Customer Screening and Risk Analysis through Intelligent KYC

Banking CIO Outlook | Thursday, November 22, 2018

In order to maintain stability along with an increased number in the customer database, the financial and banking sectors are switching towards an updated KYC process to extract the customer data. With KYC in hand, in-depth information about the customer can be retrieved. This information avoids a company from getting into defamation, and other legal activities arise due to customer frauds.

With more digitization, advanced machine learning and AI techniques have been deployed to cater to the increase in demand of KYC in an efficient manner. The manual process has been replaced with automation, which reduces the human errors along with cost metrics, and failures associated with a better customer understanding. According to the industry experts, over 12 percent of businesses are moving towards automation, and are employing robotics to enhance the rate of productivity along with transactional efficiency, and reduction in non-compliance costs.

In real time scenario, the data that is being collected from the clients are increasing and at the same time, human interpretations fail to process, analyze the collected data, and it is challenging to manually handle the complete process. With advantages of AI, the data obtained can be easily processed, and stored in the centralized system, thereby mitigating the losses due to human interventions.

In the banking sector, the probability of clients having multiple accounts is more and is a tedious task for bank authorities to look after those accounts. This drawback can be limited by deploying AI, KYC, and monitoring through personalized bots. AI’s dynamic automated scrutinization process helps in alleviating the uncertainty and understanding the customer requirements to achieve a better business relationship.

Furthermore, communication is a key source to perform overall financial activities effectively. Also, the trust factor is an important metric to ensure security and governance on the customer data and their online transactions. Through AI, both the aspects can be achieved through high-end authentication protocol and automated robotic chatbots. Moreover, the deployment of AI technologies such as machine learning, natural learning processing (NLP), and natural language understanding (NLU) help capture insights about customer sentiments, feedback, and evaluate the overall performance in terms of accuracy, precision, and total time consumption.

Although AI and automation have provided several benefits to the financial and industrial sectors, there arise certain limitations concerning data security and trust. Furthermore, fraudsters are looking for new approaches to extract customer data, which can be used for certain criminal activities. This limitation can be overcome by developing a high-end security protocol comprising hybrid machine learning protocols.

Weekly Brief

Read Also