By Ritu Dubey, Head of  Europe, Digitate

The popularity of digital banking has soared this year due to the pandemic, and banks are under the gun to adopt new technologies that can help them adapt to new market forces. Seventy-seven percent of global bankers surveyed in an Economist Intelligence Unit report said that unlocking value from AI is what will make the difference between success and failure. But it can be hard to know where to start, and banks have to resist the urge to just apply AI for its own sake. Three key areas where the banking industry should focus when it comes to how to use AI most effectively are Customer Experience, Operations & Processes, and Risk & Compliance.

AI’s application in customer experience

There was a 200% jump in new mobile banking registrations in April, according to Fidelity National Information Services, which works with 50 of the world’s largest banks. And mobile banking traffic rose 85%. Driven largely by the pandemic, demand for mobile banking is at an all-time high.

Mobile banking apps incorporate AI into services like chatbots and digital assistants.

UBS, a Swiss multinational investment banking and financial services company, partnered with  Digital Humans to create a virtual financial assistant for its customers. Others are using AI even for in-person activity – like the robot hosts at Santander’s Visitor Centre outside Madrid, Spain. The Santander Interactive Guest Assistants escort visitors to their destination while playing music.

Banks are also incorporating AI into biometric authentication, authorizations and other processes that improve the customer experience. For instance, it is now possible – and recommended – to automate hundreds of system health checks to make sure all customer-facing systems are up and running each day. This will reduce customer-impacted minutes.

In this digital age, the customer experience is about creating personalized channels for conversation. Banks are sitting on so much data that, with the help of AI, they truly are in the position to cater to the “segment of one.” This will allow banks to focus more deeply on their customers, which builds loyalty and creates differentiation. Going forward, banks will not be able to maintain competitive advantage if they are not using AI.

Using AI to increase efficiency of banking processes

While customer experience is often the most visible application of AI and where many organizations focus their efforts, they shouldn’t ignore internal operations – the “back of house,” so to speak. Beyond a certain point, you cannot dramatically improve the customer experience if there is no investment being made in the mid-office or the back office of a bank, from an AI perspective.

AI holds a lot of potential for increasing stability, resiliency and efficiency of operations and processes like reducing the time it takes to fulfil service requests, eliminating the need to spend human employees’ time on repetitive, manual processes or AI-driven automation for sophisticated tasks like predictive maintenance, KYC (know your customer) document processing, credit scoring and more.

In the world of investment banking, hundreds of thousands of batch processes run to calculate a day’s trade, to calculate Net Asset Value, to complete the settlement process, etc. AI-based automation more efficiently organizes the batch jobs that are responsible for your back-office operations. From creating a deep visibility and understanding of your batch landscape to working across different schedulers, AI baselines performance, predicts business SLA in real time and responds to failures in a complex web of batch dependencies across thousands of jobs. It helps you stay ahead of the curve with “What if?” capabilities that analyse the impact of schedule and technology changes to minimize downstream impact.

AI’s role in addressing Risk and Compliance

Ritu Dubey

In an already highly regulated industry, financial institutions in the EU face some of the most stringent regulations in the world. These include a number of requirements centred on AML (anti-money laundering) and KYC, including pre-screening of customers before opening accounts or fulfilling substantial transactions. There’s also GDPR, armed with the potential to levy multi-million-dollar fines for non-compliance.

Financial institutions are also perennially one of the biggest targets of cybersecurity attacks, and that’s only increased during the pandemic. In fact, according to the Modern Bank Heists 3.0 report, cyber-attacks against this sector increased by 238% from February to April 2020.

Automation and AI are extremely useful here, as well. Use AI-driven automation to schedule start and end-of-day checks, as well as to detect how many different versions of the same technology you are running within the same IT enterprise. The automation then pushes patches wherever it detects older and vulnerable versions of technologies. Set up intelligent automation for compliance monitoring, anomaly detection and cyber risk prevention.

In terms of fraud and money laundering, there are many AI-based tools available that conduct checks and perform fraud detection. They detect and flag connections between certain entities that would likely go unnoticed if only humans were doing these checks.

Realized potential

The need to accelerate digital transformation is immediate and critical. Though there has been a focus on customer-facing improvements, the long-term gains of mid- and back office improvements must not be ignored because that is where the scale game can be played. AI-driven automation creates differentiation in customer service and improves processes and risk/compliance needs.

What’s more, AI increases productivity and improves efficiency. It is being used to eliminate problems from happening – not just predicting them but preventing them. Implemented properly, AI also has a role to play in restoring banks’ profitability by helping to digitize legacy systems and mine vast stores of data that reveal areas of weakness and of potential growth. With all the benefits AI has demonstrated, banks that fail to adopt AI-based technologies run the risk of failing overall as competitors reap the benefits and customers flock to them.