Nowadays, artificial intelligence has become one of the most discussed topics around the world. Its potential is so enormous and essentially versatile that it raises major ethical issues and problems alongside its many benefits. The financial sector has become one of the key areas where AI is playing an increasing role. However, controversy is growing along with it, as it brings with it a number of risks that need to be considered and require forward thinking. Indeed, the focus of AI is gradually shifting from celebrating purely ‘algorithmic success’ to the question of how to apply these technologies in the real world, taking into account their consequences and impacts on society.
Artificial intelligence is not just a tool for optimising processes or maximising profits, but is becoming part of our everyday lives. It is already influencing our decision-making today. This fact imposes new requirements that must take into account not only the technical possibilities, but also the moral aspects and long-term consequences of the deployment of artificial intelligence in the financial sector. It is therefore essential to develop strategies and approaches that not only maximise efficiency and competitiveness, but also protect the interests and values of society as a whole.
Key AI capabilities in the financial sector
Automatisation
The first point we should mention is automation. It may seem like a given nowadays, but in practice, thanks to automation, banks are able to increase productivity and reduce operating costs by up to 70%. Automation is nowadays a key factor in modernising not only the banking sector. The first reason it brings benefits is the ability of artificial intelligence to perform repetitive tasks much faster and more accurately than a human. This allows banks to focus on more complex and strategic tasks. For example, processes such as transaction review, risk management or even evaluating loan applications can be automated through AI, leading to increased efficiency and speed in performing these operations.
Effective analysis
The second key capability of AI is to analyse and process huge amounts of data in a short time. Banks have extensive data on transactions, customers, markets, etc. Manual analysis would be time consuming and prone to human error. However, artificial intelligence can analyse this historical data in real time, identifying patterns or trends. This allows banks to better understand their customers’ needs and respond more effectively to market changes.
Prediction
Artificial intelligence can predict financial situations very accurately. In practice, artificial intelligence can help people, for example, to choose insurance. By analysing large data sets, AI can identify the individual needs and preferences of a given user. This leads to better and more accurate recommendations of products or services that are most suitable for that person. In this way, AI delivers increased value in financial advice and helps users better manage their finances. Predictive analytics can also help investment firms and banks to make investment decisions and optimise their portfolios.
The role of AI in financial risk management
Risk management is a cornerstone of the financial sector. Institutions may be exposed to a variety of financial risks such as credit, market or operational risks. Analytical tools allow banks to monitor financial activities, transactions and market trends in real time. As the volume of financial transactions and digital payment channels increases, the risk of misuse and manipulation also increases. Artificial intelligence plays a key role in anti-fraud (AML). By analysing large amounts of data using machine learning and artificial intelligence, sophisticated decision models can be created, outliers can be detected and risks can be assessed. In this way, banks can identify potentially fraudulent financial activity and take action to protect their customers and their business.
AI as a personal financial advisor
Scientific advances in the development of artificial intelligence with a focus on natural language processing (NLP) have reached a level where machines are able to “understand” human language to some extent. This breakthrough has, in my opinion, a major impact on the approach to artificial intelligence. Although it is not obvious at first sight, it is the development of natural language processing that has laid the foundations for so-called personalisation – tailoring services or products to specific individuals. Anyone who follows global trends in various industries is aware that personalisation is a key factor that influences how companies operate.
The financial sector has embraced personalised banking, which uses revolutionary technology called chatbots. Deutsche Bank, for example, uses deep learning to analyse the investment decisions of its international private banking clients. These algorithms also match individual clients with appropriate investment products such as funds, bonds or shares. The combination of AI and deep learning has created financial assistants that have become an integral part of modern banking. These personalised assistants help customers achieve their financial goals, track income, spending and buying habits, and suggest next steps. They are also able to answer customer questions, provide account information or even perform simple banking transactions.
Financial chatbots fundamentally improve customer service and their use benefits both parties. Customers can contact them at any time to get the information they need quickly, while they help companies and banks minimize the workload of call centers and automate the entire business process. Global banks such as Bank of America (chatbot Erica) and Wells Fargo (chatbot Fargo) have already successfully integrated these technologies into their applications.
Controversy and the issue of security
Job losses
Whether artificial intelligence will one day take control of money and the world’s economies remains as a question. It is unlikely in the near future. However, if we look at this issue from a longer-term perspective, I think the question becomes more interesting. Artificial intelligence makes decisions based on algorithms and data, but this, on the other hand, can sometimes lead to unintended and unpredictable results. As AI becomes increasingly autonomous, there is a risk of losing control over its activities. Over-reliance on AI in critical situations can lead to a degradation of human decision-making capabilities, which may result in situations where humans are unable to intervene effectively and prevent harm.
However, a related controversy is that AI will replace human workers and lead to mass job losses. This fear is incomplete, however, as the introduction of AI often creates new jobs as well. New positions are already being created, such as AI specialist, financial analyst, data scientist/analyst, etc. We should focus on creating synergies between AI and human workers. This could lead to new and innovative job roles and better use of human potential in the financial sector. It is important to look for ways to integrate AI into the work environment in order to foster development and innovation. The role of AI should be about complementing human skills and improving work efficiency rather than replacing them.
Cybersecurity
Like any modern technology, systems using AI are vulnerable to cyberattacks. The financial sector experienced the second highest number of cyber attacks during the pandemic, after healthcare. These attacks are a growing problem, especially in middle- and low-income countries. These countries are pushing for greater financial integration as they seek increased use of digital financial services such as mobile payment systems. One example is the hacking attack on Uganda’s largest networks MTN and Airtel in October 2020, which caused widespread disruption of transactions for four days. The situation has changed standards for security in the digital environment globally. As digital banking grows, so does the risk of cyber-attacks, which banks must proactively address. As banks develop new services and work with customer data, it is essential that they continuously ensure the reliability, security and trustworthiness of their systems.
Biometric authentication, such as fingerprint, facial scanning or voice recognition, is one way in which security can be strengthened. Nowadays, a user who wants to log in to online banking verifies his identity in three ways: “something he knows: login details, something he has: a mobile phone, and something he is: biometrics.” However, this technology also comes with high risks because it works with sensitive user data. There has been increased pressure in the EU to create laws to regulate AI, which were passed this year. The AI Act is the first of its kind in the world. It is a set of rules based on so-called risk levels and accordingly sets out a series of obligations for users and providers, not just financial services.
Transparency of AI decision-making
Another important issue is transparency, which acts as a gateway that opens the door of trust between the client and the company. The client should be clear about how their data is being used and should be able to decide whether or not they agree to it. Therefore, it is crucial that banks explain what information they collect, what AI processes are involved in decision-making and what data is used to do so. Ensuring transparency in AI decision-making is not only an ethical obligation, but also an important element in maintaining customer trust in modern financial institutions. In making AI decision-making transparent, it is essential to ensure that client data is properly protected and not misused. Overexposure of sensitive information can lead to various forms of misuse, including the aforementioned cyber-attacks or identity fraud.
Personal data leak
Machine learning and artificial intelligence are at the heart of many data protection issues today. In the 21st century, the digital environment is becoming the main channel for sharing and collecting information for both businesses and individuals. Statistics from the early 2020s show that there were more than 3.5 billion smartphones in operation worldwide, collecting and sharing vast amounts of data – from GPS location to personal data and user preferences through social media and search histories. And that number continues to grow. As businesses have greater access to their customers’ personal data, there is a need to continually develop rules to protect privacy and minimise risk.
By analysing the financial flows of individual customers, banks obtain large amounts of sensitive information. Lack of security in the digital environment can have a wide reach. If personal data is inadequately protected, it can be misused for identity theft, fraudulent activities or illegal surveillance of individuals. The consequences of inadequate data protection can also affect the economy and the competitiveness of companies. If sensitive business or customer information is leaked, companies and entire industries will be affected. This can lead to loss of competitive advantage, loss of revenue and reputational damage.
The current challenges
Artificial intelligence is already changing the quality of products and services offered by the banking sector. Now more than ever, banks are realising the innovative and effective solutions AI provides and understand that asset size, while important, will no longer be enough on its own to build a successful business. Instead, the success of BFSI (Banking, Financial Services and Insurance) companies is now measured by their ability to use technology to create innovative and personalised products and services.
Yet many are still experimenting and not moving to full implementation. The key to success is not only investing in the implementation of AI, but also addressing the challenges associated with its deployment. The financial sector is strategically investing in areas such as the cloud, big data platforms and smart applications that leverage modern architectures such as microservices, which are key to competing in the market. This also eliminates the upfront capital investment required to develop and deploy AI in the real world. However, operational and organisational challenges still remain, in particular the lack of the necessary skills and integration of AI into the wider fabric of the organisation.
Implementing AI with benefits also brings risks, and from my perspective, it is essential to strike a balance between the benefits of automation and ensuring security. Not only is there room for countless new innovative solutions, but also shadows of uncertainty and vulnerability associated with the use of modern technologies are emerging. Finding the right balance is an extremely complex challenge that the financial sector must actively address today to ensure a secure and promising financial future.
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