Harnessing analytics in the fight against fraud and money laundering

The fight against fraud and money laundering no longer sees businesses and compliance personnel going up against easily identifiable, shady criminals. Auditors can no longer identify patterns of suspicious behaviour months after the transaction was completed. It is no longer feasible to work in a reactive way. 

Transactions are done so fast nowadays that a manual method of monitoring is unacceptable today. Businesses need an automated monitoring system which detects and catches fraudulent intentions right away, and, if possible, before they even happen. In addition, in today’s fast-moving technological world, customers demand more than ever that their data is safe and protected. They do not want nor should they expect their data to be vulnerable to outside theft attempts. However, they still require financial institutions to enable fast online payments. There is a lot on the line but new tools for AML and fraud analytics are making it possible for companies to stay ahead of criminal intentions. 

Letting machines do the work

Machines are undoubtedly better equipped than people to handle large data sets since their computing capabilities allow them to process a huge number of transactions simultaneously. They are capable of recognizing thousands of fraudulent patterns, unlike the few that can be captured by rule setting. The drawback for these machines is that criminals are discovering loopholes and it is likely that whatever rules you set they will find a way to get past them. But what if you employed a smart system which had a mind of its own?  

New innovations in AML and fraud prevention have combined rule-based systems with smart machines and AI-based detection systems. A hybrid system such as this is able to detect and identify thousands of fraudulent patterns and learn while scanning the data. An automated, analytics-based transaction monitoring system can reveal new suspicious patterns and recognize organised crime more quickly, consistently, and efficiently. Therefore, such systems are a must have for businesses across a wide range of industries.

So how can you harness the power and insights of analytics in your fight against money laundering and fraud?

Identify your needs

The first step in adopting a transaction monitoring new system is the problem identification. To get optimal information, it is feasible to consult your employees as they are the compliance analytics experts at your organization and are therefore capable of pointing out what analytics you need, which data and techniques to use, and what results to report on. 

The experts in the organization can also identify the optimal combination of rule-based and AI approaches to adopt for the best possible chance to detect fraudulent behavior at the earliest point possible. There are also other decisions that need to be taken. For example, businesses need to optimize their existing threshold tuning, they need to explore big data, develop and interpret machine learning models, comb through text data to find relevant information, and prioritize alerts. Through the automation of these areas, an organization can both reduce the need for manpower – reducing costs – and improve its detection and prevention of money laundering and fraud. 

The benefits of the analytical approach

The analytical approach towards money laundering and fraud prevention has received numerous testimonials. Among other things, the quality of positives for further investigation has been found to be better. In addition, investigators are given a much clearer idea of why a possible positive has been flagged which results in a more efficient investigation. Efficiency in turn leads to the reduction in both false positives and false negatives. This improves customer experience.

The analytical approach makes it possible to discover complicated or organized fraudulent activity which rule-based systems would miss out on. Using analytics, organizations can group customers and accounts with similar behaviour together and subsequently set the appropriate risk-based thresholds.

Contact us to learn how ComplyRadar utilises a graph database, providing you with the power of true analytics to inspect and act on suspicious transactions in real-time.