What AI can do in the financial sector – and how you can benefit from it

However, before we delve deeper into specific application examples, let's first take a look at the basics. Artificial intelligence (AI) is fundamentally changing the foundation of many business processes in the financial sector. Whereas manual checks, rigid rules, and lengthy processes used to dominate, today we have access to adaptive systems that automatically recognize patterns, make predictions, prepare decisions, or even make them themselves. 

In the context of banks, this means that Processes such as lending, fraud prevention, customer service, compliance, and portfolio management can be made faster, more accurate, and more cost-efficient—in some cases with higher quality than purely human processing.

But not every automated evaluation is AI. To understand what AI can achieve in the financial sector, it is worth taking a look at the three key technological terms – and the distinction from traditional data analysis.

Artificial intelligence (AI) is the umbrella term for technologies that perform human-like tasks: interpreting information, learning from experience, and making situation-dependent decisions. In banks, this includes, for example, risk assessment systems, chatbots for customer service, and tools for automated document verification.& nbsp;

A key sub-area is machine learning (ML). This refers to processes in which algorithms independently recognize rules based on large amounts of data—without these having to be specified manually. In banking, machine learning is used for, among other things:

• Scoring models for credit applications
• Transaction classification in accounting
• Behavioral analysis for fraud detection
• Customer segmentation for personalized offers

Deep learning, a specialized area of machine learning, uses artificial neural networks with many layers to recognize particularly complex patterns—e.g., in texts, speech, or images. Banks use deep learning in areas such as:

• OCR applications for automated document processing
• Speech analysis during hotline calls
• Emotion recognition in customer service (e.g., to prevent escalation)

The choice of method depends on the goal: for classic predictions (e.g., "Will this loan be repaid?"), an ML approach is often sufficient. When dealing with unstructured data or a particularly large number of influencing factors, deep learning is usually used.

In traditional data analysis, rules and thresholds are usually defined manually: "If x is greater than y, then do z." The advantage is that this is transparent. The disadvantage is that it is not very flexible and often prone to errors, especially in complex decision-making processes.

AI systems, on the other hand, are data-driven rather than rule-based. They recognize patterns that are not immediately apparent and can evolve with each new data input. This makes them dynamic—but also requires explanation, especially when it comes to decisions with regulatory implications.

In short: Traditional analysis shows what is. AI predicts what is likely to happen.

The ability to make predictions is just one of many advantages that come with using AI. Another crucial point is that AI can be part of the solution to the enormous pressure for change that the financial sector has been facing for years.

On the one hand, customers are increasingly demanding digital, intuitive services—fast, personalized, and available around the clock. On the other hand, regulatory complexity is constantly increasing: money laundering prevention, ESG reporting, risk management, data protection—there is hardly any area left that does not require comprehensive documentation and control mechanisms.

At the same time, many institutions are struggling with complex IT landscapes, historically grown data silos, and manual processing efforts. These structural obstacles slow down innovation, delay decisions, and cause high operating costs. 
 
Added to this are new competitors — fintechs and big techs — that are faster to market with agile teams, cloud infrastructures, and AI-powered services, and redefine customer expectations.

AI can help the financial sector meet these challenges. Used correctly, it automates operational processes, identifies risks at an early stage, analyzes customer behavior, and fulfills regulatory requirements more efficiently. For example, it can be used to pre-check loan applications in seconds, analyze suspicious transactions in real time, or map ESG risks in risk management based on data.

Scalability is particularly attractive: Once an AI application is up and running, it can be extended to new data sets, target groups, or business areas with little additional effort, provided that the tasks and data structures are comparable. This results in a significant efficiency lever, especially for institutions with large data volumes.

However, transferability always depends on the specific use case. Artificial intelligence is increasingly evolving from a "nice-to-have" to an operational necessity. This is because many regulatory requirements can hardly be met manually anymore. In the fight against money laundering, for example, seamless monitoring of all transactions is required – in real time and across national borders. Only with AI-supported anomaly detection can such requirements still be implemented economically. Let's take a closer look at where AI technologies are already being used successfully today. It remains important that AI is always implemented in a way that ensures transparency, traceability, and compliance with regulatory requirements.

Shortly before the weekend, the managing director of a mechanical engineering company submits an online loan application for a new production facility. Instead of waiting days for a response, he receives preliminary approval within minutes—calculated on the basis of thousands of similar cases, creditworthiness data, and industry trends. No human being checked this manually—it was AI.

Such applications are no longer pilot projects. Banks are using AI specifically where speed, scalability, and accuracy are required. Whether in customer service, risk management, or fraud detection, intelligent systems supplement human decisions or make them themselves. But which tasks is AI already performing successfully—and where does it (still) reach its limits?

Credit decisions are part of every bank's core business – and are often fraught with uncertainty. Is the available data sufficient for a sound assessment? Is the applicant really solvent?

AI helps to improve these decisions based on data. It analyzes historical credit defaults, payment flows, external economic data, and even customer signals in real time—and uses this information to derive risk profiles that can specifically supplement and refine traditional credit checks.

For banks, this means less manual work, more standardization, and significantly lower default risks. At the same time, decisions can be made more quickly and processed digitally. This is a clear competitive advantage, especially in corporate banking.

Suspicious debits, manipulated transfers, or fake identities—financial fraud today is highly digital and often professionally organized. Conventional control systems often reach their limits in this area.

Artificial intelligence recognizes patterns that humans would overlook. It analyzes transactions in real time, compares them with historical cases of fraud, and raises the alarm as soon as unusual behavior occurs—such as sudden access from another country or conspicuous payment routines among new customers.

Potential damage is not only detected after days, but is stopped as soon as the first suspicious click is made. This saves money, protects customers, and significantly reduces the workload for the back office. 

Whether market, credit, or liquidity risks—banks have to analyze enormous data streams every day. AI supports the early detection of risks by identifying anomalies, simulating stress scenarios, or automatically updating changing relationships between variables—such as creditworthiness, market trends, and cash flows.

Modern chatbots are more than just simple FAQ responders. With NLP (natural language processing) and AI, they become true digital assistants—able to understand and prioritize requests and even initiate complex processes, such as bank transfers or address changes.

For example, a Swiss bank has automated most of its standard inquiries—from card orders to PIN assignment. The chatbot continuously learns as it goes—and hands over to human colleagues when it is unsure.

Digital asset management is booming, especially among younger target groups. AI-based robo-advisors assess risk affinities, analyze markets, and suggest suitable investment products. Some models even automatically adjust portfolios, e.g., in the event of price slumps. 

Areas of application:

• Entry-level solutions for new customers without consultant contact
• Tax-optimized reallocations
• ESG-compliant portfolio structuring

Traditional banks are now also using robo-advisory as a hybrid solution—for example, for initial consultations, which are later supplemented by personal support.

AI does not replace humans in every case; much more often, it supplements their judgment with data-based perspectives. Take private banking, for example, where advisors use AI-supported tools to simulate investment strategies or prepare for client meetings.

Nevertheless, it is clear that in standardized processes—such as account reconciliation, AML checks, or reporting—AI can already greatly reduce or completely automate the human role. However, artificial intelligence also has clear limitations:

• Emotional intelligence and relationship building: In private banking or when dealing with sensitive customer issues, people remain (as yet) irreplaceable.
• Creativity & strategic thinking: AI can calculate scenarios – but it cannot think visionarily or holistically.
• Data dependency: Poor or distorted data leads to incorrect results ("garbage in, garbage out"). 

Despite the multitude of possible applications, the use of artificial intelligence is not a sure-fire success. It involves investment, technical hurdles, and cultural change. The key question is therefore: Is it worth it?

Answering this question requires a nuanced approach, because not every AI application delivers an immediately measurable ROI—some contribute more to strategic goals such as resilience, regulatory compliance, or customer satisfaction. Others, on the other hand, enable immediate savings through process automation. Here is an overview of the most important aspects:

In daily practice, it often becomes apparent that it is not the technology itself that is the biggest obstacle, but rather the environment in which it is to be introduced. This section shows you which technical stumbling blocks typically arise—and how they can be overcome. Equally important are the cultural prerequisites without which AI projects will fail in the long term.

Data quality and availability

AI needs data—lots of it, clean and well-structured. In many banks, however, this data is spread across different systems, unharmonized, or subject to legal restrictions. Without a solid data foundation, any AI remains a blunt instrument.

IT infrastructure

Modern AI solutions place special demands on computing power, scalability, and flexible interfaces (APIs). Legacy systems and rigid architectures often make it difficult to integrate modern AI tools, especially when data is stored in silos.

Regulatory uncertainty

The requirements for transparency, traceability, and data protection are high. Many (financial) institutions are reluctant to use "black box" models productively—especially in sensitive areas such as lending or risk management.

Cultural change & expertise

AI projects require interdisciplinary teams: data scientists, specialist departments, IT, and compliance must work together. In practice, however, there is a lack of understanding between these departments—or simply a lack of expertise. Studies show that 70% have difficulty recruiting suitable AI talent.

Now that we have addressed the challenges involved in introducing AI, we can move on to the most important criteria for successful AI projects. These can often be broken down into three simple core factors:

• Scalability potential: Can the use case be applied to many customers, processes, or transactions?
• Data basis: Is there sufficient high-quality, relevant data available?
• Automation potential: Is the process sufficiently standardized and does it exhibit enough recurring patterns to be reliably mapped using AI?

But beware: too much pressure to achieve a high ROI in the first project can stifle innovation. Many successful banks start with a pilot project, build internal trust, and then roll out successful approaches on a larger scale.

Small pilot projects are also a good way to test the technical conditions. This is because the fundamental question of how AI can actually work in one's own company usually remains too abstract in many discussions. Yet it is of central importance for decision-makers, architects, and project teams.

The technical implementation determines scalability, security, and long-term cost-effectiveness. In practice, "using AI" does not mean installing a single tool. It requires a technical foundation consisting of data infrastructure, a development environment, and orchestrated integration into existing systems.

A wide range of powerful AI tools are available in banking today – from classic machine learning libraries to complete platform solutions. The key factor is that the tools are suited to the respective organization, data flows, and IT governance.

Usage trends:

• Large banks often build their own AI platforms in the private cloud.
• Smaller institutions are more likely to use AI-as-a-service models via public clouds.
• Tools with high auditability and model transparency (important for regulatory purposes) are particularly in demand.

In addition to the technical fundamentals, the question arises as to how an initial pilot project can be successfully implemented. In practice, these usually follow a structured approach—often based on agile or CRISP-DM models.

Typical project workflow:

Many banks now integrate this process into an MLOps pipeline, which enables versioning, model testing, and automated retraining—similar to DevOps in software development.

Data infrastructure is the backbone of every AI project. The rule is: Without data, there is no intelligence. Banks in particular, which work with sensitive data, face a balancing act: They must enable scalability without compromising security and compliance.

Important technical basics:

• Cloud architecture
  → Hybrid models are most common: Local data storage + cloud AI for analysis
  → Platforms: Google Cloud (BigQuery, AI Hub), Azure (Synapse, ML Studio), AWS (Redshift, SageMaker)
  
  
• APIs & Integration 
  → Open interfaces (REST, gRPC) enable connection to core banking systems 
  → Important: Separation of production and training data
  
  
• Data security & governance 
  → Access controls, GDPR compliance, audit trails 
  → Increased focus on: "Explainability by Design" and ethical AI governance

In addition to technical requirements, legal requirements also play a decisive role. The EU AI Act (2024/2025) is a milestone for the European Union. The Artificial Intelligence Regulation is the world's first comprehensive set of rules for the use of AI. It divides AI applications into four risk classes:

• Forbidden AI (e. g. Social Scoring)
• High-risk AI (e.g., creditworthiness checks, fraud prevention) 
• Limited risk (e.g., chatbots with disclosure requirements)
• Minimal risk (e.g., spam filters)

The high-risk AI category is particularly relevant for banks—in future, it will be subject to:

• Documentation and verification requirements
• Transparency and explainability requirements
• Risk management systems
• Human supervision obligations

The AI Act will come into force gradually from 2025, with transition periods ranging from 6 to 24 months.

The German Federal Financial Supervisory Authority (BaFin) has commented on AI on several occasions—for example, in the technical article "AI in the Financial Industry" (2024) and in the context of supervisory expectations for governance and IT (e.g., BAIT, MaRisk). BAIT, MaRisk).

Key requirements of BaFin:

• Transparency: AI decisions must be explainable and documentable—especially when it comes to lending or risk assessment.
• Governance: AI must not be confined to IT. Clear responsibilities are required, particularly in the three lines of defense model.
• Responsibility: Even if decisions are made automatically, the bank remains liable.
• Testing & validation: Algorithms must be checked regularly for performance, bias, robustness, and compliance with regulations.

Beyond the EU AI Act, numerous other legal requirements affect the use of artificial intelligence in banks—often indirectly, but with a significant impact. This is because AI solutions have a profound impact on existing business processes and thus affect areas such as data protection, IT security, risk management, and consumer protection.

This presents financial institutions with the challenge of combining technological innovation with regulatory certainty. Other regulations include, among others:

• GDPR (General Data Protection Regulation) 
  → AI processes may only be carried out on a data protection-compliant basis 
  → Art. 22 GDPR: No fully automated profiling without explicit consent
• EBA & ECB guidelines 
  → European Banking Authority specifies requirements for ICT & security risks (DORA) 
  → AI-specific requirements are under discussion (e. g. explainability, fairness)
• The AGG (General Equal Treatment Act, Germany) prohibits discrimination in lending, scoring, and automated decisions.

Another key regulatory principle is the explainability of the AI model. This applies in particular to so-called "black box" models such as deep neural networks, whose internal decision-making logic is ultimately only comprehensible to a limited extent, even for experts.

Regulatory best practices:

• Use of Explainable AI  (z. B. LIME, SHAP)
• Logging of model decisions (including input, score, recommendation)
• Model cards: standardized documentation on purpose, database, training processes, and limitations
• Data Lineage and Model Versioning: Who trained which models with which data and when?

In addition to formal requirements, there is growing pressure to define ethical standards for the use of AI – either voluntarily or with regulatory support.

Numerous banks are currently developing internal responsible AI guidelines, some of which are based on industry standards (e.g., IEEE, ISO/IEC 42001). Rating agencies and investors are also paying increasing attention to AI ethics in their ESG assessments.

Responsible AI encompasses the following principles:

The regulation of artificial intelligence in the financial sector is becoming more specific, more binding—and more demanding. For those responsible, one thing is clear: Without clear governance, explainable models, and legally compliant data handling, AI will not be productively scalable. Those who respond early on will not only be able to meet regulatory requirements, but also position themselves as a mark of quality for customers and investors.

Artificial intelligence has arrived in the world of finance – but its potential is far from being fully exploited. The coming years promise technological leaps, further regulatory clarity and new fields of application.

Generative AI, based on large language models (LLMs), enables new forms of automation, from report generation to compliance support. The challenge is that quality assurance, data protection, and explainability must grow alongside it.

Explainable AI (XAI) is increasingly becoming the standard. This is because only traceable decisions can be regulated – especially in sensitive areas such as lending or money laundering prevention. Transparency is not only a technical issue, but also a strategic one.

Autonomous processes are becoming increasingly important: for example, through AI agents that not only prepare decisions, but also make and evaluate them independently. The goal is a high degree of efficiency—with maximum control.

The direction is clear: AI is evolving from a supporting tool to an integral part of value creation. Those who invest early can set standards—and secure long-term competitive advantages.