Closing the AI Gap in Banking: Leveraging Low-Code and No-Code Platforms
- AI is difficult for some bank to adopt
- The right strategy allows late-adopting banks to succeed
- A structured, enterprise-wide approach to AI deployment is crucial
Despite the transformative potential of artificial intelligence (AI), we've seen many banks struggle to scale their AI initiatives beyond the proof-of-concept stage. As outlined in a recent post on BAI.org post by Jay Venkateswaran, Business Unit Head, Banking & Financial Services at WNS, legacy systems, compliance concerns, and the fear of costly missteps often hinder progress.
Yet, it's not too late. With the right strategy, late-adopting banks can still close the AI gap and position themselves competitively in an increasingly digital landscape.
"QuickWin" AI
The key, according to Mr. Venkateswaran, is to focus on "quick win" AI use cases that offer tangible value with minimal disruption and upfront investment. This includes piloting AI applications in areas like customer acquisition, operational efficiency, and fraud prevention.
Piloting use cases in areas like customer acquisition, operational efficiency, and fraud prevention provides tangible insights into ROI and long-term viability. These pilots may include customer service chatbots, document automation, and fraud detection—areas where AI adoption can quickly enhance both internal efficiency and customer satisfaction.
A Structured Appraoch
To move beyond experimentation, the post emphasizes the need for a structured, enterprise-wide approach to AI deployment. This is where low-code and no-code AI platforms can play a crucial role.
Low-code and no-code AI platforms are another key accelerator for banks looking to bridge the AI gap. These platforms allow teams to implement AI-driven solutions with minimal reliance on deep technical expertise. For example, Intelligent Data Processing (IDP) modules can automatically extract, classify, and analyze data from unstructured sources like PDFs or e-mails. The result is enhanced operational efficiency and regulatory compliance.
And, while half (57%) of Chief Risk Officers (CROs) identify talent shortages as one of the banking industry’s most significant long-term risks, banks should understand that they do not need to go at it alone. Many AI technologies, like OrbNet AI, do not require coding, and still provide operation benefits to streamline check processing automation and increase check fraud detection to reduce fraud losses. Additionally, many of these technology providers have integration teams to make deployment seamless. Their technologies are also available through your core processor and items processor.
The concept of artificial intelligence is daunting, but make no mistake: It has never been easier to deploy AI technologies to improve operation efficiency and reduce fraud losses.