How Robotic Process Automation could help banks improve efficiency in debit card fraud processing.
It is reported by European Central Bank that the total value of fraudulent transactions in Europe annually amounts to €1.8 billion, while in Australia, of the $788.6 billion transaction value on cards in 2018, $574.3 million is deceitful (Australian Payment Network, 2019). But have you ever wondered how financial institutions handle these fraudulent transactions? It used to be a long and painful process, which often bogged down banking workers in repetitive and mundane paperwork. However, things are getting much brighter with the adoption of Robotic Process Automation, also known as RPA.
Before RPA…
Depending on the internal protocols and infrastructures of each institution, procedures to process debit card fraud can significantly vary. Still, there are certain stages that shall be followed, namely collect dispute transaction data from web-based core banking system gather and investigate information associated with the claim; and process debit card fraud disputes via card management system.
Without the help of technology, every single step would be conducted manually, from data gathering, data processing to making decisions. For some banks, transactions can be handled by batch, while for others, they must be resolved separately. This traditional method is not only time-consuming, discouraging and overwhelming for workers but is also prone to more errors. Given the fact that there are hundreds of card fraud claims per day, it is such a challenging task for financial institutions to ensure speed and accuracy through conventional techniques, which are mostly based on human intervention.
…and after
- Data gathering
Collecting information on disputed transactions is the first and foremost step bank workers have to follow. The robot can help to navigate and log into the web-based system of the bank, select date range and export the transaction data. Bots can also be used to compile additional information from multiple internal databases or public domains to give a holistic view of customer data for fraud analysts.
- Data processing
After relevant data is captured, RPA will assist banking officers in removing duplicates, formatting and refining data to make it fit for use. When combined with Artificial Intelligence (AI) technologies such as Optical Character Recognition (OCR), Text Analytics, and Machine Learning, the robot is able to track activities, collate payments, determine anomalies and identify fraud patterns. Apart from freeing up humans from monotonous tasks, RPA also ensures much higher accuracy and escalating productivity, as it can self-learn from previous cases, thereby reducing time spent on subsequent disputes at most.
- Decision making:
RPA has revolutionized the back-end operations of banking organizations, and debit card fraud processing is not an exception. The automation solution has been able to reduce costs while easing financial employee workloads, as well as increasing the quality of fraud processing and minimize the number of human error-related issues. While challenges faced by financial institutions in detecting and processing fraudulent card transactions may continue in the future, RPA, more or less, can empower efficiency in resolving these challenges.
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References
- Nets (2019). European Fraud Report – Payments Industry Challenges. [Link]
- Australian Payments Network (2019). Australian payment card fraud 2019. [Link]
