How does advanced analytics enhance financial crime compliance?

Using advanced analytics approaches significantly increases the effectiveness and efficiency of financial crime compliance.

In brief

• Application is particularly promising in areas such as client onboarding/KYC, AML and fraud detection.
• Since these techniques are heavily data-driven, it’s important to invest in data quality and expertise to interpret results.

The financial industry has always been at the forefront of using data processing and analytics. But financial institutions are also major targets for fraudsters and criminals, as they handle large amounts of money on a daily basis. Against this background, it’s no surprise that data analytics is a key component of financial crime compliance.

As technology advances, the financial industry has started to adapt and implement new technologies to combat financial crime, moving on from comparatively simple rule-based systems to more advanced data analytics. In this article, we take a closer look at two of the most effective tools in this fight: artificial intelligence (AI) and graph analytics.

Artificial intelligence

AI is used to analyze large amounts of data and identify patterns and anomalies that might indicate fraudulent behavior. One of the biggest challenges in financial crime compliance is the sheer amount of data that needs to be analyzed. This includes data from various sources, such as customer information, transaction data and social media.

For example, AI can be used to analyze transactional data and identify unusual behavior – such as sudden changes in spending patterns or large withdrawals – in a far more specific way than classic rule-based systems. This helps banks to identify potential money laundering activities with significantly fewer false alerts, which considerably increases monitoring efficiency. It also allows new money laundering patterns to be detected before they are even spotted by human experts. This contributes to enhanced transaction monitoring effectiveness.

The recent IIF and EY Survey Report on Machine Learning - Uses in Credit Risk and AML Applications indicates that more than half of the responding banks already use machine learning in production with a further 30% doing pilot projects. A key aspect of applying machine learning in such a regulated area as AML is to take the regulator on the journey with you as most regulators themselves are in the early stages of a learning curve when it comes to using AI.

Another application of AI is the area of document analytics. Documents play a kay role in many processes from passports used to confirm a client’s identity in onboarding to bills of lading in trade finance. Machine learning can help to handle this huge volume of documents by identifying and capturing key information such as names, addresses and account numbers, or to flag any discrepancies or potential issues with the documents, such as forgery. 

Graph analytics

Graph analytics is an innovative approach that can help combat financial fraud by identifying relationships between entities (e.g., clients) or flows (e.g., of money) that are not immediately apparent by other means. Graphs are representations of data that focus on the relationship between entities. By analyzing complex networks of data, such as financial transactions, graph analytics can detect suspicious behavior that may indicate fraudulent activity or money laundering.

Graph analytics offers many advantages but stands out for these three core abilities:

Graph analytics is a powerful tool that can help combat financial fraud by uncovering hidden patterns and relationships between individuals and organizations. By using advanced algorithms to analyze complex networks of data, investigators can identify suspicious behavior that may be indicative of fraudulent activity. As the technology continues to evolve, it is likely that graph analytics will play an increasingly important role in the fight against financial fraud.

High potential

Both the use of AI and graph analytics offer great potential to optimize and improve current processes for combating financial fraud. However, a successful implementation of these technologies depends on some critical success factors.

One of the main challenges is the need for large amounts of high-quality data. Without good data, these technologies cannot effectively identify patterns and anomalies. Banks must also ensure that they are using the right algorithms and models to analyze this data, and that these algorithms and models are constantly updated to reflect new types of fraudulent activity.

Another challenge is the need for skilled personnel who can effectively use and interpret the results of AI and graph analytics. Banks must invest in training their staff to use these technologies and interpret the results. They must also ensure that they have the right infrastructure in place to support these technologies, including the necessary hardware and software.

In conclusion, AI and graph analytics are powerful tools that can help banks identify and prevent financial crimes. By analyzing large amounts of data and identifying patterns and anomalies, these technologies can help banks stay ahead of the curve and prevent new types of fraudulent activity. However, implementing these technologies is not without its challenges, and banks must invest in the right infrastructure and personnel to effectively use and interpret the results of these technologies.