AI fraud detection in banking

By analyzing large datasets, AI models can learn to recognize the difference between suspicious activities and legitimate transactions, and they can help identify possible fraud risks to prevent financial crime—even catching trends that a human agent might miss.

Financial institutions are increasingly integrating AI solutions into new and existing workflows to improve decision-making, fraud prevention and risk management. AI-powered machine learning models trained on historical data may use pattern recognition to automatically catch and block possible fraudulent transactions from being executed. They also may require human agents to complete extra authentication steps to verify a suspicious transaction. AI technology can also use predictive analytics to estimate what types of future transactions a person might make, and it can recognize if a new type of transaction or transactional behavior is unusual. 

In these ways, AI fintech can help protect individuals from financial losses resulting from various types of fraud, including phishing scams, identity theft, payment fraud, credit card fraud and other varieties of banking fraud.

AI fraud detection systems are not perfect, and some false positives may negatively impact the overall customer experience. But preventing fraudsters from committing financial crimes ranging from unauthorized charges to money laundering is paramount to ensuring the security of client accounts and maintaining regulatory compliance for financial institutions.

As advancements in AI technology continue, both AI-driven fraud prevention providers and leading financial institutions are banking on AI to become an even more valuable tool in preventing fraud attempts and mitigating fraud risks.

AI technology allows computers to behave, learn, adapt, problem solve, and act with autonomy in ways similar to human cognition. While AI systems are not necessarily as intelligent as their human counterparts, when operating within strict, rule-based systems, an AI trained and focused on specific tasks can match or outperform human agents at much faster speeds and much greater scales. 

AI systems used in banking fraud prevention are highly tuned for specific tasks. AI models are trained using large amounts of carefully curated data through a process called supervised learning. This method teaches the model to recognize specific patterns for specific tasks.

In contrast, unsupervised learning allows AI systems to draw conclusions from previous data without directed training materials.

In supervised learning scenarios, AI systems are trained on specific fraud tactics to guide pattern recognition. An example of a supervised learning data set might look like thousands of normal financial records mixed with identified examples of fraudulent behavior, such as an unusually large transaction or money transfer to a known fraudulent address.

AI is trained this way to become very skilled at recognizing both average, likely legitimate transactions and transactions that are common to known fraudulent behavior patterns. 

Unsupervised anomaly detection techniques are used to fill in the gaps where supervised training models might be lacking. These techniques empower AI models to recognize previously unpredicted—but still unusual—behavior patterns. The use of AI systems with unsupervised learning functionality can analyze new data to detect possible fraud tactics before human agents are even aware of such threats. 

Through both supervised and unsupervised learning, banks can use AI automation to screen for previously confirmed fraud patterns and raise the alarm if unknown patterns indicate the possibility of new fraudulent activities.

One of the most common applications for AI technology is the social media chatbot, an automated program that can conduct conversations with customers. Chatbots like these are often used for customer service, answering basic questions and providing information in real-time without having to wait for a human agent to be available.

Beyond customer service, the banking industry uses many other types of programs and software incorporating AI to identify and prevent potential fraud. Banks use AI systems for real-time detection, tasking AI-enabled programs to analyze massive amounts of transactions to identify and flag any suspicious account activity in a large variety of ways, including the following: 

• Workforce support: Human workers doing traditional fraud prevention can access LLM-based AI assistants to communicate with natural language and query large datasets or reference lengthy and complicated policy documents.

• Regulatory compliance: Banks are under major pressure to remain in regulatory compliance. AI programs can help banks implement Know Your Customer (KYC) policies with computer vision by analyzing identity verification documents for any inconsistencies or signs of fraud. They can also help banks implement Anti-Money Laundering (AML) processes by flagging known accounts or behaviors associated with illegal money laundering, such as the movement of identical currency amounts between disparate accounts. 

• Anomaly detection: AI systems are particularly useful for any application requiring pattern recognition. Specific types of AI, known as graph neural networks (GNN), are designed to process data that can be represented as a graph, such as the data very common to the banking industry. GNNs are capable of processing billions of records to identify patterns across wide swaths of data to track and catch even the most complex frauds. 

• Risk scoring: AI and machine learning models are built on weighted data to assign probabilities to potential actions and assess their most accurate decision or action. As such, they can make assessments based on multiple factors, such as transaction amounts, frequency, location and past behavior, making them very well suited for determining risk. AIs can determine the risk of a given transaction, as well as the risk of granting a loan or a line of credit to potentially fraudulent applicants. 

• Network analysis: Machine-learning techniques like graph analysis can be used to uncover networks of potential fraudsters by analyzing relationships between entities and identifying suspicious connections or clusters.

AI systems are ushering in a new era of fraud detection and security in the banking industry, offering dramatic improvements over traditional methods of fraud detection. However, AI models are built upon the learnings and practices of the traditional systems, and traditional methods still have their place. 

• Easy implementation: Traditional fraud detection techniques are built on predefined rules-based approaches that are well established and simple to execute. For example, flagging any new transactions that exceed a certain average range based on a particular account's spending habits. 

• Human intelligence: Traditional human fraud detection analysts bring a certain level of domain experience, intuition and expertise. In some situations, only a traditionally trained human might be able to verify the legitimacy of a given transaction or recognize a potential fraud attempt. 

• Limited scope: Traditional, rules-based fraud detection systems rely on fixed relationships (e.g., if X, then Y). While this approach can be effective, it also fails to incorporate the possible vast and complex interactions between many different data points. 

• Limited scale: As transaction volumes swell, traditional systems built and managed by human-powered fraud detection specialists struggle to quickly process the growing amount of data generated every minute of every day. Hiring more workers is not only expensive, it may not even be sufficient. 

• High error rate: The rules-based systems used in traditional fraud detection systems are typically very rigid, triggering whenever a potential fraud indicator is observed. This rigidity can lead to a high volume of false positives. For example, if a given account has never made a withdrawal of more than USD 100 and then attempts to withdraw double that, the system will likely block the transaction. But while this behavior is unusual, it doesn’t necessarily indicate fraud. In this situation, a customer may simply need to make an unusually large withdrawal. These false positives can trigger unnecessary investigations and delays, which lead to reduced customer satisfaction.

• Improved pattern recognition: AI systems are excellent at ingesting massive amounts of data to recognize complex and obscure patterns. By seeing the bigger picture, AI systems can identify anomalous activity with better accuracy.

• Massive scalability: Through automation, AI systems can monitor huge amounts of transactions far greater than humans could ever manage. AI-powered fraud detection is capable of real-time analysis and can provide a rapid response faster than traditional methods. 

• Adaptability: Once trained, AI algorithms don’t stop learning. As AI systems continue to work, they are able to learn and adapt, improving their capabilities to catch new types of fraud and enhancing their efficacy.

• Data dependent: AI models require extremely large amounts of data to train, learn and grow. This data must be either sourced or created (synthetic data), but also curated. A given AI model’s accuracy is dependent on the quality of the training data. 

• Complex implementation: AI systems can be challenging to integrate into existing systems. While AI systems save money in the long run, they can also require a large initial investment. 

Since implementation, various financial institutions and banks have found significant evidence to support the increasing adoption of AI fraud detection. Using advanced, long short-term memory (LSTM) AI models, American Express was able to improve fraud detection by 6%. And PayPal was able to improve their real-time fraud detection by 10% through AI systems running around the clock, worldwide. 

In practice, use-cases for AI fraud detection in banking are plentiful and rising, including the following.

As a revolutionary technology, AI fraud detection is already having a dramatic impact on the banking industry, with potentially even greater potential. However, despite the many advantages offered by AI, this new technology is not without its challenges.

AI systems are getting better every day, but they are not infallible. AI models can frequently generate inaccurate results, known as hallucinations. In banking, inaccurate results may be mitigated by creating hyper-specialized models designed for very specific tasks, but these types of models limit the potential value of AI. While hallucinations are not so common as to make AI unusable, increasing accuracy will be critical for the advancement of AI in banking fraud protection.

Bias in data analysis has been an issue since the earliest days of science, long pre-dating computer technology. Unfortunately, the issue persists. In the sensitive field of financial services, much work has been done to eliminate bias and discrimination from lending practices and account protections. Eliminating bias in AI models built by potentially biased technologists is a critical challenge that must be overcome to avoid discrimination based on factors such as gender, race, disability and religion.

Issues of governance of data privacy are vital in the banking industry. AI models require access to massive amounts of data, which must be obtained and processed ethically. Furthermore, the implementation of AI must also be highly considered to avoid violating any existing data privacy laws. Indeed, as this new technology evolves, lawmakers and regulators may need to assess and update our current regulatory environment to ensure secure customer privacy.