Artificial intelligence (AI) is radically changing the way banks operate. There are already a large number of applications in place in retail banking, and most of your customers have probably at least interacted with a customer service chatbot.
Now, AI capabilities are transforming the business model for corporate banking. As a result, the potential impact of AI is racing up the executive agenda. Here are six ways AI is already starting to transform the way corporate banks operate:
Anti-money laundering (AML) fraud detection
Money laundering still is a major concern for corporate banks. With increasingly sophisticated AI, corporate banks can rapidly review trade transactions and improve fraud detection, prediction and prevention. This is a powerful enabler to complying more efficiently with global AML regulatory requirements.
AI powered detection also significantly improves the corporate customer experience. Algorithms can assess multiple factors and categorise clients according to their risk profile; allowing banks to customise the appropriate level of verification required. The use of AI in corporate loan origination systems, such as VeriPark’s VeriLoan, reduces human errors and plays a vital role in mitigating risk and boosting operational efficiency.
Cross-selling and up-selling of personalised services
We have all come to expect more personalised offers and services. Corporate banking clients are no exception. VeriPark’s AI-driven Corporate Next Best Conversation (NBA) solution helps Relationship Managers (RMs) to provide appropriate and timely advice to their clients.
When RMs have access to useful information, such as clients’ complete transaction history, they are better placed to offer tailored services and product offers. Our NBA solution also uses social listening and news feed monitoring to detect and alert RMs to any relevant news updates – something very few RMs have time to track effectively for each client. This reduces the risk of RMs missing valuable customer service or up-selling opportunities.
By using predictive analytics and algorithms to analyse client behaviours, NBA also alerts RMs to products and services that are suitable for their specific client. With a user-friendly application programming interface (API), RMs can see the recommendations on their tablet during face-to-face sessions with clients. This facilitates an easy and natural discussion about business news, industry insights and potential sales of new products, such as currency swap contracts, or improved customer services, such as remote check deposit facilities.
According to Capgemini’s research, 87% of banking executives said it would be “highly impactful if an AI engine could spot relevant events that led to engaging with a client and closing a deal.”
Streamlining processes to improve customer service
Many corporate banks are still dealing with outdated legacy systems and laborious manual processes. VeriLoan includes an easy-to-use rule engine built with Inrule for workflow and business processes, such as loan origination and servicing. This means more efficient routing of loan approvals and event alerts triggered by specific criteria such as interest rate changes.
Rule engines can also help banks streamline operations and improve complex business processes. For example, the rule engine can be tailored to define business rules that efficiently determine who must act for each task in the loan origination and servicing process. This all helps RMs to respond quickly and effectively to their clients’ requests.
Increasing operational efficiency with Robotic Processing Automation (RPA)
Robotic process automation (also known as RPA) involves using software robots or virtual assistants programmed to complete repetitive and labour-intensive tasks. RPA can drastically reduce much of the manual work associated with specific corporate banking tasks, such as verifying identities and complying with AML checks. For example, it’s estimated that effectively implemented RPA could reduce the time spent on fraud and AML checks from 40 minutes to around 2 minutes – meaning human employees can focus on complex client interaction and decision-making activities.
Improving customer engagement and satisfaction with chatbot
AI-powered chatbots are already automating many routine customer service questions in retail banking. They are boosting customer experience and loyalty by providing customers with instant support and fast solutions, 24/7. And, there’s no reason why the same technology can’t be used to help corporate banking clients solve day-to-day problems and queries.
Improving customer insights from Machine Learning (ML)
Banks are constantly gathering data through interactions with corporate clients online and in person. Sifting through this data is an impossible manual task, but ML can analyse this data swiftly and effectively. This means banks can learn from all those client interactions and start to predict the future – what does our complaints analysis tell us about clients’ needs? Which client segments are likely to be most profitable and how should we target and service them?
AI is set to change every function and business line in corporate banking. From fending off money launderers to sifting through mountains of data to predict which customer segments are most price sensitive. That gives banks opportunities to drive revenue growth, differentiate themselves through superior client experience, reduce operational costs and improve risk management effectiveness and efficiency.