Artificial intelligence (AI) has become a key catalyst for change in the financial sector. According to Funds Society, the global AI market in finance will reach $39 billion by 2032, representing a 350% increase compared to 2023. Banks and financial institutions worldwide are accelerating their adoption of AI to prevent fraud, automate processes, ensure regulatory compliance, and improve operational efficiency. In fact, European banks are among the global leaders inAI implementation, with institutions such as UBS, HSBC, BNP Paribas, and BBVA occupying top positions in international AI innovation rankings. Below, were view the emerging trends in the sector—from fraud prevention to operational efficiency—and how these fit within an increasingly demanding regulatory context, with initiatives like the European AI Regulation and Spain’s Algorithmic Transparency Law.
Combating financial fraud has been one of the first areas where AI found practical application in banking. Machine learning systems analyze massive volumes of transactions in real time to identify anomalous patterns that could indicate fraudulent activity. This allows for the detection of fraud attempts more quickly and accurately than traditional methods. According to El Economista, the adoption of AI has enabled companies to reduce fraud attempts in electronic transactions by up to 86%. In some cases, up to 35% of companies have been able to completely eliminate fraud thanks to these suspicious behavior analysis techniques. A clear example is Visa, whose AI system prevented $40 billion in fraudulent transactions worldwide in 2023.
AI not only blocks known threats but also learns from new patterns to anticipate emerging fraud. Advanced payment platforms, for example, can now recognize the buyer in90% of cases through data analysis, even if it’s their first time transacting with a business. When unusual behavior is detected, the system proactively denies the operation to protect both the client and the institution. Thanks to these capabilities, banks and payment processors are minimizing losses due to fraud and also reducing “false positives”—alerts that later turn out not to be actual fraud. This is crucial, since traditional solutions generated numerous false alerts, resulting in wasted effort and resources. AI helps filter these alerts, allowing risk analysts to focus on genuinely critical cases.
Another transformative trend is the intelligent automation of processes. Many institutions started by applying AI to administrative and back-office tasks, where the effects are most visible. The technology enables the automation of repetitive tasks, from document processing to the reconciliation of accounting data, with much greater speed and accuracy than manual work. For example, computer vision and machine learning algorithms are used to read and verify financial documents, streamlining processes such as account opening or transaction validation. In fact, it is expected that document processing automation will be one of the main AI use cases in banking by 2030.
Currently, financial institutions already use AI to automate numerous administrative processes, product offer personalization, and KYC/AML checks (know your customer / anti-money laundering). These automations reduce the operational workload on employees, minimize human errors, and accelerate customer service. A particularly benefited area is new client onboarding: implementing AI solutions in this process can reduce operational onboarding costs for banks by around 40%. AI is also drastically reducing manual reconciliation and review tasks by automatically cross-checking data and verifying consistency. This frees human teams to focus on higher-value activities, such as strategic analysis or personalized client support.
Regulatory compliance and risk management are areas where AI is providing great advantages to banks and insurers. Traditionally, rule-based software generated a huge volume of suspicious activity alerts, of which between 90% and 95% ended up as “false positives” requiring no further action. This overloaded compliance departments, raising costs and slowing down investigations.
AI allows for more precise detection of genuinely illicit operations by finding hidden patterns in transactions and relationships between clients. For example, advanced algorithms analyze each user’s behavior and compare it to typical risk profiles, quickly adapting to new money-laundering methods criminals might devise. The result is fewer irrelevant alerts and better protection against illegal actors, while also reducing the likelihood of regulatory fines. Studies show that with AI, a bank can cut between 45% and 65% of alerts for potential breaches or fraud, while still detecting at least 99% of genuinely suspicious cases.
Ultimately, the use of AI in finance seeks to improve operational efficiency and the competitiveness of institutions. In an environment of tight margins, the productivity improvements and cost reductions provided by AI are crucial for maintaining profitability. Banking already views artificial intelligence as a key ally for reducing costs and boosting efficiency in almost every business area. Many entities are integrating AI into their digital transformation roadmaps, allocating billions of euros to tech projects involving dataanalytics and intelligent automation. For example, CaixaBank has launched atech plan to invest €5 billion between 2025 and 2027, with AI as a lever topersonalize advisory services, improve product offerings, and develop new financialservices.
Efficiencygains are already visible in concrete indicators. Here are some measurableachievements thanks to AI in the sector:
Theseoperational improvements result in better financial efficiency (for example, incost-income ratios) and also in an improved experience for customers andemployees. A more automated and precise banking system means fewer errors, lesswaiting, and more personalized services. Not surprisingly, by 2030, exponentialgrowth is projected in generative AI investment in banking, with a focus on usecases directly related to efficiency such as customer service chatbots,automated risk assessment, real-time fraud detection, and massive documentprocessing. In short, AI is helping financial institutions do more with less,strengthening their competitiveness in an increasingly digital market.
The rapid incorporation of AI in the financial sector comes with increasing attention from regulatory bodies to ensure the ethical, safe, and transparent use of these technologies. The European Union is finalizing the approval of a pioneering AI Regulation, covering both specific applications categorized as “high risk” (such as credit scoring algorithms) and general-purpose AI systems like ChatGPT. This framework will impose strict requirements for data quality, traceability, risk management, and human oversight for AI systems used in banking, especially those making sensitive client decisions. The goal is to promote trustworthy and bias-free AI aligned with European values.
In Spain, progress is also being made on specific initiatives to strengthen AI governance. On one hand, the Artificial Intelligence Supervision Agency has been created, along with a dedicated AI regulatory sandbox, both of which started their first steps in 2024. These tools will allow innovations to be tested in controlled environments and ensure compliance before widespread deployment. On the other hand, the potential Algorithmic Transparency Law is gaining traction, which would require companies to explain how their algorithms work when these impact decisions about citizens. This legislation seeks to guarantee the right to explanation and auditability of automated processes, preventing possible discrimination or opaque results from the use of AI in areas such as credit granting or risk management.
Financial supervisors are also including AI considerations in their guidelines. International organizations like the Bank for International Settlements (BIS)and the Basel Committee on Banking Supervision have highlighted the need for controls on algorithmic models and for sharing best practices in AI governance. On a day-to-day basis, authorities emphasize that entities maintain mechanisms for human oversight and bias correction in all automated processes affecting clients. For example, lending or fraud detection decisions should include reviews and ethical criteria, avoiding unintended harm to vulnerable groups. Regulators’ message is clear: AI-driven innovation is welcome, as long as it is accompanied by transparency, accountability, and compliance with current regulations.
ALS Innovation has extensive experience in the development and integration of artificial intelligence solutions for the financial sector. Our team combines expertise in data analysis, regulation, and banking operations to support institutions in adopting technologies that improve efficiency, security, and compliance. Thanks to this practical approach, we help our clients harness the potential of AI with assurance and in a way that is adapted to the current challenges of the sector.
