AI code generation – what do banks need to know and beware?

In the ever-evolving landscape of financial technology, artificial intelligence (AI) has emerged as a largely transformative force. As banks seek to innovate and improve efficiency, AI-driven code generation represents one of the most promising advancements. However, whilst this technology offers significant opportunities, it also introduces new complexities and risks that demand careful consideration. 

AI code generation refers to the use of AI technologies to create software codes automatically. This technology leverages algorithms and advanced machine learning models to generate code that can perform a variety of tasks within software applications. Essentially, AI becomes an active participant in software development, potentially reducing the time and human effort required in coding.

Gartner predicts that 75% of software engineers will use AI coding assistants by 2028. Whilst I don’t doubt this to be directionally true, it does not mean engineers’ role disappears, but they will need to evolve.

Advantages and Innovations in Banking through AI

AI code generation is fundamentally altering how banks approach software development. By automating routine coding tasks, AI enables developers to focus on more strategic projects and complex problem-solving, amplifying the creative and intellectual capabilities of human engineers. This shift not only accelerates the development cycle but also enhances the quality of the output. It’s also being applied to generate documentation, data mapping and interface details for legacy applications. This makes it easier to then modernize and transition processes to newer systems.

Along with AI, GenAI’s capability to generate synthetic data is particularly advantageous for the sector. Financial institutions often grapple with the dual challenges of data sensitivity and the need for massive datasets for training machine learning models. Synthetic data serves as a powerful tool, providing high-quality, anonymised datasets that mimic real-world data distributions without compromising client confidentiality. 

Additionally, GenAI contributes significantly to the optimisation of algorithms. Through advanced machine learning techniques, systems can now self-adjust and improve over time, based on continuous feedback loops. This means that the algorithms powering banking operations, such as fraud detection or risk assessment, become more effective and efficient, driving performance enhancements across the board.

The Risks Associated with AI Code Generation

Despite its numerous benefits, AI code generation is not without its risks. The primary concerns for banks revolve around control, security, and compliance. 

• Loss of control - Automating code generation can sometimes mean less oversight over the exact functions and operations of the code. This can lead to inconsistencies and unexpected behaviours in software applications, which can be particularly problematic in the highly regulated banking industry.
  
  
• Security vulnerabilities - Generated code can also introduce new vulnerabilities. The complexity of AI models can make it difficult to identify security flaws, and the reliance on external data for training these models may open new avenues for data breaches.
  
  
• Compliance issues - Regulatory compliance is paramount in banking. Automated systems must adhere to all relevant laws and regulations, which might not always be up to date with the latest AI advancements. Moreover, the opaque nature of some AI systems, often referred to as the "black box" issue, can make it difficult for banks to demonstrate compliance with regulations that require transparency in decision-making processes. 

Given these risks, a cautious approach towards AI in code generation is advisable. Banks should consider robust testing and audits before deploying AI-generated code to ensure the code meets quality and compliance standards. They should also opt for AI tools that offer transparency in their algorithms’ decision-making processes. This is crucial not only for regulatory compliance but also for gaining trust from customers and stakeholders. Having a ‘human in the loop’ to review and have oversight of risk issues above is not just advised, but essential. Internal audit and external regulators will demand it, and management should do it as a matter of course. This is no different to monitoring the performance of credit risk changes at all levels from individual decision making to system automated results.

Towards a Smarter Banking Future

AI systems are not set-and-forget solutions, they require continuous monitoring and regular updates to ensure they remain effective and secure against emerging threats and are performing as expected. Collaborating with technology providers who have established expertise in AI can help mitigate risks. Banks should look for partners who understand the intricacies of both AI and the specific regulatory as well as operational challenges faced by the financial industry. 

AI code generation holds significant promise for banks, offering the potential to drive innovation, reduce costs, and enhance service delivery. However, the adoption of this technology must be managed prudently, with a clear strategy for addressing the associated risks. By doing so, banks can not only capitalise on the benefits of AI but also protect themselves and their customers from potential pitfalls, paving the way for a smarter, more secure future in banking.