In this article, we explore:
- How AI is transforming banking by automating routine processes and assisting employees with fast access to information.
- The impact of AI adoption on the banking workforce and how employees’ roles may change as they spend less time on repetitive tasks.
- Which banking tasks can AI automate, including data processing, document analysis, customer service, fraud detection, compliance monitoring and workflow management.
- How banks can prepare their workforces for AI by mapping activities, identifying emerging skills, redesigning roles and investing in continuous development.
How does AI transform banking?
AI is creating amazing opportunities for banks. It’s already optimised operations, automated routine processes, and quickly provided employees with the necessary customer information. It’s also predicted to add between $200 billion and $340 billion in value annually, largely through increased productivity.
Banks are using AI across:
- Strengthening fraud prevention: Barclays uses AI and machine learning to analyse transaction data, identify unusual patterns and help detect fraudulent activity.
- Waiting times and call lengths: Scotiabank employees in branches and contact centres answer client queries faster and more efficiently with the use of their internal chatbot called AskAI.
- Credit decisioning: Monzo uses machine learning across areas including personalisation, customer operations, fraud prevention and credit decisioning. Its technology can use customer data and behaviour to help provide more relevant experiences and support.
But technology alone is not enough for transformation; there is also a need for people skilled in implementing and managing it.
Executives must learn to leverage these opportunities to train their employees to use AI effectively. The question of how many jobs it will take is the wrong question to ask; the broader question is how to use the capacity freed up by AI.
For example, within five months, 129 FDM Consultants supported a global bank’s operations. The teams were pre-trained in several critical skills, such as Agile delivery, DevOps, YAML and AI code validation. The result of this approach is a reduction in processing times via process automation.
Instead of using AI for cost-cutting, banks may use it for redesigning processes, enhancing human expertise, and freeing up capacity for high-value tasks.
Learn more about the full case study.
What effect does AI have on the banking workforce?
Several possible impacts of AI adoption on banking staff are:
Reduction of hours spent on repetitive tasks: By automating routine processes such as data processing, document analysis, information retrieval, and customer service, AI can reduce the time employees spend on these tasks.
Extra capacity for high-value work: By automating routine tasks, employees will have additional capacity to work with clients, address challenging situations and analyse collected data.
Learning new skills: In addition to gaining the usual banking expertise, employees will need to learn to apply AI to their work. New skills may include AI and data literacy, critical thinking and problem-solving, adaptability, and communication. Technical specialists may need to know how to create and manage AI applications and have skills such as AI engineering, cloud computing, cybersecurity and AI governance.
Banks should note that AI adoption will affect the banking workforce differently depending on the roles and tasks.
As Eoin Doyle, FDM Head of Product, explains: “AI doesn’t change what we’re trying to achieve at FDM, it changes how we get there. The organisations pulling ahead are the ones that understand the business outcome first, then the problem, then the workflow, and only then ask where AI actually lands.”
This means banks need to look beyond the technology itself and consider how AI can change the way work is organised and delivered.
How does AI change job roles in banking?
AI can help with or automate many banking tasks, especially those involving large volumes of structured or unstructured data.
The majority of jobs combine both of these kinds of tasks. Routine tasks are repetitive and rule-based, while high-value tasks require context, judgment, communication or specialised knowledge.
AI is becoming increasingly effective at supporting the first type of task.
Although it does not mean that the jobs will disappear, it means that the composition of these jobs will change.
For example, a financial analyst will spend less time gathering and organising data and more time analysing it. A technology specialist will spend less time performing routine development work and more time solving difficult problems.
And eventually, there will be a workforce that combines human expertise and AI capabilities.
This is confirmed by the fact that, among other things included in the UK Government’s Financial Services AI Adoption Plan, is the development of skills and talent.
Which tasks can AI automate in banking?
AI can assist with or automate many banking tasks:
Among them are:
- Processing and preparation of data
- Analysis of documents
- Customer service and resolution of inquiries
- Research and information gathering
- Preparation of reports
- Support of software development
- Detection of fraud and anomalies
- Monitoring of compliance
- Workflow management
The main goal is not always to fully automate all these tasks. Sometimes the benefit comes from reducing friction in the process.
For example, the AI system will be able to prepare all the information that the banker needs before the meeting with a customer. And the banker can use this time to understand the client’s situation and goals and make decisions.
Such collaboration between humans and AI is likely to become one of the key features of the bank’s workforce.
How can banks prepare their workforces for AI?
Preparation for AI involves more than using technology or conducting one-time training courses. Our research shows that 32% of businesses point out a shortage of specialist skills as a top barrier to tech adoption. Below are ways in which organisations can prepare their workforce.
1. Map the work
Understanding which activities are repetitive, which require specialised knowledge, and which rely on human judgment.
2. Define required skills
After the activities have been redefined, organisations can identify the skills employees need to acquire to perform their new roles.
The required skills can involve AI literacy, data skills, cybersecurity skills, cloud skills and human skills.
3. Redefine roles based on value
Rather than simply integrating AI tools into existing processes, organisations need to understand whether those processes require any evolution.
4. Develop skills on an ongoing basis
The adoption of AI is not a one-off event. Hence, organisations need to develop skills as expectations change continuously.
The ongoing development of employee skills needs to become an integral part of the strategy for adopting AI.
Why is transforming a workforce necessary for adopting AI?
Technological innovations alone do not drive transformation.
A bank can have highly advanced AI systems and yet fail to realise their full potential because its employees lack the skills, confidence, and procedures necessary to use them effectively.
Transformation of the workforce should occur at the same time as the adoption of AI.
The financial services industry in the U.K. has already realised this linkage.
According to governmental analyses, the industry needs to develop the skills and talent needed to adapt to the growing adoption of AI technologies – further workforce transformation is expected as part of the U.K.’s financial services skills agenda.
The recent appointment of HSBC’s first Chief AI Officer is a clear signal that AI considerations are being taken seriously.
Organisations should create environments that foster employees’ learning, experimentation, and adaptation as their roles evolve.
This implies that banks should view the adoption of AI not as a technological transformation but also as a workforce transformation.
Conclusion
The future of AI in the banking sector is unlikely to be determined solely by the number of tasks machines perform.
It will be determined by how effectively banks are integrating AI with human capabilities.
The banks that will benefit the most from AI adoption will probably be those that understand where the technology can remove friction from processes, where the most value is created by humans, and how technology and people can work together.
How FDM can support
FDM supports organisations through a multi-tiered approach to building resilient defences against AI-powered threats.
Consultants from our IT Operations and Risk, Regulation & Compliance Practices have the skills that matter to our customers and know what can affect them and what’s changing in the industry. The foundation for this is training and ongoing support.
Businesses that invest in people-powered, forward-looking strategies will not only stay ahead of evolving fraud tactics but also protect their organisations from financial and reputational damage.