Will generative AI kill the low-code rule engines used in banks?

April 1, 2024

The question of whether generative AI will "kill" the business rule engine or decisioning platforms used in banks is complex, and the answer depends on multiple factors, including how these technologies evolve, how they are implemented in banking workflows, and the skill set availability with the banks.

Lets’ look at some of the considerations before we come to a conclusion.

1. Generative AI and low-code platforms may evolve in complementary ways rather than in a direct competition with each other. Low-code platforms enable quick development and deployment of applications with minimal coding, which can be crucial for banks looking to implement solutions rapidly. Generative AI can enhance these platforms by providing advanced predictive analytics, personalized customer experiences, and provide the resultant decisions in a targeting user friendly manner.

2. Generative AI could augment the capabilities of low-code platforms by providing more sophisticated data processing and analysis tools. This can lead to more powerful and intelligent applications being developed on low-code platforms, enhancing their value rather than diminishing it.

3. Certain banking functions may be more amenable to generative AI solutions, such as personalized customer interaction, fraud detection, and risk assessment. However, low-code platforms may still be preferred for developing and deploying a wide range of banking applications that require rapid iteration or that need to adhere to strict regulatory requirements. The flexibility and control offered by low-code platforms can be crucial in these contexts.

4. Banks operate in heavily regulated environments, where compliance and security are paramount. While generative AI offers powerful capabilities, its applications will need to navigate these regulatory landscapes. Low-code platforms, often designed with these considerations in mind, may offer more straightforward paths to compliance.

5. Low-code platforms democratize the development of applications by making it accessible to non-programmers, which can be a significant advantage in resource-constrained environments. Generative AI, while powerful, requires a different set of skills to develop and manage effectively. The integration of generative AI into low-code platforms could make advanced AI capabilities more accessible to a broader range of users within the banking sector.

6. AI generated code is not a replacement for visual coding tools. Business users still require a visual, declarative development experience. Second, because natural language is insufficient as the sole authoring experience of software, being still both imprecise and inefficient for capturing complex ideas. Put another way: The visual tools that form the core developer experience of low-code platforms (process diagrams, entity relationship diagrams, WYSIWYG UI canvases, etc.) are still required to express the intent of the software being built, manage it, and know what it actually is and does. But, once matured, natural language prompts will become a normal, complementary method to interact with the required visual tools — both for drafting new apps and iterating on them.

Early examples

1. Google's AutoML leverages Generative AI to assist in low code automation by automatically generating machine learning models. It enables developers with limited machine learning expertise to create custom models that cater to specific business needs.

2. GitHub Copilot developed by GitHib and OpenAI that assists users of Visual Studio Code, Visual Studio, Neovim, and JetBrains integrate development environments by autocompleting code.

3. Salesforce's Einstein platform incorporates Generative AI to provide intelligent suggestions and predictions for app development within their low code environment. This enables developers to create smarter applications without diving deep into code intricacies.

So, in conclusion, while generative AI presents a transformative potential across various industries, including banking, it is more likely to transform low-code decisioning platforms rather than rendering them obsolete. The future could see a more integrated approach, where generative AI enhances the capabilities of low-code platforms, leading to more intelligent, efficient, and user-friendly banking solutions.

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