In an era where banking technology evolves at a rapid pace, a certain bank has taken significant strides by deploying its core banking applications on-premise while utilizing Amazon Web Services (AWS) for its state-of-the-art analytics applications.
According to HCL Tech, These applications are not just supplementary; they are developed in-house, marking a profound commitment to technological advancement within financial services.
The bank’s visionary process involves a comprehensive Workload Automation (WA) solution that seamlessly integrates both on-premise systems and AWS services. This solution is crucial as it enables the orchestration across different platforms without manual intervention, ensuring efficiency and robustness in operations.
HCL Workload Automation emerges as the optimal solution for this complex requirement. It functions as a meta-orchestrator, not only bridging the gap between on-premise systems and AWS but also extending its capabilities to manage Kubernetes workloads. This is further enhanced by its out-of-the-box plugins that connect effortlessly with various AWS services, making it a powerhouse for managing diverse and dynamic banking operations.
The bank has implemented a sophisticated mechanism to detect fraudulent transactions in real time using AWS Simple Queue Service (SQS). Upon detection of a suspicious transaction, an AWS SQS Receive Job is initiated, capturing the details of the transaction immediately. Following this, the AWS Simple Notification Service (SNS) is employed to alert the customer about the suspicious activity without delay, ensuring prompt and proactive customer service.
Further steps include extracting crucial transaction details such as the account number, transaction amount, and type via simple Unix jobs. These details are crucial for a deeper analysis which is conducted by querying the on-premise database that stores historical transaction data of the customer.
Post data extraction, the historical transaction data, along with the current transaction data, are uploaded to an AWS S3 bucket. This dual data storage is not only a backup measure but also a preparatory step for further analysis.
An AWS Lambda function named VerifyTransaction then takes over, analysing the suspicious transaction against historical trends to ascertain its legitimacy. Another Lambda function, BehaviourModels, is crucial as it builds and retrains the AI model based on the customer’s response to the fraud alert—whether they confirm or deny the transaction’s validity.
The implementation of HCL WA offers multiple advantages. It provides seamless orchestration across a wide range of AWS services and on-premise applications, all of which are managed through native, code-free job types. This system ensures end-to-end visibility of the workflow, enabling not just the detection but also the management of events in real time. The automatic and seamless retraining of behavioural models as new data is assimilated showcases the dynamic adaptability of the system.
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