CHAPTER ONE INTRODUCTION 1.1   Background to the Study

Confidence accounting is a new development and radical approach to accounting. Confidence accounting is a probabilistic approach as opposed to the traditional deterministic one. Confidence accounting is gradually evolving based on the promise that I might lead to better financial decisions by enabling decision makers to take an imager term view or an balance the odds as well as the books (Long finance news 2013).

According to Moxley (2013), confidence accounting has the tendency being worked up into a method which might give insight into the accounts of complex organizations. Sharing from this varying view, it can be deduced that the debate on confidence accounting is high and still on going. The sharp argument is premised on the fact that confidence accounting stands to enhance financial reporting majorly in developed countries of the world though the concept is still very vague in developing countries such as Nigeria. Accountants should adopts a more scientific approach to measurement and difficult-to-assess figures according to professor Micheal Mainelli FCCA who makes the case of confidence accounting.

A decade ago, a series of failure embarrassed auditors. Large firms with successful-looking financial statements collapsed. Today, the audit process is under fore once more as questions are asked about why problems at large financial institutions were not spotted earlier, surely, this is a good time to rethink auditing.

People who move from science to accounting are stunned to find that auditors do not practice measurement science. Measurement is about both accuracy and precision. Accuracy – how closely a stated value is to the actual value. Precision – how likely it is that repeated measurements will produce the same results. A measurement system can be accurate but not precise, precise but not accurate, neither, or both. Scientists view measurement as a process that produces a range. Scientists express a measurement as X, with a surrounding interval.

There is a big difference between point estimation and interval estimation. Auditors provide point estimates, scientists intervals. For example, physical scientists report X±, social scientists report interval estimates for an election poll and state how confident they are that the actual value resides in the interval. Statistical terms, such as mean, modem, median, deviation, or skew, are common terms to describe a measurement distribution’s look and feel. The key point is that scientists are trying to express characteristics of a distribution, not a single point finance should be no different.

For want of a term that distinguishes the use of distributions from the use of points or discrete values, let’s use confidence accounting. In a world of confidence accounting, the end results of audits would be presentations of distributions for major entries in the profit and loss, balance sheet and cash-flow statements. The value of freehold land in a balance sheet might be stated as an interval, €150.000.000 + 45.000.000, perhaps recognizing a wide range of interesting properties and the illiquidity of property holdings. Next to each vale would be confirmation of the confidence level, e.g. 95% confidence that another audit would have produced a value within that range.

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