The ultimate checklist for AI success in banking

What’s the customer problem you’re trying to solve?

Kerim Alain Bertrand, Senior VP of Growth Western Europe at VeriPark, emphasises the importance of understanding the customer problem you're trying to solve with AI. Whether it's fraud detection, risk assessment, customer service automation, or personalised marketing, AI can bring considerable value to both customers and employees. AI is not a ‘magic potion’ so we need to be able to clearly articulate the value and the benefit.

Assess existing data availability and quality:

The success of AI implementation hinges on the quality of data. Significant effort is required to train AI with real-world data that is comprehensive, relevant, and properly structured for optimal results. Think of it as an ambitious untrained intern – who is responsible for training it?

Data privacy, security and compliance:

Security is paramount in the financial sector. Robust security measures and refined data governance policies are essential to protect sensitive customer information and ensure ethical and responsible use of AI technologies, especially in compliance with regulations like the European AI act.

Build a skilled AI team:

Richard Kalas, Client Solutions Director at GFT Technologies, underscores the need for a skilled team comprising data scientists, AI engineers, domain experts, and compliance officers to successfully implement generative AI in the financial industry.

Despite the seemingly user-friendly interfaces readily available, to create and build a practical and robust tool, you will need a team of people who understand both the technical aspects of AI and the specific requirements of the financial industry.

Evaluate your existing infrastructure and scalability:

AI is an amazing enabler to accelerate the transition to the cloud for banking operations - see it as an opportunity to make the transition. Many security concerns can be mitigated by transitioning to the cloud. Partnering with Mambu can facilitate a seamless transition, enabling scalability and integration with existing systems for a more efficient AI adoption process. Read more about how we can help.

Develop responsible AI practices and guidelines:

Responsible AI practices should prioritise the role of humans in overseeing and guiding AI systems to ensure fairness, transparency, and accountability. Annette Harris, GM and Head of Financial Services Netherlands at Microsoft, stresses the importance of mapping clear guardrails for AI usage to mitigate biases to ensure the well-being of society and ensure compliance with regulatory standards. AI is inherently discriminatory therefore regular evaluation and auditing of AI models to ensure compliance are crucial for ethical AI practices.