GenAI is quite different from other forms of artificial intelligence. Many business leaders expect it to behave like conventional software, providing a consistent set of results for the same inputs. However, rather than following a strictly defined deterministic path, GenAI uses probability to generate results – which may vary.
To understand the difference, consider a scenario in which three academics are given an identical writing task. All three academics complete their articles with the same level of professionalism, but just like probabilistic GenAI, each article is unique. This variance and non-repeatability is an important area to consider for heavily regulated financial services companies, as GenAI may not be the appropriate tool when certainty is required to ensure compliance.
Other key risks associated with GenAI include:
- Privacy and data security: Generative AI models require large amounts of data to train and generate content. This data must be protected, anonymized or consented to – to comply with data privacy laws and regulations, such as the Privacy Act 1988 or the Notifiable Data Breach Scheme.
- Ethical and social implications: GenAI algorithms need controls or guardrails to ensure that the results are unbiased and accurate. For example, a GenAI tool may produce inaccurate or unfair credit scores, loan offers, or investment advice based on biased or incomplete data.
- Legal and regulatory compliance: For example, GenAI models may produce documents that do not meet the standards of clarity, completeness or disclosure required by the Australian Securities and Investments Commission (ASIC) or the Australian Prudential Regulatory Authority (APRA).
It is important to note that GenAI models are trained on general knowledge across a wide range of topics. Therefore, GenAI models typically require additional training to understand financial services terminology and data. GenAI also leverages rapid engineering – a methodology for asking GenAI properly structured questions, designed to provide the most consistent results.
However, GenAI excels at text aggregation and synthesis, evaluating large volumes of data, and cognitive search in large enterprise knowledge repositories. When used in the right context, GenAI can bring a step change in the productivity of knowledge workers. For example, an EY client was able to double their capture rate of high-risk clients using GenAI.
