Start-up banks are challenging the traditional high street institutions to find more agile ways of working – to do more, smarter, with less.
AI can be an attractive prospect for this. However, deploying this technology is not a plug-and-play situation.
For many banks, we're still at an interim stage when it comes to AI. 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. AI, however, is becoming a matter of necessity – from all sorts of perspectives – cost, risk and market competitiveness to name just some.
Consumers expect services to be easy-to-use, data-driven, instantaneous and catered to them. For banks, this means providing personalised services that address an individual’s spending patterns, device preferences and more.
Successfully accessing, and then analysing, data will lead to more streamlined services for customers across every aspect of banking, such as mortgages, credit cards or personal banking. Quicker time to insights for banks also means quicker service delivery to customers. Banks can capitalise on their insights around customer spending patterns to provide tailored recommendations on financial wellbeing to clients – boosting their customer experience.
However, while there is great appetite for more analytics, more AI and more insight in general to power these types of services, all of these require banks to overcome significant infrastructure hurdles.
You can't plug AI into an old system and expect it to churn out the results you want; instead, you need to get your data into a searchable, agile framework before you can add AI over the top. To do this, banks need technologies such as data warehouse automation. This can bridge the gap between the legacy infrastructure and the future of cloud-based, data agility, by automating a lot of the manual, time-consuming migration tasks involved in data collection.
Data warehouse automation can streamline and accelerate the migration process. In addition, correctly deployed automation can reduce many of the different potential risks that come alongside modernisation: 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 warehouse automation can allow banks to be more competitive, and more innovative. In the greater scheme of things, while monolithic high street banks and newer challenger upstarts vie for customer loyalty, it's never been more important to find ways to become more agile with data and insight technologies.
AI capabilities are going to evolve significantly in the next few years. At the same time legal requirements like the PSD2 or IFRS 9 change frequently – and will continue to do so – requiring more agile architectures. In order to keep pace with these changes, banks will require comprehensive data ingestion, ways to manage the data landscape and faster time to insight systems. During this transition, data warehouse automation will be a critical step between current legacy environments, and a bank’s AI-driven future.
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