Simon Axon, International Financial Services Industry Director
The banking industry has massively benefited from Artificial Intelligence (AI). With a number of actions now being supported by AI and chatbots, the industry is seeing time-saving options to tend to customers and anticipate their every need. By creating more efficient systems, banks can cut an estimated six to ten percent off their operating budgets annually.
As industries become more familiar with AI and its capabilities, financial institutions need to find ways to hold onto their customer base while simultaneously attracting new ones. For this to be successful, it is important that banks have high quality data to implement enhanced AI into their systems.
One way to do this is by leveraging trusted AI, which is essentially AI that creates accurate, auditable, justifiable, and responsible decisions. With so much data being used, it is important that financial institutions know how to manage this information from external sources. Additionally, these models must be trained using the datasets required to make those decisions in order to guarantee that adequate decisions are made. One such model that requires heaps of data is machine learning (ML), as it is commonly used to spot fraudulent transactions after understanding a pattern for this kind of behaviour.
Whilst AI is widely used across the industry, a lot of organisations are taking a holistic approach to deploying this technology in their operations and integrating it into every part of their business. Despite their best efforts, firms are finding it difficult to be successful in this dynamic industry as they are dealing with large amounts of data and new technology, which comes with challenges of their own.
With the EU AI Act and the focus on innovation for AI in the UK, financial institutions must ensure that customers’ private and sensitive data is protected. As such, when training the AI models, it is important to not share customer and transaction details with third parties as banks are trusted to keep this information safe.
Financial institutions must also know which datasets were used to make decisions and which were used to train a model. Constantly monitoring and developing these models in line with regulations will ensure that they are created in a trusted and accurate way.
It is important to constantly adapt to change in today’s fiercely competitive market to fulfil the changing wants of its clientele. To accomplish this, financial institutions must use data analytics to provide insights that will make better decisions and streamline processes. By implementing an enterprise-wide strategy with the development of technology like AI and ML, banks will be able to predict their customers’ demands and proactively provide them with tailored products and services. This level of personalisation will allow financial services institutions to have a strong competitive advantage in the industry.
