In the fast-evolving world of artificial intelligence, confusion often reigns. While AI promises transformative potential, understanding its true value – especially in the context of financial services – requires separating fact from fiction. Here, we debunk the most pervasive myths, offering clarity and actionable insights for financial institutions.

Myth #1: AI Is a One-Size-Fits-All Solution

Many businesses mistakenly believe that AI can be applied universally, or that a product exists that is the magic bullet. The truth is, the success of AI depends on aligning the right tools with specific business challenges, whether it’s reducing costs, enhancing customer experience, or driving revenue. Financial services organisations must tailor their AI strategy to their unique needs and goals.

Myth #2: AI Is a Standalone Solution

Some still believe that AI operates in isolation, an autonomous entity capable of transforming entire systems on its own. In reality, AI must be embedded within an organisation to work alongside people and other technologies to help transform processes. Effective AI implementation requires thoughtful integration into the broader operational model, ensuring smooth collaboration between AI and human expertise.

AI in Action

As specialists in Financial Services, we have deep expertise in helping our clients to identify, navigate and implement AI use cases. In the Projective Group we have already worked with several companies in the European financial services sector to deliver AI success stories. There are obvious tasks where AI can be of great benefit. Here is a snapshot of where we see AI being successfully used in the market.

1. Eliminating errors and ‘waste’: Across the international payments networks, there remains a relatively high proportion of failed transactions. In general terms, the potential exists to integrate learning agents to the hardware / software solutions to reduce or even eliminate such errors. Such tools could start with basic, rule-based error-checking. Over time, they can learn the local patterns and the flag those that fall outside those boundaries. In a fully-fledged implementation, the tools could suggest remediation and even be given (limited / controlled) capabilities to auto-correct. We built a prototype of this capability for SWIFT.
   
2. Simplify Reporting: Financial services companies have huge reporting requirements. Many employees spend their days compiling, amending, and rewriting reports. AI can help reduce the duplication of tasks and simplify processes. It can even predict how data in these reports can be used. AI can explain complex report content and remove duplication. Moreover, it can significantly speed up large-scale data quality issues through classification, corrections, and processing unstructured text. The cost reduction and revenue generation benefits of this are easy to spot.
   
3. Credit Risk Models and Fraud Detection: Banks are already successfully using AI to assist them in activities that range from the creation of credit risk models through to fraud detection (with a reduction in false-positive rates).
   
4. Read, Summarize and Create Code: AI applications can read, summarise and create programming codes (e.g. in Cobol), but also eliminate technical legacy. This enables companies to better understand how their platforms work and how they can make better use of them.
   
5. Chatbots (LLMs): Chatbots are invaluable research tools and can take on a huge amount of the role of your customer services department. They are good at summarising legal documents and never tire of completing onerous KYC and AML checks. We have used AI to build a chatbot for one client.

The development of AI is progressing rapidly – what is cutting-edge differentiator today becomes a commodity feature tomorrow. The toughest part of any initiative is moving from PoC to production, where regulatory, security, and scalability challenges arise. Leaning on established vendors for certifications and support can help, as can a focused AI development strategy. ‘AI’ may be the future, but it is not a miracle cure. Your company will still have to consider risk factors, the cost of adoption and the unavoidable fact that not everyone has the skills to use it effectively.

We can help to structure and implement a realistic and clear AI strategy. A strategy that will allow you to use artificial intelligence to deliver real value.

This article was first published in the Payment & Banking white paper titled “AI Use in the Payment Industry.”