Exploring Responsible AI Adoption

“AI banking, or Artificial Intelligence in banking, refers to the application of various AI technologies such as machine learning, natural language processing, and robotic process automation within the banking and financial services industry.

These technologies enable banks and financial institutions to automate processes, enhance customer service, detect fraud, manage risk, and personalize financial products and services. AI in banking can be used for tasks such as customer service chatbots, credit scoring, algorithmic trading, fraud detection, and risk management. By leveraging AI, banks aim to improve efficiency, reduce costs, and deliver a more seamless and personalized experience to their customers.”

The preceding definition of Artificial Intelligence in banking was generated by ChatGPT. The ease of using the tool and relevant content provides a striking example of the power of generative AI. Yet, requesting this definition a few months from now may provide an even more refined definition.

The power of AI tools, combined with rapid tool advancement, can seem daunting for bankers. As with other innovations adopted into financial services, those who act thoughtfully and seek ways to leverage new technology to further their organization’s strategy will come out ahead.

What banks and consumers are saying about AI

According to recent research from EY Parthenon, retail and commercial banks are already investing or plan to invest in AI applications, with 60% of large banks (assets above $500 billion) having made tangible investments in AI and 86% of smaller institutions already investing in or planning to invest in Artificial Intelligence.

But these investments should be considered with prudence and responsibility. Bankers must pay attention to what customers are saying about AI.

Consumers express concern about the role of AI in banking. According to FIS’ Trust In Generative AI Survey, the vast majority of consumers say higher data transparency (86%), human oversight (85%), and government regulation/legislation (82%) would help them trust generative AI more when used in the context of financial services.

“AI banking, or Artificial Intelligence in banking, refers to the application of various AI technologies such as machine learning, natural language processing, and robotic process automation within the banking and financial services industry.

These technologies enable banks and financial institutions to automate processes, enhance customer service, detect fraud, manage risk, and personalize financial products and services. AI in banking can be used for tasks such as customer service chatbots, credit scoring, algorithmic trading, fraud detection, and risk management. By leveraging AI, banks aim to improve efficiency, reduce costs, and deliver a more seamless and personalized experience to their customers.”

Consumers agree that getting advice from a human expert is best; however, AI technology is rapidly evolving. Consumer sentiment will continue to evolve as well.

The same FIS survey revealed that a lack of trust is the primary reason U.S. consumers are not interested in AI-powered services (according to 50% of survey respondents). Nearly half of the respondents (48%) also say they prefer interacting with human financial services professionals.

Understanding AI opportunities

Within banking, AI is currently being used to automate high-volume administrative functions that depend on simple and repeatable tasks, responding to basic prompts, or pattern recognition in large data sets. Traditional AI or machine learning examples include data aggregation and reporting, monitoring for fraudulent transactions, or even money laundering patterns. In contrast, generative AI is a type of Artificial Intelligence technology that can produce a wide range of content including text, imagery, audio, and synthetic data.

Both types of AI offer many possibilities to address productivity challenges in today’s financial institutions. Potential use cases abound from improving customer service, with chat bots listening in on conversations and looping in a supervisor to head off potential customer issues to testing of new banking applications. The key for regional and community banks in dealing with AI opportunities is to avoid trying to “boil” the proverbial ocean. Instead, bankers must take a responsible approach to AI, exploring the aspects and power of the technologies that best align with their institution’s overall strategy and immediate tactical objectives. The following steps offer a rational approach to exploring AI at a regional or community financial institution:
Steps to responsible AI adoption

1. Access current AI initiatives within your organization. Executives may be unaware of the ways in which their bank may already leverage AI. An audit of technology initiatives at your institution will provide a common ground (and perhaps unexpected insight) into your organization’s AI experience.
   
   
2. Educate staff on AI best practices and opportunities, leverage the knowledge and expertise of your key technology partners. AI users and work groups should exist as an integral offering from your bank’s technology partners. Take advantage of learning from partners and peers.
   
   
3. Develop a short-list of AI initiatives supportive of key strategies of your bank – from improving your customers’ service experience to increasing the speed of new product introduction. Then align a few key AI initiatives supportive of your goals and objectives.
   
   
4. Share AI initiatives with key partners and stakeholders, take advantage of all your team’s experiences to learn from their AI efforts, such as with board members who are executives within other industries, at other institutions, and within other industries.
   
   
5. Evaluate the effectiveness of AI initiatives. Use unique Key Performance Indicators to assess any improvements and survey customers on their experiences. Adjust as needed.