Stepping Stones to Artificial Intelligence in Banking

Neil Barton is the Chief Technology Officer for WhereScape.

The banking industry is ripe for disruption. Startup banks are challenging the traditional monolithic financial institutions to find more agile ways of working, to be smarter and do more with less. Artificial Intelligence (AI) can be an attractive prospect but deploying such an advanced technology is not a plug-and-play scenario.

The reality of the situation is that, many banks are still at an interim stage when it comes to Al. There are examples of incredibly sophisticated systems being used in fraud detection and loan authentication, but it would be a push to say this was the norm.

So why should banks be making the move to AI a strategic priority? And what steps can a bank take now to ensure success with AI systems further down the line?

Regulatory Pushes and Consumer Pulls

The short answer is that AI is becoming a matter of necessity – from all sorts of perspectives, including cost, risk and market competitiveness.

A new generation of consumers expects services to be available 24/7, to be intuitive, instantaneous and catered to them. For banks, this means providing services that cater to an individual’s spending patterns, device preferences and current life situation. It’s a tall order for businesses to provide manually, but all the data on customers is there to provide those kinds of services. Banks just need to find a way to access it.

Successfully accessing, and then analyzing, data will lead to more streamlined services for customers, and quicker time to insights for businesses means quicker service delivery to customers. Banks can also capitalize on their insights around customer spending patterns to provide tailored recommendations on financial well-being to clients – boosting their customer experience.

At the same time, legislation changes are adding additional complexity, including regulations that have had a global impact, like the General Data Protection Regulation (GDPR). GDPR requires “single points of truth,” on customer personal data. Implementation for many organizations has proved to be an exceptionally onerous tasks, if not outright impossible, to complete manually; and banks need some sort of system that can intelligently pull together all the insights into one, clearly representative, report of compliance.

Turning to Artificial Intelligence

If AI is a necessity for consumer personalization and legal compliance, why aren’t banks everywhere simply investing and deploying it? It has emerged that, while there is great appetite for more analytics, more AI and more insight in general, all of these require banks to overcome significant legacy infrastructure hurdles. You can't plug Al into an old system like a USB drive and expect it to churn out the results you want; instead, you need to get your data into a more searchable, agile framework before you can add AI over the top.

To do this, banks need to ensure they are predominantly focusing on stepping stone technologies - such as data infrastructure automation. Data infrastructure automation can provide a vital bridge between the legacy data warehouses that hold many organizations back, and the future of data infrastructure and agility, by automating a lot of the manual, time consuming migration tasks.

This is particularly useful for traditional banks, who have substantial legacy infrastructure environments. These banks tend to have a very large number of old mainframe systems, with little or no modern analytics capabilities - something that they are, or should be, seeking to change as quickly as possible. The more diverse the IT infrastructure landscape, and the more sprawling the legacy and modern systems, the greater the need is to find a way to streamline all this and deploy more sophisticated, quicker analytics capabilities.

Data infrastructure automation can help with this – streamlining and accelerating the data infrastructure migration process. In addition, correctly deployed automation can reduce many of the different potential risks that come alongside modernization: risk of error, risk of doing things slowly, risk of human oversights. And, in addition, the cost savings of automating data ingestion processes with data infrastructure automation can allow banks to be more competitive, and more innovative. In the greater scheme of things, where the monolithic financial institutions are feeling the push from newer challenger upstarts, it's never been more important to find ways to become more agile with data and insight technologies.

IT infrastructure, and particularly analytics and AI capabilities, are going to evolve significantly in the next few years. At the same time legal requirements like the GDPR, are likely to change – and will continue to do so. In order to keep pace with these changes, banks will require increasingly comprehensive data ingestion, ways to manage their data landscape and faster, cheaper time to insight and value systems. During this transition, data infrastructure automation will be a critical stepping stone between current legacy environments, and a bank’s AI-driven future.

 Opinions expressed in the article above do not necessarily reflect the opinions of Data Center Knowledge and Informa.

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