In the startup world, everyone is looking to do more, smarter, with less. For up and coming banks, Artificial Intelligence (AI) can be an attractive prospect. There are many advantages to AI, but to utilize this sophisticated technology requires preparation and work to receive the benefits of it, rather than a “plug and play” approach.
Many banks are still at the earlier stages when it comes to AI. There are some examples of incredibly sophisticated systems being used in fraud detection and loan authentication, but it would be a push to say this is the standard. However, AI is becoming a matter of necessity in all areas of banking from cost, risk and competitiveness and more.
Customers expect financial services to be easy-to-use, data-driven and personalized to them. For the banks, this means providing personalized services that address an individual’s patterns, preferences and more.
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When financial service providers are able to successfully access and analyze data, it leads to more streamlined services for customers across every aspect of banking, such as mortgages, credit cards or personal banking. Quicker time to insights means quicker service delivery to customers. Banks can then capitalize on their data around customer patterns to provide tailored recommendations on financial wellbeing to their clients, which boosts overall customer experience.
While there is a great hunger for more analytics, more AI and more insight in general to power these types of services, all of them require banks to overcome significant infrastructure challenges.
Organizations simply can’t just “plug-and-play” into an old system and expect it to churn out the desired results. Instead, financial service providers need to get their data into a searchable, agile framework before adding AI over the top. In order to execute this, banks need technologies such as Data Warehouse Automation. Automation bridges the gap between legacy infrastructure and the future of cloud-based, data agility, by automating the manual, time-consuming migration tasks involved in data collection.
Data Warehouse Automation can streamline and accelerate the migration process. Correctly deployed automation also reduces many of the potential risks that come alongside modernization: risk of error, risk of doing things slowly, risk of human oversights. In addition, the cost savings of automation data ingestion processes with data warehouse automation can allow banks to be more innovative, raising their competitive value in the process.
In the next few years, we are going to see an AI evolution. During this, legal requirements will also change frequently requiring more agile architectures. To keep pace with these changes, banks will require comprehensive data ingestion, ways to manage the data landscape and faster time to access insights. During this path, data warehouse automation will be a critical step between current legacy environments, and a bank’s AI future.
Read More: Temenos Launches Data Lake to Fuel Next-Generation AI-Driven Banking Applications
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