Artificial intelligence in banking, or how AI will revolutionise our fight against bank fraud 

The financial sector undergoes continuous transformation and regularly pursues better and more advanced solutions. However, in step with this technological development and advancing digitisation, it also faces an increased risk of financial fraud. As cybernetic methods grow more and more sophisticated, traditional crime detection methods become largely insufficient. In this context, banks are turning to new technologies and artificial intelligence is now a real game-changer in the banking industry. The question is: how exactly can AI enhance the processes of fraud detection and prevention to ensure more secure banking services?

Artificial intelligence in banking, or how AI will revolutionise our fight against bank fraud 

What is artificial intelligence and how does it work?

Artificial intelligence (AI) is a field of knowledge that encompasses a set of techniques, algorithms and methods that allow computers to perform tasks that simulate the functions of the human brain. These including abilities such as learning, comprehension, reasoning and decision-making.

Machine learning

In machine learning, algorithms are provided with huge data sets, from which they can learn. There are three types of machine learning:

  • supervised learning – algorithms are trained on input data matched with desired output values. They correlate both pieces of information and learn to correctly identify certain patterns;
  • unsupervised learning – algorithms learn from input data without any sort of output data. In this case, they analyse the data and group them based on similarities;
  • reinforcement learning – algorithms learn from interactions with the environment. They are provided with specific rules and principles and asked to use them to solve a range of specific tasks. By trial and error, they learn to distinguish between correct and incorrect solutions.

Natural language processing

Natural language processing (NLP) is a technology that, in a sense, gets to the core of the sense and meaning of language, allowing it to be interpreted and translated it into symbols that can be understood by algorithms.

Neural networks

Neural networks are models inspired by the structure of the human brain. Each neural layer is a little piece of software that processes input data and passes them on to other layers, which then generate output results. In this way, it learns to recognise patterns and perform various tasks.

Banking sector. Advantages of artificial intelligence

The use of AI solutions to detect financial crime offers a range of opportunities for enhancing security, effectiveness and customer service:

Immediate response to threats

Traditional data analysis methods may be time-intensive and ineffective in dealing with dynamically evolving threats. Thanks to AI’s ability to monitor data in real time, companies can quickly identify suspicious activity and take prompt action to quickly respond to new types of financial crime and changing criminal behaviour patterns.

Thorough analysis of large data sets

AI systems can thoroughly analyse large data sets to detect even the most subtle patterns and anomalies in financial operations. The algorithms are much more infallible than the human brain, which enables more effective fraud detection.

Flexible machine learning

Generative AI may be trained to identify new types of financial fraud based on provided data. Thanks to continuous learning and fine-tuning to adapt to new threats, AI can build predictive models to reduce the risk of future fraud, boosting the security of banks and other financial services companies.

Time and money savings

Artificial intelligence brings important time and money savings thanks to the automation of fraud detection processes. Banking industry can now focus on strategic development tasks instead wasting time on manual data searching.

Greater customer trust

Customers expect maximum safety and reliability in banking services. Thanks to the use of AI for fraud detection, institutions can build their reputation as trustworthy partners, attracting new customers and maintaining lasting customer loyalty.

Financial fraud in banking industry

Financial fraud has always occurred in the history of banking, but technological progress and changing trends mean that it is now becoming more sophisticated and difficult to detect. Banks need to face all sorts of fraud, ranging from traditional types, like identity theft or document forgery, to modern cybercrime technologies. Let us look at some of the most frequent attack types to see how AI technologies can help protect customers.

Identity theft

Identity theft involves the illegal acquisition of personal data. In the context of banking, such data commonly include first name, last name, PESEL number, residence address, ID number, or credit card number. With this customer data, a criminal can, for instance, open a bank account, take out a loan, or perform financial transaction in the name of the victim.

AI-based solution: artificial intelligence may monitor customer behaviour in real time to detect any unusual activity, such as unauthorised logins from new devices or unknown locations, which may indicate an attempt at identity theft. Banking algorithms may also use facial recognition technologies to compare customer faces with photographs in their official documents.


Phishing is a form of online fraud, where fraudsters impersonate trusted institutions (including banks), companies or individuals to deceive people into revealing their personal data. Commonly, they will e-mail or text a website URL of a website that looks just like the real one. The unwitting victim will then reveal their data, which may be used to take over their account, take out a loan or mortgage, set up a company or do illegal shopping.

AI-based solution: Artificial intelligence may analyse the contents of e-mails and other online messages to look for suspicious signs and block suspicious messages before they land in the recipient’s inbox.

Credit card fraud

Credit card fraud includes any unauthorised use of credit cards or credit card data for shopping or financial transactions without the consent of the card owner. Credit cards can be stolen even when not physically lost. Criminals will rely on phishing or identity theft to get access to their victim’s credit card data and then perform online transactions in their name.

AI-based solution: advanced technologies may analyse credit card use patterns to quickly identify unauthorised transactions. Thanks to machine-learning algorithms able to analyse large data sets of historical transactions, AI-powered systems may easily detect any future anomalies.

Account takeover

An account takeover attack happens when a fraudster attempts to get access to a person’s bank account, often following an earlier login data theft or takeover.

AI-based solution: AI systems may detect untypical user behaviour, such as a change in login patterns or suspicious account activity. Advanced neural network models will identify any subtle interrelationships and transaction patterns that deviate from the norm.

Document forgery

Document forgery involves creating false copies or modifications of IDs, agreements, transfer confirmations or invoices.

AI-based solution: Artificial intelligence can analyse official documents to check for various manipulations, discrepancies or anomalies. It can also use extensive databases of real documents, such as IDs, to detect fraud.

How to use AI in financial services companies? Key takeaways

Artificial intelligence is gradually changing the banking landscape by helping in effective financial fraud prevention. Thanks to advanced machine-learning algorithms, natural learning processing models and neural networks, AI enables an immediate response to threats, a thorough analysis of large data sets and flexible adaptation to new forms of crime.

AI brings time and money savings by automating processes, increasing customer trust and maximising the security and reliability of banking services. AI integration is becoming a key element in many banks’ anti-fraud strategy, opening up new avenues for improvements in effectiveness and customer service.