AI in Banking: Accessible Applications and Use Cases

AI in Banking: Accessible Applications and Use Cases

AI in Banking: Accessible Applications and Use Cases

Overview

Big banks in the US have poured billions of dollars into artificial intelligence (AI). And with good reason — the technology, which broadly encompasses algorithms designed to mimic the human brain, has numerous applications in banking, from improving customer service to helping to manage risk. However, AI isn’t just for these larger institutions. The rest of the industry can benefit from AI, as well, by making smart investments using targeted approaches. This report explains what AI is and why it matters for financial institutions, provides an overview of accessible use cases, offers examples of AI in the real world to help ground this idea for executives just beginning to explore it, and reviews key challenges to adopting AI and how they can be overcome.

Key Highlights

  • Artificial intelligence, or AI, refers to the overall concept of creating processes to stimulate human thinking in computer programs. There are two major subsets of AI: machine learning and natural language processing (NLP).
  • AI is meant to complement face-to-face relationships by making connections through data that a person may otherwise not (or not easily), creating better experiences and driving efficiencies. The difference between AI and traditional software systems is the higher amount of information and data that can be processed at a lower cost.
  • The promise of AI and its benefits is leading to increased interest in and adoption of this technology in banking and financial services broadly — in fact, the AI in financial services market globally is expected to reach $22.6 billion by 2025.
  • For now, the major investors tend to be large financial institutions. However, smaller players can also leverage AI in targeted ways without huge investments in the technology. This report focuses on three key areas that are particularly accessible: improving customer service, expanding loan underwriting, and fraud management and identity verification.
  • Getting started with AI can seem like a huge undertaking for all but the largest institutions — but it doesn’t have to be. Banking providers of all sizes are using AI today; the key is to identify applications and use cases that make sense for your bank and set goals that are achievable.

The Bottom Line

It’s quite possible that the best AI strategy for your bank today isn’t all that complex. Not every institution needs to integrate widely with an array of vendors, or even pursue all of the applications laid out in this report. However, failing to upgrade your technology because of a fear of losing out on personal relationships should not be an option, either. AI isn’t fully mainstream yet, but it likely will be. And getting comfortable with it today, in ways that complement rather than replace the human touch, will go a long way in preparing for that future.

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