Behavioural analytics for fraud detection
Today’s digital landscape offers hackers and other online fraudsters extremely sophisticated means to carry out their dishonest activities. These kinds of cyber-attacks can cost businesses a great deal of money and also tarnish their reputations. Thankfully, business owners today have access to various new-age security measures to ensure that their financial transactions, sensitive business data, and customer information are safe and secure at all times.
Modern technologies like Artificial Intelligence (AI) and Machine Learning (ML) enable algorithms that can use behavioural analysis to detect and prevent criminal or fraudulent behaviour. This blog will offer you a better understanding of fraud detection analytics and explore how modern fraud prevention and management solutions can keep your business and its customers secure.
What is behavioural analytics?
Behavioural analytics refers to the scientific examination of behaviour patterns. Modern fraud detection and prevention solutions incorporate behavioural analytics tools to monitor the behaviour patterns of customers and flag sudden changes in these patterns that indicate unusual behaviour. The ability to spot unusual behaviour with behavioural analytics enables businesses to spot potential security risks or cyber threats extremely early, making it possible to predict and detect anomalous activity and prevent fraudulent transactions from being completed.
How is behavioural analytics used for fraud prevention?
Behavioural analytics is used to detect and flag potential instances of fraud or other illegal activity by changes in established user behaviour patterns.
The process starts with the collection of large amounts of data by monitoring user activity. Data points that are monitored include the customers’ IP address details, location information, the devices, VPNs, and proxies they use, their system and browser configurations, the payment methods they use, the time of day that they log on, their transaction values, and usual purchase patterns. The following steps are typically followed in the behavioural analytics process.
Establishing baseline behaviour
The system analyses patterns in historical data to establish a baseline for each customer’s usage behaviour and habits. This provides an understanding of what is considered typical behaviour for each user.
Behaviour analytics tools constantly monitor user activity in real time, making it possible to flag suspicious activity as it happens. Should a customer display behaviour that deviates from their established usage patterns, the system will generate an alert indicating the possibility of fraudulent activity. Deviations in usage patterns could include unfamiliar purchases that are not in line with past activity or unusually large purchases.
Incorporating machine learning algorithms
Fraud detection analytics can be enhanced with the integration of machine learning algorithms that can adapt to emerging and changing usage patterns and identify suspicious activity through the analysis of large datasets. The usage of machine learning enhances the accuracy of behavioural analysis systems when it comes to spotting and identifying potentially fraudulent user activity.
Creating user profiles and risk scoring
By creating user profiles based on patterns, behaviour monitoring systems can assign risk scores to each user that reflect the likelihood of their involvement in fraudulent activity. The system refers to historical data, machine learning algorithms, and previously flagged anomalies to assign these risk scores, and user profiles with higher than average risk scores may be scrutinised further or require more comprehensive authentication measures.
Fraud detection and prevention
Real-time monitoring of user activity allows modern behaviour analytics tools to detect and flag potentially fraudulent activity before it leads to actual financial losses. The system will respond to potential threats by notifying users, blocking activity that is deemed suspicious, and even informing law enforcement of a potential fraud in progress.
The usage of adaptive security measures
Adaptive security measures can add an extra layer of security to protect financial transactions and user data from cybercriminals. An example of adaptive security is the system automatically requesting additional steps in the user authentication process in response to user profiles and customer behaviour that may be considered high risk.
As online transactions grow in volume in this digital age, behavioural analytics has become an indispensable tool for automated e-commerce fraud prevention. After all, it wouldn’t be possible to manually monitor and analyse the massive amounts of user data in real time to spot fraudulent activity before financial losses transpire.
How can Infosys BPM help with fraud prevention?
The Infosys BPM ecommerce fraud prevention incorporates cutting-edge technology such as artificial intelligence, machine learning, and advanced algorithms backed by a highly specialised team of fraud detection specialists, to protect businesses and their customers from would-be hackers and cybercriminals.