Ethics

1 Introduction

Ethics has been left to the final chapter, but it pervades the whole E1 syllabus. The syllabus names it in three places: the role of ethics in the role of the finance function (E1A), the ethics of technology usage (E1B), and the ethics of data usage (E1C). When we discussed technology and data, there were ethical issues about privacy, surveillance and the careless handling of information. In marketing, claims about products must be justifiable. In human resources, people must be treated fairly and their personal information protected. This chapter draws the threads together: what ethics means, why it matters commercially, CIMA's ethical principles, and the newer ethical questions raised by data and artificial intelligence.

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2 What are ethics?

2.1 Definition

Ethics is concerned with distinguishing between good and evil, between right and wrong human actions, and between virtuous and non-virtuous characteristics of people and organisations – and with the rules and principles that ought to govern behaviour.

Ethics goes beyond the law. Behaving legally is not optional; ethics is about how to behave where the law does not compel you – and an action can be perfectly legal yet widely seen as wrong.

Ethics pervades business. For example:

• Technology and data – privacy, surveillance, collecting information covertly, monitoring employees, relying on algorithms to make decisions about people.

• Business strategy – moving operations abroad, the approach taken to competitors, monopoly building.

• Finance – paying suppliers on time, realistic forecasts, proper accounting.

There are various philosophical approaches to ethics:

• Absolutists believe certain actions are always wrong – no ifs or buts.

• Relativists (pluralists) believe nothing is objectively right or wrong: 'right' depends on the prevailing view of a particular individual, culture or period.

• Consequentialists judge an action by its consequences or outcome.

These labels are not of great importance to this syllabus: the case for good ethics in E1 is made through good, sustainable business – as the next section shows.

3 The importance of ethics in business

Different stakeholders hold different ethical views. On a crowded train, some standard-class passengers see nothing wrong in sitting in first class ('I've bought a ticket, I should have a seat'); other passengers, and the train company, see it differently ('you can buy extra comfort if you want to'). Some shareholders have ethical objections to their company manufacturing arms; directors and employees may have none.

Perhaps what matters most is that stakeholders are informed about a company's ethical position, so there is openness and everyone understands where it stands. Corporate codes of ethics help achieve this: documents issued to employees that establish ethical rules or guidance – how to behave if offered a bribe by a supplier, what to do about a machine in a dangerous condition, what fair treatment of customers and colleagues requires. Many large companies publish their codes so outside stakeholders can see the standards the company claims to hold – typical provisions require employees to deal fairly and honestly with customers and suppliers, to avoid deceptive or misleading practices, and never to offer or accept anything in exchange for an improper advantage.

Even where stakeholders disagree about ethics, ethical behaviour is linked to profitability and financial viability. An organisation can gain a short-term advantage from an unethical act – concealing a drug's side effects to boost sales, say, or cheating an emissions test – but most ethical breaches are eventually discovered, and the long-term damage, financial and reputational, is huge.

Good ethics therefore:

• Reduces risk for shareholders

• Lowers the cost of capital – risk and the cost of raising funds are linked

• Increases goodwill towards the company, improving sales

• Makes regulatory compliance easier, reducing fines and damages

• Attracts good candidates to a company with a good reputation

• Makes joint ventures and partnerships easier to form.

4 Ethics and the role of the finance function

The 2027 syllabus explicitly includes the role of ethics in the role of the finance function. The connection is trust. Chapter 2 described finance as enabling, shaping and narrating the creation and preservation of value – and every one of those roles collapses if finance's output cannot be trusted:

• Enabling – plans, forecasts and resource allocations are only useful if they are honest. A deliberately optimistic forecast misallocates the organisation's resources.

• Shaping – performance management only works if the figures are objective. Massaged numbers reward the wrong people and hide the real problems.

• Narrating – corporate reporting exists to give stakeholders a faithful account of value creation. Misleading reporting destroys the trust of investors, lenders and regulators – usually permanently.

To a large extent, being ethical is the professional accountant's unique selling proposition: if an accountant cannot be trusted, there is no point paying for their work. This is why finance professionals are bound by a professional code of ethics, to which we now turn.

5 CIMA's ethical guidelines

CIMA's Code of Ethics is based on the International Code of Ethics issued by the International Ethics Standards Board for Accountants (IESBA). All CIMA members and students are required to comply with it.

The five fundamental ethical principles are:

Compliance is continually threatened. Personal relationships can threaten integrity and objectivity – an accountant may be reluctant to report errors made by colleagues; pressure from a superior to 'adjust' a figure threatens integrity; a bonus that depends on reported profit threatens objectivity. Professionals must recognise such threats and reduce them to an acceptable level – or refuse to act. The Code and its detailed framework for handling threats are examined further in your wider CIMA studies; for E1, know the five principles and be able to spot situations that threaten them.

6 The ethics of technology and data

The syllabus singles out two modern applications of these principles: the ethics of technology usage and the ethics of data usage. The technologies and data practices of Chapters 3 and 4 – big data, automation, artificial intelligence – create ethical problems that simply did not exist a generation ago.

6.1 Data ethics

Organisations now hold enormous quantities of personal data about customers and employees. Using it ethically means:

• Privacy, lawful basis and consent – collect personal data openly, for stated purposes and on an appropriate lawful basis; where consent is relied on it must be valid. Do not quietly re-use data for an incompatible purpose.

• Proportionate surveillance – monitoring employees and customers may be lawful, but covert or excessive monitoring destroys trust and is widely seen as unethical.

• Security – holding data creates a duty to protect it. Careless storage, weak passwords, unencrypted laptops left in cars: losing other people's data is an ethical failure as well as (under data-protection law) a legal one.

• Honest use – analytics can be used to exploit as well as to serve: targeting vulnerable customers, or pricing by what each individual can be made to pay, may maximise short-term revenue at the cost of fairness and reputation.

6.2 The ethics of AI and automation

When algorithms and AI make or influence decisions – credit approvals, recruitment screening, pricing, forecasting – further issues arise:

• Bias – a model trained on historical data learns historical prejudices. A recruitment-screening model trained on past hires can systematically disadvantage groups the organisation under-hired before.

• Explainability and transparency – people affected by a decision are entitled to an explanation. 'The computer says no' is not one. Some powerful models are genuinely hard to explain, which is itself a reason for caution in sensitive uses.

• Accountability – responsibility for an automated decision cannot be delegated to the machine. Someone in the organisation must own the outputs, check them and answer for them.

• Generative AI – brings its own hazards: outputs that are fluent but wrong (hallucination) must be checked before use, and confidential company or customer data must not be pasted into public AI tools where it may be stored or reused.

7 Test your knowledge

Two quick checks before you move on: work through the flashcards to fix this chapter’s key terms and definitions, then sit the objective questions for exam-style practice. Both mark themselves and explain the answers as you go.