MARIUS VAN DEN BERG: How SA banks can deploy and benefit from generative AI

Generative artificial intelligence (AI) is a groundbreaking technology with the potential to revolutionise the banking industry in SA — but not without some risk. With its ability to analyse vast amounts of data and generate new insights, generative AI is becoming an increasingly powerful tool for banks to enhance their operations and improve customer service. 

The major opportunity is going to be enhancing the productivity of knowledge workers through the use of AI “co-pilots”. Consider the number of management reports, PowerPoint presentations and Excel models at modern banks — all developed manually by humans. Using generative AI technologies to pull data off the system and generate your board report or response to a customer complaint will mean a productivity gain of two to three times. Humans will need to be kept in the loop, as for now the accuracy will need to be checked.

A major benefit of generative AI in banking is its potential to enhance credit scoring models. Traditional credit scoring methods rely on historical financial data, such as credit history and income, to determine an individual's creditworthiness. However, generative AI can go beyond these limited factors and incorporate a broader range of data points. By analysing unconventional variables such as social media behaviour, online shopping patterns and even sensor data from wearable devices, generative AI can provide a more comprehensive and accurate assessment of an individual’s creditworthiness. This approach has the potential to open up credit access for individuals who may not have a traditional credit history but demonstrate responsible financial behaviour through alternative means. 

Fraud prevention is another area where generative AI can have a significant effect on banking. By analysing transactional data and identifying patterns that may indicate fraudulent activity, banks can use generative AI to create predictive models that alert them to potential fraud before it occurs. This proactive approach helps banks prevent financial losses and safeguard customers’ accounts from unauthorised access, fostering greater trust and security in the banking ecosystem.

Generative AI also means banks will be able to automate even more of their routine administrative tasks. This may include enhancing current automation technologies, for example in the area of digitising documents, paper-based loan applications or processing vendor invoices. 

Improved customer service is another major benefit of generative AI in banking. Through the implementation of generative AI-powered chatbots, banks can provide round-the-clock support to customers, addressing queries and resolving issues in real-time. These chatbots can be programmed to understand and respond to customer needs, delivering personalised recommendations for financial products and services based on individual preferences. The result is enhanced customer satisfaction and engagement, as well as improved efficiency in query resolution.

“Co-pilots” can also help in the front office but the systems need to be populated with company-specific data. The benefits include being able to respond to the customer query much more quickly and accurately as these systems can reference internal documents and with less reliance on onboarding and training, which means that staff turnover is less of a challenge. Customer experience will improve through less time on hold and fewer transfers between departments, a well-known source of frustration to anyone who has bounced from person to person when trying to resolve a query with a company. 

Another big opportunity we see in retail banking is a new type of interface that relies on natural language. In the banking context we may see apps in future where customers are able to use natural language to interact, for example “Pay my mother” rather than navigating a maze of menu items. 

However, along with its benefits generative AI also presents challenges that must be addressed to ensure responsible and ethical usage. One significant concern is the potential for algorithmic bias. If the algorithms used to generate insights are not designed with diversity and inclusion in mind, they may inadvertently perpetuate discriminatory practices. Banks must prioritise fairness and transparency in algorithm development. 

Data privacy and security are additional critical considerations when implementing generative AI in banking. Banks must take measures to ensure that customer data remains confidential and secure, adhering to privacy regulations and industry best practices. It is essential to strike a balance between leveraging data for valuable insights and safeguarding sensitive information.

Open AI and IBM recently appeared in front of the US Congress on the need to regulate AI both at a national and global level. It was clear from that discussion that it is just a matter of time before AI regulation is implemented in the US. The EU has a draft bill that is going to be promulgated in the near future. It is therefore likely that regulation across other nations such as SA will follow suit quite quickly. 

That said, generative AI also plays a vital role in regulatory compliance within the banking sector. The technology can assist banks in analysing large volumes of transactional data, identifying suspicious activities and ensuring adherence to regulations. By automating compliance processes, generative AI helps banks mitigate risks, avoid penalties, and maintain the integrity of the financial system.

Generative AI holds immense potential to transform the banking industry as part of banks’ broader business strategy. By embracing generative AI with caution and ethical considerations, banks can unlock new possibilities, redefining their role in serving customers and operating in the digital age.

• Van den Berg is EY Africa banking & capital markets consulting leader.