Technology

Digitally native customers are driving banks to jump into the future by embracing technological breakthroughs such as artificial intelligence, machine learning and robotic process automation. And in the process, banks are discovering the many advantages of these innovations, from cutting down on costly human errors to improving everything from fraud management, operational efficiency and trading. As they progress through their digital evolutions, many are reinventing themselves for the better.

When A.G. Bell invented the telephone, he had no idea that less than a century and a half later, the phone would be used to talk through—and text, shop, even bank with. Smartphones are small enough to be held in one’s hand and big enough to handle nearly any function. How are banks making fraud-vulnerable processes such as onboarding fast, efficient and secure by verifying the identity of customers?

The huge global increase in connectivity, prompted by the launch of mobile devices, has affected banks just as much as retailers. As a result, financial institutions have had no choice but to put digital at the front and centre of their strategies – using technology to enhance the customer journey at every touch point

Mainframe computers have enabled banks to manage huge amounts of financial data for nearly 70 years, but these legacy systems are today proving to be hindrances to progress. Lean fintechs are taking full advantage of today’s ground-breaking, agile technology, while established banks are struggling to transform their bedrock digital infrastructure for the new world. How are banks migrating to cutting-edge systems that will maintain them on their industry’s frontlines?

The Fourth Industrial Revolution, through which emerging technologies converge to push the boundaries into uncharted territory, has already begun, and data is the fuel that is powering it. Forward-looking banks are not just riding but driving the wave, discovering and implementing the many advantages that vastly improved multi-channel analytics of today’s deluge of data offers.

Cyberattacks have become one of the biggest threats, not only to business but to society at large. Cybercriminals, hacktivists and nation states are now capable of deploying malicious code to bring down everything from corporates to critical infrastructure in an instant.

With Brexit just around the corner, the European banking sector is under significant pressure to forecast and mitigate potential economic shocks. Given the levels of uncertainty over possible outcomes whilst the details of any potential deal (or no-deal) still to be confirmed, how forewarned can banks actually be without testing hundreds of possible permutations? 

The rapid adoption of artificial intelligence and machine learning in all corners of the financial sector, particularly in anti-money-laundering (AML) efforts, has excited and inspired onlookers and participants alike. But as with all innovations, there are pitfalls to unquestioning acceptance that can actually worsen the situations these technologies are meant to address. Human intelligence must work cooperatively and in the lead role alongside AI and ML to guarantee the best results.

The United States has reached a critical point in determining data privacy standards. With mounting concern among all stakeholders, it is no longer a question of whether more privacy laws will be enacted, but how—and specifically, whether the problem will be resolved at the state or national level.

In the decade following the global financial crisis, banks have faced a flood of new laws and regulations. The pace of change has been furious. Banks have been forced to hire more and more bodies to manage large, enterprise-wide efforts in an attempt to simply stay ahead of regulatory enforcement actions and the ensuing fines and penalties.