4 critical considerations for implementing AI in the banking industry

The promises of AI are great, but understanding the considerations needed to build and implement AI within an organization is challenging. Building the right solution, leveraging the skill sets available and solving the highest-priority problems for the banking organization is the key to success.

From business innovations and media headlines to TV and movies, it seems that artificial intelligence (AI) is virtually everywhere. While still in its early stages across the financial services industry, AI adoption is expected to accelerate over the next few years.

And it’s expected to save companies big bucks. According to a recent study by Accenture, 77% of banks plan to use AI to automate tasks to a large extent in the next 3 three years. In addition, a recent study by Autonomous Next, indicates the potential cost savings of using AI could total $450 billion across the banking industry by 2030.

The State of AI in Financial Services Today

Many large financial institutions, including commercial banks, insurance and wealth management companies, have started using AI or partnered with AI startups. In many use cases AI is augmenting human decision making and automating routine tasks.

As an example, Erica, Bank of America’s AI powered virtual assistant, uses voice commands and texting to help customers with basic tasks like looking up account information or transferring money.


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