#The five biggest artificial intelligence trends for banking in 2024
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As we enter 2024, it is likely that there will be expeditious growth in both adoption and effectiveness in the areas where it is already used, with banking continuing to remain at the forefront of the “real-world” adoption of AI.
Banks will use AI in financial literacy support, savings and investment planning, and, more generally, to help individuals and businesses to optimise their financial situation.
This will include improving the customer experience through personalised insights and enhanced security in banking apps, which will help support customers in an era of fewer brick-and-mortar bank branches and more channels for digital engagement.
This will only accelerate as banking becomes increasingly embedded into other areas of life. However, as access to data continues to grow, so too will the pressure for increased regulation and the adoption of best practices to ensure the ethical and responsible use of the technology. As leading trends in AI for banking in 2024, these are as follows.
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1. Generative AI
The rise of generative AI promises to unleash a wave of innovation, efficiency, and personalisation for banks and their own customers. This can revolutionise how banking operations and services are delivered. It can also create novel and unique services, deliver huge efficiencies for banking operations, and change the way end users interact with banking.
According to McKinsey, across the banking industry, the technology could deliver value equal to an additional $200 billion to $340 billion annually. This can result from various use cases and applications, allowing huge efficiencies in the banking backend. Banking customers are also going to witness improved support as well as unique banking services and experience.
2. Responsible AI
With the rise of use of AI in banking and finance applications, there will be a need to have true explainable AI whose models could be easily understood, analysed, and augmented by the business stakeholders and the regulating authorities.
In addition, there is a need for the outputs of these models to be easily understood and analysed by the lay user. There is also a need to make sure that the outputs of these models are not biassed (against any customer sector or demography) and they are fair and safe.
Indeed, trust and bias continue to be prominent barriers for AI adoption. For example, Apple’s algorithms — used to decide whether to grant credit lines — were accused of gender bias in 2019, allocating fewer credit lines to women than men.
With these risks at the forefront of AI technology for “high-risk” applications, such as credit scoring, banks are likely to feel increased pressure from regulatory and customer influences. So, the “explainability” of AI is key, and banks will need to establish a set of processes that allow users to understand the output created by machine learning algorithms.
The success of AI will be enhanced visibility of explainable AI to spot and correct potential flaws and vulnerabilities in models.
3. AI governance
Most governments and regulating authorities all over the world are working on tight AI governance that will allow access to the full power of AI while dealing with it as a safe and useful technology with its own regulation and governance to safeguard any inadvertent repercussions.
There will be an increased need for tight governance and compliance processes for the safe use of AI in different banking and financial institutions.
4. AI to realise financial wellbeing
Financial wellbeing will be a very important concept that explainable AI can help to realise for banks and financial institutions. For instance, managing bank end processes, intra-day liquidity forecasting, sentiment analysis, etc.
It will also be of benefit to customers, by forecasting cash flow and support in case of financial difficulty, or help to pick the best suitable mortgage or help in wealth advice, for example. Explainable AI will help underpin stable financial markets, as well as healthy finance support for banking end customers.
5. Expanding data sources
With the rise of the Internet of Things (IoT) and social media, more data will become available about the banking industry and its end customers. AI can play an important role in extracting full value from unstructured social media data and huge volumes of IoT data and fuse this with the customer banking data.
This will allow the banking apps to help and support the banks and their end customers in extensive ways which allow the production of novel unique services which can change the face of banking for the years to come.
Hani Hagras is Chief Science Officer and Head of the AI Business Unit at Temenos, the banking software company. Temenos is a world leader in Explainable AI and developing Generative AI with ethical and responsible deployment in banking. Hani is also a Professor of Artificial Intelligence at the University of Essex, where he is Director of the Computational Intelligence Centre and Head of the AI Research Group.
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