Why Artificial Intelligence is a Game-Changer for Banks Worldwide

Faced with a fast-paced and increasingly competitive business environment, well-established companies who have enjoyed prolonged periods as market leaders need to adapt to new innovations. Today, data and technology are driving business transformation in every sector around the globe. Neglecting the innovative power of technologies such as Artificial Intelligence (AI), Machine Learning (ML) and big data analytics will result in a competitive disadvantage for legacy companies and is likely to have a negative impact on their business. JP Morgan CEO Jamie Dimon recently predicted this with a stark warning: “I expect to see very, very tough, brutal competition in the next 10 years.”

With the decade of AI in full swing, companies risk becoming obsolete and outpaced by start-up competitors if they do not embrace AI-enhanced technologies to improve their business operations. This is particularly true for the banking industry whose well-established players face heightened competition from non-traditional FinTech companies. 

How should companies respond?

A recent Deloitte report, titled ‘Banking disrupted: How technology is threatening the traditional European retail banking model’, highlights the need for banks to focus on AI-enhanced technologies and adopt data analytics methods to compete with up-and-coming businesses. The more traditional business approach many banks have displayed until recently is increasingly being disrupted by new entrants who are transforming customer demand towards instant and digital payment methods. These changing demands require banks to use the latent potential that is available through decades of customer data: banks should embrace AI-enhanced analytics to exploit the magnitude of accessible customer data in order to optimize their business operations and branch networks.

Major banks and other legacy companies are ill-advised if they think they can simply replicate a start-up approach to AI technologies. Instead, they should act according to these four recommendations in order to create a robust data culture that supports the resilience of their business:

• Embrace transparency: Sharing non-sensitive information such as business performance data throughout a company improves the common understanding of where a business is at and how business goals can be achieved.

• Improve accountability: Increased transparency implies greater accountability. Comprehensive and robust data analysis is necessary to account for proposed results and potential shortcomings.

• Appreciate unconventional approaches: AI-enhanced technologies often challenge or contradict traditional business practices. Data analytics thrive through unconventional methods.

• Use multiple data sources: AI technologies will only be as effective as the quality of the data going into them. Companies should apply advanced analytics to a wide range of data sources, from their own customer data to high-quality and alternative data including adverse news, companies data, Politically-Exposed Persons, legal records, and more.

Why AI matters for banks

Increasingly, banks are joining the global move towards embracing AI-enhanced technologies in order to better understand consumer demands and improve the resilience of their business. The use case for these technologies in the banking and financial industry is manifold and stretches from Anti-Money Laundering (AML) compliance and customer communication to streamlining business processes. Most recently, three major banks have made global headlines by making AI a priority:

• In May, the Commonwealth Bank of Australia, the country’s biggest bank, launched a joint venture with a data analytics firm to provide enhanced data insights to customers and automate decision-making processes.

• In June, American investment giant JP Morgan Chase completed their third FinTech acquisition in the past year to acquire an ESG (Environmental, Social, and Governance) investment platform specialized in merging enhanced analytics with personalized investment opportunities.

• Also in June, Deutsche Bank announced a new partnership with American Fintech powerhouse Oracle to accelerate the bank’s digital transformation process. With the newly-announced collaboration, the two companies aim at exploring potential further use cases for data security technologies, AI, and analytics.

These initiatives clearly pave the way for further AI-fueled innovation in the years to come. For legacy companies to retain their market leadership positions, following a path based on insights from AI and big data is necessary. Otherwise, they risk falling behind those new competitors who have embraced AI, ML, and big data from day one.

The bottom line is this: whether we talk about Robotic Automation Processes (RPA) to manage compliance risks, through tools such as ongoing risk monitoring, or AI to optimize customer experiences, unlocking the full potential of AI and big data is a must for banks in today’s world. Faced with ongoing disruption of the financial industry by FinTech, AI-enhanced and data-driven application will clearly be a game changer for legacy companies all around the world. Now, the only question remaining is: How far are you on your AI journey?

See how Nexis Data as a Service can help.