and wasn’t tied to any particular characteristic of underwriting, Baker would expect to observe a similar distribution for loans currently in default. However, a statistical analysis of data on the nonperforming loans that the bank originated reveals significantly different characteristics, as presented in Figure 3 below.

Figure 3: Probability distribution - original loan amounts (nonperforming)

The population presented in this distribution is those loans within the bank’s portfolio that are in default. The statistical mean original loan amount on these nonperforming loans is $95,132 — significantly lower than the mean original loan amount of the bank’s entire mortgage portfolio. The observed standard deviation of this population is $26,538; based on a normal probability distribution, Baker concluded that virtually all nonperforming loans were originated at amounts between $42,056 and $148,208. There’s statistical significance in this disparity: loans originated by the bank that are in default exhibit a much lower mean original loan amount and degree of variability than the entire mortgage loan portfolio.
Baker was aware that there were many possible fraud and non-fraud scenarios that would explain this disparity. Borrowers with original mortgage loan amounts between $42,056 and $148,208 may present a greater credit risk based on volatile employment situations or adverse credit histories. Mortgage loans in this range typically require a small down payment, which increases the borrowers’ incentive to “walk away” when situations become dire. Baker noted that the dispersion of the nonperforming loans is also very narrow: the coefficient of variation — or the ratio of standard deviation to the mean — is less than 1/3 ($26,538/$95,132 = .27). In other words, there’s a very narrow range of original loan amount in which a majority of his employer’s loans default above and below the statistical mean.
Baker focused on one particular characteristic based on his knowledge of his organization’s internal control structure: The mean value of the original loan amount on nonperforming loans is slightly below $100,000, and the bank requires secondary approval on those mortgages with original loan amounts above that threshold. This secondary approval serves as a check against unauthorized (and potentially fraudulent) loan origination. Baker’s analysis of the probability distribution of nonperforming mortgage loan data indicated the secondary approval control might have been circumvented in a mortgage-fraud scenario.
Baker’s probability distribution of original loan amounts represents a target-rich environment of data for fraud analytics. Those nonperforming loans with original values between two standard deviations below the mean ($42,056) and the $100,000 threshold is depicted graphically in Figure 4 below.

Figure 4: Probability distribution - nonperforming loans w/o secondary approval

USING SAMPLES TO CHARACTERIZE THE POPULATION

Just as circumvention of controls is only one possible explanation for the observed distribution of original loan amounts on the bank’s bad mortgages, there are numerous statistical characteristics of nonperforming loans that Baker could have analyzed for indications of mortgage fraud including:

Number of payments made prior to default.
Ratio of appraised value to fair market value at time of origination.
Refinance loan requests for excessive amounts.
Mortgage applications in the names of holding companies.
Multiple mortgage loans requested in the name of the same buyer (straw-purchaser scheme).

To this point in his analysis Baker had worked with aggregated financial information about his employer’s mortgage portfolio, including metrics involving underwriting and loan performance. The bank’s control environment includes information systems, which capture numerous data points for management reporting purposes. Baker needed to examine the loan files for analysis of fraud indicators. Because it would have been impractical to review every loan file within the target-rich range that Baker had identified in his analysis, he utilized sampling methods to test for fraud. Knowing that mortgage fraud often involved falsified appraisal information, Baker decided to analyze two key attributes: the loan officer and appraisals associated with each loan.
The concept behind sampling is intuitive. Baker’s intent was to arrive at some conclusions about the entire population of mortgage files within his target-rich environment based on observations of a sample that he selected. Sampling has a wide array of applications and is so effective that a sample of only 1,000 people allows political pollsters to arrive at reasonable conclusions about how 300 million Americans will vote.
The size of a sample is as positively correlated to precision as it is to diminishing returns; an efficient sample should be large enough to be meaningful yet not so much as to be inefficient. Baker selected 200 files in the target range and identified the distribution of approving loan officers on nonperforming loans with original loan amounts between $42,056 and $100,000 presented in Figure 5 below.

Figure 5: Distribution table, approving loan officer on nonperforming loans with original loan values between $42,056 and $100,000

Baker needed to determine how precise his sample was before making further conclusions; by selecting only a sample size of 200 files, how confident could Baker be in his data? To determine how closely a sample matches the total population, statisticians calculate the margin of error using this formula:
MarApr-formula

in which z* (pronounced z-star) is the confidence interval critical value, p is the observed proportion within the sample, and n is the number of files sampled. The confidence interval critical value measures the number of standard errors (or deviations) to be added or subtracted to achieve the desired confidence level in sample results. Most statisticians rely on a confidence level of 95 percent in calculating margin of error, and z* for that level is 1.96. The margin of error for the selected sample size is then applied to the identified distribution as a plus/minus range.
In this case, Baker used the formula to determine, with 95 percent confidence, that based on the selected sample, Mike Matthews was the approving loan officer in 35 percent to 49 percent of all mortgages in the target range as presented in Figure 6 below.

Figure 6: Confidence intervals for the selected sample of nonperforming mortgages

The number of files in the sample is in the denominator of the margin of error formula; consequently an increase in sample size reduces the margin of error. For example, if Baker had sampled 400 files and found that the percentage of mortgages that Matthews had approved remained the same, the margin of error would have been 5 percent, and the confidence interval would have been between 37 percent to 47 percent. If Baker had wanted to increase his confidence level, he would’ve selected a higher z* value; to obtain a 99 percent confidence level he would’ve used a confidence interval critical value of 2.58 because 99 percent of all sample means fall within 2.58 standard errors (or deviations) of a normal distribution population mean.
At this point in his analysis, Baker took inventory of the facts: nonperforming loans exhibited characteristics (original loan amount) atypical of the bank’s entire loan portfolio. Internal controls required secondary approval on all mortgage loans above a $100,000 threshold, and a majority of the nonperforming loans fell below this threshold. Loan officer Matthews had approved more of the nonperforming loans than any other employee in the bank’s mortgage division, but this doesn’t necessarily prove fraud. Matthews was a highly successful mortgage loan originator for the bank; Baker knew he needed to compare his data with divisional sales figures to identify correlations.

DIGGING DEEPER: USING THE SAMPLE CHARACTERIZATIONS TO MAKE CONCLUSIONS
Sales performance reports indicated that Matthews had originated approximately 35 percent of the bank’s total mortgage loan portfolio in the past year, while loan officer Larry Jenkins had struggled with his sales numbers over the same period. Jenkins originated approximately 3 percent of the bank’s mortgage portfolio but between 10 percent and 20 percent of the nonperforming loans below the secondary approval control threshold. In addition to examining why Jenkins was significantly outperforming his overall average in this particular subset of loans, Baker analyzed characteristics of the appraisal reports contained in the loan files.
One of the most common mortgage fraud schemes is illegal property flipping, in which a falsified appraisal increases the sale price — and loan amount — for a straw buyer. In some cases, mortgage loan officers, appraisers and other parties act in collusion to execute the scheme in exchange for kickbacks. Baker analyzed the appraisers on nonperforming loan files originated by Jenkins and compared the appraised value of the properties to historic sales prices and property tax assessments. Within the 30 files on nonperforming loans that Jenkins had approved in the target range, Baker noted the following key statistics:

Southern Appraisal Services performed the appraisals on 24 of the 30 loans in the sample. Records on file with the state division of corporations revealed that Southern Appraisal Services is an LLC owned by a holding company named Precision Real Estate Professionals.
Public records indicated that the registered agent for Precision Real Estate Professionals shared an address with a relative of Jenkins.
Appraisal values on the 30 properties exceeded the most recent property tax appraisals by a factor of 10. Baker wasn’t surprised to learn that the appraisal values in the loan files also significantly exceeded comparable sales data obtained from the Multiple Listing Service database.

These statistical methods provided Baker with the proper predication to expand his examination. He determined that the increase in defaults wasn’t a result of aggressive marketing and that he had sufficient evidence to suspect a mortgage fraud scheme. After he allayed the board of directors’ concerns, he initiated a full-scale investigation of Jenkins’ activities. Armed with his analysis and some additional background investigation, Baker was able to obtain a quick admission of guilt from Jenkins, who was promptly terminated and handed over to the authorities.

FRAUD EXAMINERS' SABERMETRICS
Baseball fans like to discuss the merits of the “Sabermetrics” approach to analyzing the game and characterizing the skill of players and teams through advanced statistical analysis. (The term is derived from the acronym SABR — the Society for American Baseball Research.) The same statistical methods allow fraud examiners to make characterizations about large volumes of data even when only pieces of it are available. Statistical analysis isn’t absolute proof of fraud and certainly is no substitute for a thorough, sound fraud examination. Initially, Jenkins might have had valid reasons for originating a significant percentage of nonperforming loans. However, only through Baker’s complete investigation was Jenkins’ fraud later confirmed. The value of his analysis was the development of a strong foundation for his fraud examination and detection efforts.

John Giardino, CFE, CPA, is a senior fraud analyst working in the telecommunications industry.

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Materials mix and yield variances

Some tips to help alleviate students' fears of variance analysis

Since long ago, variance analysis has been an area that evokes fear in students worldwide. Students enter the exam hall, desperately running through the formulae used to calculate all the different variances, fearful of forgetting them before they have managed to put pen to paper. Then the inevitable happens: they turn over the exam paper and a variance question stares back at them. Frantically, they scribble down all the formulae before they are lost forever. Alas, they can’t remember it quite accurately enough. Is it actual quantity x standard price or standard quantity x actual price? Panic grips them. Logic flies out of the window. They move desperately on to the next question.

Does this sound like a familiar story to you? If it does, carry on reading. This article might help you. Many articles have been written about variance analysis over the years, but the purpose of this one is to cover the area of calculating materials mix and yield variances. While the calculation of a mix variance can also be done for sales, this is not covered by the Paper F5 syllabus at present.Therefore, I shall concentrate purely on the materials variance here.

Material usage variance
Most students have relatively little difficulty in calculating a straightforward material usage variance. As a reminder, let’s recap on what the material usage variance is and how it is calculated. The material usage variance analyses the difference between how much actual material we used for our production relative to how much we expected to use, based on standard usage levels. So, for example, if we made 5,000 items using 11,000kg of material A and our standard material usage is only 2kg per item, then we clearly used 1,000kg of material more than we expected to (11,000kg – [2 kg x 5,000 items]). In terms of how we value this difference, it must be at standard cost. Any difference between standard and actual cost would be dealt with by the material price variance.

There can be many reasons for an adverse material usage variance. It may be that inferior quality material have been purchased, perhaps at a lower price. This may be reflected in a favourable material price variance: the materials were cheaper but as a result there was perhaps more waste.

On the other hand, it may be that changes to the production process have been made, or that increased quality controls have been introduced, resulting in more items being rejected. Whatever the cause, it can only be investigated after separate material usage variances have been calculated for each type of material used and then allocated to a responsibility centre.

Further variance analysis where several materials are used
The fact that most products will be comprised of several, or sometimes hundreds of different materials, leads us back to the more detailed materials mix and yield variances that can be calculated in these instances. In many industries, particularly where the product being made undergoes a chemical process, it may be possible to combine different levels of the component materials to make the same product. This, in turn, may result in differing yields, dependent on the mix of materials that has been used. Note, when we talk about the materials ‘mix’ we are referring to the quantity of each material that is used to make our product ie we are referring to our inputs. When we talk about ‘yield’, on the other hand, we are talking about how much of our product is produced, ie our output.

Materials mix variance
In any process, much time and money will have been spent ascertaining the exact optimum mix of materials. The optimum mix of materials will be the one that balances the cost of each of the materials with the yield that they generate. The yield must also reach certain quality standards. Let us take the example of a chemical, C, that uses both chemicals A and B to make it. Chemical A has a standard cost of $20 per litre and chemical B has a standard cost of $25 per litre. Research has shown that various combinations of chemicals A and B can be used to make C, which has a standard selling price of $30 per litre. The best two of these combinations have been established as:

Mix 1: 10 litres of A and 10 litres of B will yield 18 litres of C; and

Mix 2: 8 litres of A and 12 litres of B will yield 19 litres of C.

Assuming that the quality of C produced is exactly the same in both instances, the optimum mix of materials A and B can be decided by looking at the cost of materials A and B relative to the yield of C.

Mix 1: (18 x $30) – (10 x $20) – (10 x $25) = $90 contribution

Mix 2: (19 x $30) – (8 x $20) – (12 x $25) = $110 contribution

Therefore, the optimum mix that minimises the cost of the inputs compared to the value of the outputs is mix 2: 8/20 material A and 12/20 material B. The standard cost per unit of C is (8 x $20)/19 + (12 x $25)/19 = $24.21. However, if the cost of materials A and B changes or the selling price for C changes, production managers may deviate from the standard mix. This would, in these circumstances, be a deliberate act and would result in a materials mix variance arising. It may be, on the other hand, that the materials mix changes simply because managers fail to adhere to the standard mix, for whatever reason.

Let us assume now that the standard mix has been set (mix 2) and production of C commences. 1,850kg of C is produced, using a total of 900kg of material A and 1,100kg of material B (2,000kg in total). The actual costs of materials A and B were at the standard costs of $20 and $25 per kg respectively. How do we calculate the materials mix variance?

The variance is worked out by first calculating what the standard cost of our 1,850kg worth of C would have been if the standard mix had been adhered to, and comparing that figure to the standard cost of our actual production, using our actual quantities. My preferred approach has always been to present this information in a table as shown in Table 1 below. The materials mix variance will be $46,000 – $45,500 = $500 favourable.

Remember: it is essential that, for every variance you calculate, you state whether it is favourable or adverse. These can be denoted by a clear ‘A’ or ‘F’ but avoid showing an adverse variance by simply using brackets. This leads to mistakes.

The formula for this is shown below, but if you were to use it, the variance for each type of material must be calculated separately.

(Actual quantity in standard mix proportions – actual quantity used) x standard cost

As a student, I was never a person to blindly learn formulae and rely on these to get me through. I truly believe that the key to variance analysis is to understand what is actually happening. If you understand what the materials mix variance is trying to show, you will work out how to calculate it. However, for those of you who do prefer to use formulae, the workings would be as follows:

Material A: (800kg – 900kg) x $20 = $2,000 Adverse

Material B: (1,200kg – 1,100kg) x $25 = $2,500 Favourable

Net variance = $500 favourable

In this particular example, I have kept things simple by keeping all actual costs in line with the standards. The reality is that, in the real world, actual costs will often vary from standards. Why haven’t I covered this above? Because any variance in materials price is always dealt with by the materials price variance. If we try and bring this into our mix variance, we begin distorting the one thing that we are trying to understand – how the difference in materials mix has affected our cost, rather than how the difference in price has affected our cost.

Why haven’t I considered the fact that although our materials mix variance is $500 favourable, our changed materials mix may have produced less of C than the standard mix? Because this, of course, is where the materials yield variance comes into play.

The materials mix variance focuses on inputs, irrespective of outputs. The materials yield variance, on the other hand, focuses on outputs, taking into account inputs.

On the other hand, it may be that changes to the production process have been made, or that increased quality controls have been introduced, resulting in more items being rejected.

Whatever the cause, it can only be investigated after separate material usage variances have been calculated for each type of material used and then allocated to a responsibility centre.

Mix 1: 10 litres of A and 10 litres of B will yield 18 litres of C; and

Mix 2: 8 litres of A and 12 litres of B will yield 19 litres of C.

Mix 1: (18 x $30) – (10 x $20) – (10 x $25) = $90 contribution

Mix 2: (19 x $30) – (8 x $20) – (12 x $25) = $110 contribution

The formula for this is shown below, but if you were to use it, the variance for each type of material must be calculated separately.

(Actual quantity in standard mix proportions – actual quantity used) x standard cost

Material A: (800kg – 900kg) x $20 = $2,000 Adverse

Material B: (1,200kg – 1,100kg) x $25 = $2,500 Favourable

Net variance = $500 favourable

The materials mix variance focuses on inputs, irrespective of outputs. The materials yield variance, on the other hand, focuses on outputs, taking into account inputs.

Table 1: Calculating the standard cost of 1,850kg worth of C (standard mix)

Actual usage in standard proportions:			Actual usage in actual proportions:	Var.
	$		$	$
A = 800kg (8/20 x 2,000kg) x $20	16,000	A = 900kg x $20	18,000	2,000A
B = 1,200kg (12/20 x 2,000kg) x $25	30,000	B = 1,100kg x $25	27,500	2,500F
Total	46,000	Total	45,500	500F

Materials yield variance
Where there is a difference between the actual level of output for a given set of inputs and the standard output for a given set of inputs, a materials yield variance arises. In our optimum mix, we calculated that 20kg of inputs of A and B should produce 19kg of our output, C. We are effectively saying that there is a loss rate of 5% (20 – 1/20) in our process, ie our outputs, in kg, should be 95% of our inputs. Applying this to our example then, we can say that we would have expected our inputs of 2,000kg to yield an output of 95% of 2,000kg, ie 1,900 kg. Our actual yield was only 1,850kg, which is 50kg less than we would have expected. To calculate the materials yield variance, all we have to do is value this difference between the actual yield (1,850kg) and the expected yield for our given set of inputs (1,900kg) at the standard cost of our output, C, ie at $24 per kg. It is easy to see how to calculate this when we look at it logically and present it in a very simple table as shown in Table 2.

No formula really needs to be learnt if you understand the logic behind the materials yield variance and grasp the principle that any price differences between actual and standard are always dealt with by the price variance alone. However, for those who do prefer to use a formula, the materials yield variance formula is:

(Actual yield – standard yield from actual input of material) x standard cost per unit of output

(1,850kg – 1,900kg) x $24 = $1,200 Adverse

Making observations about variances
From our example, it can be seen that there is a direct relationship between our materials mix variance and our materials yield variance. By using a mix of materials that was different from standard, we have resulted in a saving of $500, in standard cost terms. However, the downside of this is that our cheaper mix of materials has resulted in a significantly lower yield of material C than we would have got had our standard mix of materials been adhered to. This yield was $1,200 lower than it would have been, which is over double the amount that we saved by using a cheaper mix of materials.

Overall, by netting the two variances off against each other, we have an adverse material usage variance of $700 ($1,200 A less $500 F). As indicated earlier on in the article, this could have been calculated on its own, without breaking it down further into its mix and yield elements, by comparing the quantity of materials we expected to use (based on standard usage) for our actual production to the quantity of material we actually did use for our production.

Using my preferred method of a table, our calculations would look like Table 3.

Actual production of 1,850kg requires an input of 1,947kg (1,850 x 100/95) in total of A and B

Table 2: Value difference between actual and expected yield at standard cost of C

Actual yield	Standard yield for actual quantities input	Difference	Standard cost per kg	Var.
1,850kg	1,900kg	50kg	$24	$1,200A

Table 3: Calculating the adverse material usage variance ($700)

Standard quantity for actual production	$	Actual quantity	$	Var.
A = 780kg (1,947 x 8/20) x $20	15,600	A = 900kg x $20	18,000	2,400A
B =1,168kg (1,947 x 12/20) x $25	29,200	B = 1,100kg x $25	27,500	1,700F
Total	44,800	Total	45,500	700A

Again, if you like to learn the formula, this is shown below, although it would have to be applied separately to each type of material.

(Standard quantity for actual production – actual quantity) x standard cost

Understanding the bigger picture
Now that you understand how to deal with the numerical side of materials mix and yield variances, and the fact that these are simply a detailed breakdown of the material usage variance, it is also important to stress the fact that quality issues cannot really be dealt with by this variance analysis. I have mentioned the fact that there is a direct relationship between the mix and the yield variance and that neither of these can be considered in isolation. In addition to this, however, it is also essential to understand the importance of producing products that are of a consistently good quality. It can be tempting for production managers to change the product mix in order to make savings; these savings may lead to greater bonuses for them at the end of the day. However, if the quality of the product is adversely affected, this is damaging to the reputation of the business and hence its long‑term survival prospects. While substituting poor quality input materials may in some cases lead to yield volumes that are the same as those achieved with higher quality materials, the yield may not be of the same quality.

Unfortunately, this factor cannot be incorporated into the materials yield variance. In the long run, it may be deduced from an adverse sales volume variance, as demand for the business’s product decreases, but it is likely to take time for sales volumes to be affected. Any sales volume variance that does arise as a result of poor quality products is likely to arise in a different period from the one in which the mix and yield variances arose, and the correlation will then be more difficult to prove.

Similarly, poorer quality materials may be more difficult to work with; this may lead to an adverse labour efficiency variance as the workforce takes longer than expected to complete the work. This, in turn, could lead to higher overhead costs, and so on.

Fortunately, consequences such as these will occur in the same period as the mix variance and are therefore more likely to be identified and the problem resolved. Never underestimate the extent to which a perceived ‘improvement’ in one area (eg a favourable materials mix variance) can lead to a real deterioration in another area (eg decreased yield, poorer quality, higher labour costs, lower sales volumes, and ultimately lower profitability). Always make sure you mention such interdependencies when discussing your variances in exam questions. The number crunching is relatively simple once you understand the principles; the higher skills lie in the discussion that surrounds the numbers.

Written by a member of the Paper F5 examining team