Today, in the start-up world, all are interested to do more innovative, with less. Not only for the existing but for upcoming banks, Artificial Intelligence (AI) can be a striking prospect. Though there are so many pros of AI, to use this sophisticated technology, the organisations need preparation and work to obtain the benefits of it, rather than a “plug and play” approach.

Several banks are still at the earlier stages when it comes to AI. There are some examples of extremely advanced systems which are used in fraud recognition and loan verification, but it would be a push to state this is the standard. But, AI is growing as a matter of requirement in every aspect of banking from cost, risk and competitiveness and many more.

Customers assume financial services to be user-friendly, data-driven and personalized for them. For the banks, this indicates providing personalized services that explain an individual’s patterns, preferences and more.

When financial service providers are capable to effectively access and analyze data, it produces more efficient services for customers across every aspect of banking, for example, mortgages, credit cards or personal banking. Quicker time to insights means faster service delivery to customers. As a result, banks can capitalize on their data around customer patterns to offer customized recommendations on financial security to their clients, which increases overall customer experience.

As there is a great need for more analytics, more AI and more insight in common to power these types of services, all of them need banks to beat major infrastructure challenges.

Organizations only can’t just “plug-and-play” into an old system and imagine it to churn out the required results. Instead, financial service providers want to get their data into a searchable, agile framework prior to adding AI over the top. To accomplish this, banks require technologies like Data Warehouse Automation. Automation bridges the gap between legacy infrastructure and the future of cloud-based, data agility, by automating the manual, lengthy migration tasks associated with data collection.

Data Warehouse Automation can modernize and speed up the migration process. Perfectly installed automation also reduces many of the probable risks that come beside modernization: risk of error, risk of doing things slowly, risk of human oversights. Additionally, the cost savings of automation data ingestion processes with data warehouse automation can let banks to be smarter, increasing their competitive value in the process.

In the coming years, we are going to see an AI development. At this time, legal requirements will also change regularly necessitating more agile architectures. To keep pace with these changes, banks will need complete data ingestion, ways to handle the data landscape and faster time to access insights. In this way, data warehouse automation will be a vital step between current legacy environments, and a bank’s AI future.