For years now, there has been a growing interest in trust and reputation related areas across industry sectors. Some of these areas include corporate social responsibility, regulatory compliance, good financial results, innovation, communication, brand identity, or the incorporation of ESG criteria in management. In short, areas that impact the long-term sustainability of organizations.
Recent scientific studies based on more than 300 publications have confirmed a causal relationship between these areas and reputation: sustainability leads to trust, and trust leads to good corporate reputation, which in turn generates income and profitability. This confirms the intuition that it is essential to pay the utmost attention to preserving stakeholder trust as the pillar of corporate reputation and other intangible assets that ultimately are the drivers of profitability.
Trust and reputation: proactive management of reputational risk
But, what are trust and reputation?
On a textbook definition level, trust is the firm belief in the reliability, truth, or ability of someone or something, and reputation is the beliefs or opinions that are generally held about someone or something . Both definitions are inherently subjective to some extent: trust and reputation are based on perception and opinion, not necessarily on verified facts, are built from information that comes from different stakeholders (analysts, specialists, market participants, etc.) in different formats and media (publications, reports, news, social networks, etc.).
This subjectivity is especially relevant in a context characterized by immediacy and ease of access to communications: information spreads to online media and social networks in a matter of seconds, users share data and opinions on the web in real time and with hardly any filters. This means that a reputation crisis can develop at high speed, with the truthfulness of the facts often relegated to the background and left unchecked due to lack of time to confirm them, which poses a challenge to managing the impact of such a crisis.
All this has led to a greater interest in reputational risk across industries. This risk is often incorporated into the ESG risk framework (as prescribed by the COSO principles, for instance) and, in the particular case of the financial sector, is defined by the European Banking Authority as “the current or prospective risk to the institution’s earnings, own funds or liquidity arising from damage to the institution’s reputation". Reputational risk has not traditionally been regarded as a prime risk, but the already discussed factors, together with the amount of high-loss and even bankruptcy cases due to reputational events in recent years, are drawing the attention of regulators, large financial institutions and corporations to this risk.
Although regulations have attempted to lay down requirements for identifying, measuring and managing this risk, the inherent difficulty of this task has meant that at present the level of regulatory development and standardization is lower for this risk than for others. In any case, regulators and supervisors continue to work towards incorporating reputational risk into the strategic risk management processes of corporations and financial institutions.
This is also reflected in the fact that companies are developing reputational risk management frameworks, still incipient in most cases, which in their most advanced form cover all relevant risk management areas: governance, three lines of defense organization, policies and procedures, data and models, scenario analysis and stress testing, reporting and limits, and particularly communication, given the nature of this risk.
Traditionally, organizations have tried to measure reputational risk from information obtained through indices, surveys, qualitative analysis, etc. To this must be added at least three new components: i) the exponential growth of immediately available data (e.g. data from social networks and useful digital press sources); ii) the development of artificial intelligence and machine learning techniques such as natural language processing and deep neural networks, aimed at data processing, content interpretation or sentiment analysis; and iii) the availability of low cost, mass processing capabilities.
All this marks a turning point in reputational risk measurement: possibilities that were previously unfeasible are now feasible at a reasonable cost. There are now tools for identifying and labeling potentially harmful news, sentiment analysis models, reputational risk measurement tools, and scorecards with KRIs for internal management.
In this context, this study aims to provide a comprehensive view of reputational risk management. The study is divided into three sections, which are intended to:
- Describe the context for and regulations on reputational risk.
- Present the components of a reputational risk management framework.
- Examine the use of quantitative techniques applied to reputational risk management using advanced artificial intelligence and machine learning methods.
Finally, the document intends to illustrate how all these components are being implemented in practice in large corporations and global financial institutions.