The evolution of fraud techniques in financial crime 

The banking and insurance sectors are facing increasing challenges in validating transactions. Rapid technological advances, the growing number of different payment methods, the rise in daily transaction volumes, and the emergence of new types of virtual assets with economic value (such as cryptocurrencies) are placing ever‑greater pressure on compliance teams and the technological tools they use. At the same time, the increasing sophistication of financial schemes makes financial crime harder to detect, leading to challenges such as high levels of false positives (alerts created marking a transaction as suspicious which, after investigation, is validated as legitimate and real, without any hint of illegality/error) or rising operational costs. 

Organizations in sectors such as banking and insurance require more frequent and robust updates to their technological infrastructure to detect these techniques sooner and more efficiently. In this context, technological applications help ease the workload of professionals, offering automated workflows, transaction monitoring, AI solutions, and more. 

AML and KYC: the first line of defence against fraud 

Anti-Money Laundering (AML) refers to the processes used to foresee and prevent fraudulent transactions involving funds of criminal origin, which are often disguised to appear legitimate. Many companies in the banking and insurance sectors are required to follow strict regulations (European, for example) to prevent this type of activity. 

Know Your Customer (KYC) is a crucial initial step to validate customer legitimacy and combat financial crime. KYC enables organizations to collect and verify a customer’s identity information when opening an account or establishing a business relationship—for example, validating a passport or other identification document. 

Risk verification and continuous monitoring (WLM + AMM) 

After the initial KYC validation, additional stages help prevent fraud and money laundering:  

• Watch List Management (WLM) 

Validation that the individual is not on a predefined sanctions list, based on the information collected in KYC. This process, called WLM (Watch List Management), allows customer details to be compared with their presence or absence on risk lists such as politically exposed persons (PEP), international sanctions (OFAC – Office of Foreign Assets Control, for example), and internal risk lists. 

• Alert Management & Monitoring (AMM) 

Another phase of the validation process involves profiling customer behavior—we call this process AMM (Alert Management & Monitoring). This system validates abnormal transactions and generates alerts for potential financial crimes, identifying patterns of suspicious transactions that indicate money laundering or terrorist financing. 

Technological applications and their real impact in fighting financial crime 

Given the large volume of data, automating these processes and integrating near‑real‑time information helps teams filter alerts and reduce false positives, enabling teams to focus on high‑risk cases. 

By detecting financial crime more effectively—through applications that identify fraud attempts and money‑laundering patterns—compliance teams can avoid regulatory penalties, protect the organization’s reputation, and prevent the institution from being used as a channel for criminal activity, including terrorist financing. 

Tools capable of processing and structuring large data volumes and identifying inconsistencies have ultimately become essential allies to many professionals in the field. 

NetReveal: a critical application for fraud detection and prevention 

One notable example is NetReveal, a platform specialized in detecting and preventing financial crime, fraud, and risk management for financial institutions and regulated companies. NetReveal uses advanced AI and machine learning to identify suspicious activity, minimise false positives, and accelerate the analysis used in client screening and transaction monitoring. 

NetReveal enables organizations to monitor transactions and financial activities, investigate suspicious cases, recognize risk patterns, reduce false positives, and classify suspicious financial behaviours. Designed to handle large data volumes, the platform aggregates information into unified entities. For example, if a user’s phone number or address matches the details of someone already under investigation for a financial crime, that individual receives closer attention. 

In the insurance sector, the platform helps identify potential fraudulent claims—for instance, a person who has been involved in more than one home-flooding accident within the same year. By cross‑checking customer details, insurers can determine whether the case represents a warning sign for the insurance team. 

Finally, NetReveal is also used to assist in money laundering control, allowing the detection of suspicious transactions, once again breaking down the details of the individual in question and validating abnormal transactions. This is one of the practical applications of NetReveal in preventing illicit financing of terrorist groups or politically exposed individuals. 

The importance of technological applications in the future of financial crime prevention 

Financial crime is becoming increasingly complex, with more robust structures and harder‑to‑detect mechanisms. IT tools that integrate and analyse information act proactively and preventively, helping organizations combat these threats and detect them more easily. The combination of these tools with the internal knowledge of compliance teams is crucial to improve fraud resolution and support faster reporting to authorities. 

NetReveal is one of many solutions that help banks and insurers detect fraud, including money laundering. Its model for verifying data on individuals who may be committing fraud streamlines the work of many professionals in the field who would have to do this process manually, while maintaining legibility and anonymity in accordance with the GDPR (General Data Protection Regulation), and reducing the margin for error. 

More than simply automating or accelerating procedures with high reliability and security, AI‑enabled technological applications turn mindset into action—facilitating preventive approaches instead of reactive responses in the fight against financial crime. 

Isa Caravela, Application Support Analyst @ Neotalent Conclusion