Risk Monitoring: Utilization of Artificial Intelligence
Our case study was performed and demonstrated to a global bank having a one of their overseas branches in Tokyo. The goal of this case study is to demonstrate the use of artificial intelligence in assisting back office repetitive work, especially in regards to risk assessment and continuous customer monitoring. The following was raised and performed to address the current issues.
Data Entry Inconsistencies
Being a financial service system provider, we understand the pain points and the conducts of banking and the financial industry. Given that each institution will have their own policies and regulations in conducting their operations, a set guideline is usually placed to keep operations monitored. However, manual data entry for customers leads to inconsistencies in format of data entry, due to the cause that different administrator handling the data input may have different preferences in inserting the data. This leads to an unfiltered data, causing difficulties for further system digitalization and automation. To respond to this query, we proposed and demonstrated a serialized and uniform data entry through OCR and auto-filling since the onboarding process. This minimizes manual intervention, which significantly reduces the inconsistencies in new data entry.
Update and Continuous Monitoring
As part of risk-monitoring and risk assigning is to monitor the nature and content of transactions, a continuous monitoring with an up-to-date information pertaining to each customers are necessary. In regards to this, we proposed and demonstrated our behavioral-based artificial intelligence to detect abnormalities in transaction behavior periodically, which triggers a flag in the administrative panel. Our system will match the behavior with the background information of the individual obtained during onboarding process, updated during their journey, and will trigger risk assessment and assigning of risk level automatically.
Restrictions for High-Risk Clients
An agenda for this case study was to automatically restrict the use of client account upon triggered high-risk flag. Once the artificial intelligence detects an abnormality in transaction behavior, the system will flag and will raise alert for the administrator to check and verify. This hybrid approach is taken to minimize false positive, but maintaining a highly automated, and easing the operations of the back office. Upon checking and verifying, the flagged customers will be imposed on restrictions in making transactions or even accessing their accounts, depending on the reason and risk-level triggered.
Regulatory Compliance & Security
Anti Money Laundry and Countering The Financing of Terrorism is a necessity and is emphasized in regulatories globally. One of the key to impose a successful attempt to minimize money laundry and other acts is through a strict implementation of a thorough background check and constant monitoring in customer behavior. As such, our system complies and reinforces the attempts that the regulators are imposing globally.
The Future of Banking
The Tokyo branch of the bank confirmed the necessity to implement digitalization, starting from digital onboarding by implementing a seamless electronic know your customer (eKYC) system. As specified by local authorities, eKYC plays a significant role in risk-monitoring, prior to transactions. Thus, a simplified, streamlined, and an automated system will benefit those operating in the back office of the bank, ultimately benefiting the customers. In addition, a system that streamlines the back office operation, easing the tasks on monitoring and flagging will be beneficial for the banks overall operations, and ultimately, serving the customers on thier needs with a faster line of services.