Compliance Checker

Companies must adhere to many rules: laws, contracts, and their own internal regulations. A compliance checker is a program that automatically checks whether these rules are being followed. For example, it reads an advertising text, a loan application, or a piece of program code and compares the content against a list of requirements. If it finds a deviation, it issues a warning and points out the location. The name comes from English: “compliance” means adherence to rules, “checker” means examiner. In the end, however, a human usually still makes the final decision.

Why banks and corporations use such checkers

Rule violations are costly. In recent years, banks have paid fines amounting to billions, for example because they failed to report money laundering. On top of that comes the damage to reputation, which often lasts longer than the fine itself. An automatic checker is meant to detect such cases early, that is, before a regulatory authority finds them.

The second reason is the sheer volume. A large bank processes millions of transfers every day. No team could review them all by hand. Software, on the other hand, checks every single transaction and only puts the suspicious ones on an employee’s desk.

An important distinction: a compliance checker is not a judge. It does not make a final decision about whether a law has been broken. It pre-sorts cases and documents that a check was carried out. Regulatory authorities explicitly demand exactly this kind of documentation.

From rigid rule lists to learning review systems

The older and simpler design works with fixed rules. A human writes them down in advance, for example: “Report every transfer over 10,000 euros to a country on this list.” The program works through the rules rigidly. The advantage is traceability, since one can always say which rule was triggered.

Newer systems additionally use machine learning. This means the software is shown many past cases with known outcomes and derives patterns from them itself. This way, it can also detect anomalies for which no one had previously formulated a rule. Some systems use language models for this purpose, that is, programs that can understand and summarize texts. These then check, for example, whether a draft contract contains a prohibited clause.

Both approaches share the same core problem: false alarms. If the system triggers too often without cause, reviewers drown in alerts and eventually stop looking closely. If it is too lenient, real violations slip through. Striking this balance is the real work involved in operating such systems.

Where such checks run in everyday life

The term is most commonly encountered in the financial sector. When a transfer gets held up with a note about a security check, such a system has often been triggered. A check also runs in the background when opening an account, verifying whether the person is on a sanctions list.

In software development, similar tools are called linters or scanners. They check whether code meets the team’s standards or contains known security vulnerabilities. And in large companies, checking programs verify whether marketing texts comply with advertising regulations.

The term appears in the news particularly in connection with new AI laws. The European Union requires detailed evidence and documentation for high-risk AI applications. This has given rise to an entire market of programs that automatically verify this evidence themselves. Experts refer to this as RegTech, meaning technology for regulation.