Johnathan Bangura – VP Financial Services, Industry Solutions, Rimini Street
Artificial intelligence (AI) has fast become a key consideration for the financial services sector. Operations from across the industry have utilised its technology in recent years in an effort to gain a competitive edge. From high street banks to trading floors and insurance organisations, AI has been leveraged to make decision-making processes faster, analyse datasets more efficiently and to gain greater insights into customer behaviours.
Embracing AI
According to a report by McKinsey, AI can potentially deliver up to $1 trillion in additional value each year to the global banking industry alone, yet these possible rewards come with attendant risks. Generative AI tools, such as the large language modelling software ChatGPT, may have gained significant publicity and widespread usage recently but in the risk-averse financial services sector, businesses must be aware of several considerations before they think of adopting it.
Selecting the right technology to suit your operations is a crucial starting point, since the GenAI landscape is constantly developing and producing new functionalities for businesses to leverage. Research from Gartner found that 85% of AI projects fail to deliver due to biases in data selection and interpretation, emphasising the importance of CIOs making informed decisions on which tools and models their organisations have the expertise to incorporate.
Navigating the Risks
An unfortunate consequence of incorporating data-sharing technology and generative AI models into a business is the potential for data leaks and reputational damage. With an estimated 22% of UK businesses alone experiencing data leaks and cyber-attacks in the past 12 months, data security is a vital concern. Since GenAI tools require data-sharing in order to hone their functionality, businesses face an increased risk of exposing potentially sensitive information. Closed GenAI tools will have limited effectiveness owing to the restricted pool of data access, therefore for models to function at peak capacity sharing with centralised repositories and external organisations is key.
Not only is this data-sharing a potential reputational and business risk, with Deloitte finding that 62% of financial services firms identify data security and privacy as their top concerns when adopting AI technologies, but it also requires the implementation of new workflows that could disrupt existing processes, as well as new digital transformation strategies to safeguard intellectual property.
Approaches to Adoption
In addition to increased digital security protocols, the adoption of GenAI technology will require wholesale and significant changes to business processes. Organisations can either aim for small-scale, incremental improvements through using GenAI tools to complement existing systems or pursue a full-scale digital transformation. While fully uprooting processes with the help of AI could deliver greater benefits, it can be highly disruptive for workforces and give rise to concerns for employees about job security. To help quell this consternation, employees should be involved in the AI adoption process, while businesses should commit to achieving a tangible ROI within a relatively short timeframe, such as 12 months. It is a challenging goal, but a study by Accenture shows that 35% of C-Suite executives reported positive ROI on their AI investments..
Ultimately, since GenAI is a technology in a constant state of evolution, it is vital that organisations see its adoption as an experimental process, with room for the possibility of failure without major repercussions. In this environment, businesses can explore the benefits of this technology more readily and tailor its adoption to their needs. Equally, since several industries and competitors will be looking to adopt GenAI with a similar risk profile, financial services organisations should look for collaborative opportunities to safeguard the sector as a whole and establish standards for the future. No single organisation can tackle these challenges alone, making cooperation essential if we are going to see GenAI as a transformative tool for years to come.
