Behavioral analytics in fraud detection provides a nuanced understanding of user activities, enabling financial institutions to more accurately identify suspicious patterns and enhance their security measures against potential threats.
There is a practical scenario where the team of fraud risk management specialists is analyzing the behavior of customers, employees, and stakeholders who access the institution’s digital channel and perform different financial transactions.
Behavioral Analytics uses machine learning techniques to understand and anticipate the behavior of users, customers, or people at a granular level across each aspect of a transaction.
To analyze the data to predict fraudulent behavior, the fraud risk management team tracks the data of customers, employees, and others who perform financial transactions.
To understand the behavior the fraud risk management team tracks financial transactions data available in internal systems.
Further, the data of fraud incidents, fraud attempts, and related root cause analysis are identified and analysed by the institution’s customers, employees, and stakeholders. Additionally, the profiles and behaviors of customers, employees, and other stakeholders are tracked including their behavior in using resources, platforms, web portals, and digital payment channels.
To predict fraud the team of fraud risk management specialists interlinks the behavior and fraud data to identify links and hidden patterns.
Behavioral Analytics in Fraud Detection
Using AI and ML, linked datasets retrieved from the behavior analysis and fraud analytics is deeply studied to know whether the actual frauds are the result of specific behavior pattern amongst the customers, employees, and stakeholders.
The fraud risk management specialist shall identify similar fraud reasons and assign different levels of risk scores to each behavioral pattern. High-risk or higher number of specific behavior that led to fraud incidents are segregated and accordingly, fraud risk mitigation controls are developed and implemented to avoid similar frauds in the future.
For example, based on the behavioral and fraud analytics data, the fraud risk management team identifies that those customers and stakeholders who belong to specific jurisdiction, let’s suppose jurisdiction X, are found involved in more fraudulent attempts and fraudulent transactions. Based on a detailed analysis of behavior and fraud data it is found that jurisdiction X is declared as a high-risk jurisdiction by the government because of higher level of corruption and money laundering cases. Further, it is discovered that jurisdiction X has lower GDP growth, a high level of inflation, a high rate of unemployment, and lower literacy rates.
Therefore, the fraud risk management team recommends management develop more robust controls to restrict onboarding customers and provide services to customers belonging to country X. Further, transactions initiated from country X needs to be scrutinized before processing. The controls would include checking the KYC of customers and beneficiaries of transactions originating from country X.
Final Thoughts
In the complex digital landscape of financial transactions, the fraud risk management team utilizes Behavioral Analytics, powered by machine learning, to dissect and predict fraudulent behaviors. By meticulously tracking transactional data from customers, employees, and stakeholders, alongside analyzing past fraud incidents and root causes, the team crafts a comprehensive picture of behavioral patterns that may predispose entities to fraudulent activity.
Leveraging AI and ML, this information is synthesized to discern if particular behaviors correlate directly with fraud. An illustrative finding from this method revealed a distinct risk in transactions related to jurisdiction X, a region beset with economic challenges and a track record of corruption. Consequently, the team advised more stringent controls for interactions with this jurisdiction, underlining the proactive measures organizations must take based on nuanced, data-driven insights to safeguard their digital operations.
