Anomaly Detection Software : Key to Reduce Fraud

Fraud and money laundering are among the biggest challenges Banking organizations face. To deal with these, banks are investing in AI-based anomaly detection software that help banks identify/detect fraudulent transactions using risk level, based on a wider range of granular customer data. Fraud cases have different patterns. Upon detecting such patterns (for example, similar opening deposits, payment inaccuracies, inaccurate application information, continuous deposits and withdrawals), anomaly detection software with real-time authentication can trigger/alert the concerned teams. Time series anomaly detection software helps banks in investigating triggers/alerts. Thus, this technology aids Banking firms in monitoring anomalies automatically by understanding customer behavior and pattern, and helps maintain better compliance standards.

Let’s deep dive more into how anomaly detection software is helping banks reduce fraud.

Banks have formulated a security strategy to implement a streaming analytics technique with real-time anomaly detection mechanisms to identify fraudulent activities. User behavior anomaly detection in banks is done through:

??Data loss prevention (DLP) management

??Identity and access management (IAM)

??Security information and event management (SIEM)

??Threat intelligence and management

Banks are upgrading their behavioral anomaly detection solution that uses machine learning and Markov modelling to detect unknown abnormal behavior patterns like rare transaction sequences. Machine learning technology allows experts to understand the behavioral pattern by looking at the algorithmic patterns from the large amount of clustered data sets. This allows banks to differentiate between normal and abnormal behavior patterns. The banks make use of scoring functions in these scenarios to reduce the number of false alarms.

Network attacks have exceptional patterns that are not found in normal traffic behavior. Many of the banks can detect network attacks by making use of k-NN algorithm, Bayesian network, and so on. Network behavior anomaly detection helps banks in better management of risks and performance measurement processes. These are done through:

??Network intelligence and security systems

??Network traffic analysis (intrusion detection that identifies strange patterns in the network traffic that signals a trigger about hacking)

??Risk mitigation and management systems

The k-NN algorithm also helps banks in evaluating the credit risk of loan applications. It predicts future behavior, in terms of credit risk based on past experience of customers with similar characteristics. Banks are using these tools to construct a predictive loan model and simultaneously maintain favorable PAR (portfolio at risk) levels.

Banking organizations have a huge amount of unstructured data. The huge data sets are an opportunity for banks to scale up their current statistical and computational approaches. Banks are using AI technology for automated anomaly detection from records and databases. Bayesian network anomaly detector with the joint probability density function is being used to record anomalous activity. It also helps banks in identifying suspicious activity reporting from a sequence of transactions and in capturing patterns in the customers’ transactions by computational anomaly index. The output is compared against a pre-defined threshold to know if the transaction made by the customer is normal or suspicious.

?A company by name Guardian Analytics, a market leader in machine learning solutions and real-time behavioral analytics made an announcement to provide Real-time Wire Fraud Detection Webinar Series in April 2019.

?Many Fintech companies like Feedzai provide AI based anomaly fraud detection platform that is helping banks from any fraudulent activities. Similarly, Fintech company like Ayasdi is providing anti money-laundering solution that helps banks in analysing customer behaviour and pattern.

?Companies like Cisco Systems Inc. offers products like NetFlow that allows the user to identify anomalies by producing detailed accounts of traffic flows. This helps in providing a high-level of diagnostics to classify and identify network anomalies.

Banks should build better security capabilities in the coming years to avoid fraudulent and money laundering activities. Is your bank ready to adopt anomaly detection software to develop state-of-the-art capabilities for an effective security management system?