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28 June 2023 Blog Banking

Transforming Banking with AI: Overcoming Technological Challenges to Drive Innovation

AI is the conversation everyone is having right now. Whether with palpable excitement or with deep concern about the future, it is the topic that has ignited the world.

No longer the preserve of the technically minded data scientist, AI has crossed over to the mainstream, most visibly with the phenomenal advances being made by ChatGPT, Bard and Midjourney, to bring generative AI to the people.

Leveraging the power of AI, in the services we consume, the apps we use and behind the scenes shaping a new digital future. This era of generative AI for everyone is already creating new opportunities to drive innovation, optimization, and reinvention. Unfortunately for some, this incredible rate of change has already overturned some industries and businesses.

In the same way the internet revolutionised how we shop, communicate and bank, AI will likely open many new careers. We may see some traditional jobs disappear, but the AI job market will fragment and create niches for those willing to re-train or the newly educated. We can only hope that as a global society we manage to control AI and make the most of the benefits.

So how about AI in Banking? How will the industry make the most of a data reliant tool in an area where data privacy is such a sensitive subject? And how can an industry that has historically been slow to adopt new technology, be confident in embracing a still emergent technology?

A 2022 Bank of England report[1] showed that machine learning was a key strategic consideration for 80% of financial service businesses. The same number of businesses would also apply their current data governance frameworks to reduce the risks associated. There are obvious areas for banks and financial services where AI or machine learning would easily fit and there is plenty of room for growth. Accenture notes that banking is only at 27% of its maturity scale for AI. With that in mind you would expect to see all organisations embracing some degree of machine learning or risk falling behind their peers in the market, either due to lost opportunity or not recognising potential efficiencies. 

So where are these obvious areas where AI can bring improvements into the banking industry and the customer experience? Here are a few examples:

1. Enhanced Efficiency: AI can automate simple and repetitive tasks, allowing inter bank staff to focus on more creative and complex work – arguably Pareto’s 80:20 rule may well apply in this case. This increased efficiency can lead to cost savings, faster turnaround times, and improved productivity across various banking processes. AI coupled with a proven QA framework should be able to deliver efficiency at a quality level deemed acceptable.

2. Personalized Customer Experience: AI-powered chatbots and virtual assistants can provide personalized recommendations and support to customers, enhancing their banking experience. These systems can understand customer preferences, anticipate their needs, and offer tailored solutions, ultimately improving customer satisfaction and loyalty. This could apply to new client acquisition as well as existing customer upsell opportunities.

3. Fraud Detection and Risk Management: AI algorithms can analyse large volumes of data to identify patterns and anomalies, enabling banks to detect fraud and manage risks more effectively. By continuously monitoring transactions and customer behaviour, AI can identify potentially fraudulent activities in real-time, preventing financial losses and protecting customers. Whilst algorithmic fraud detection has been in use for some time already, AI could bring a level of sophistication and adaptability not yet seen before, circumventing threats as they emerge.

4. Data-Driven Decision Making: By analysing customer behaviour, market trends, and risk factors, AI systems can help banks make more informed and data-driven decisions, allowing them to respond much quicker to changes in the market. This in turn would improve outcomes and give a competitive advantage.

handshake robot

Whilst AI brings numerous benefits, it also poses challenges and potential threats that will need to be carefully considered and managed to ensure risks are minimized.

1. Job Displacement: The automation capabilities of AI may lead to the replacement of certain job roles, particularly those involving repetitive and routine tasks. However, it's important to note that whilst AI also creates new job opportunities, such as AI system developers, data analysts, and AI ethicists, society will need to adapt and provide reskilling opportunities to affected workers to ensure responsible and beneficial growth across the board.

2. Ethical Considerations: AI algorithms can perpetuate biases present in the data they are trained on, which may result in unfair or discriminatory outcomes. In banking how we can ensure it is fair? We have moved away from relationship banking some time ago with reliance on credit rating leading to a more transparent approach to judgements, but at times also leading to consumer fear over an adverse credit score. You can argue that data bias already exists in all walks of life but with AI there is the danger of data bias being at a scale not seen before.

3. Data Privacy and Security: The increased use of AI generates vast amounts of data, including sensitive customer information. Protecting this data from unauthorized access and ensuring compliance with privacy regulations is of utmost importance. Banks and other organizations must implement robust security measures to safeguard the data feeding AI systems. Existing measures will already be in place with the management of digital data, the challenge for banks is to consider the wider distribution of data for use with AI.

4. Governance and Control: As AI becomes more pervasive, it is essential to establish clear governance frameworks and regulations to manage its deployment and mitigate potential risks. Collaboration between governments, organizations, and technology experts is crucial to develop policies that balance innovation, privacy, and societal well-being. There is no doubt that bad actors will attempt to leverage it for personal gain, but what if those bad actors were rogue nations, could the worst happen, and a single state then threaten world stability?

Finally, what about the area of banking that Formpipe operates in?

As Formpipe operates within the banking sector, albeit from a customer communication management perspective, we have also considered how we might be able to use AI to enhance our services. One area under discussion is the possibility of AI being used to re-write legacy code. AI can be utilized to automate certain aspects of code generation and optimization, however, rewriting entire legacy codebases with AI is a complex task that requires careful consideration and expertise. It is not a straightforward process and must be done with caution to ensure the integrity and security of the software being updated.

In summary, AI has the potential to bring significant benefits to society, including the banking sector. However, it also presents challenges in terms of job displacement, data privacy, ethics, and governance. By proactively addressing these challenges, society can harness the transformative power of AI while ensuring fairness, security, and accountability.

The next few years will define what individual banks need to consider keeping pace with their peers but also what we as a collective need to do to embrace the opportunity without falling foul of the potential pitfalls. 

[1] https://www.bankofengland.co.uk/report/2022/machine-learning-in-uk-financial-services


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Ben Saxton
Head of Banking