Five Things Community Financial Institutions Should Know About AI Limitations

While using Artificial Intelligence (AI) in digital banking offers many benefits, financial institutions must be aware of some limitations and challenges. Here are a few limitations of using AI in digital banking:

1. Data Privacy Concerns
2. Lack of Transparency
3. Bias
4. Dependence on Data Quality
5. Technical Challenges

Data Privacy Concerns

Data privacy and security concerns can arise as AI technology relies on large amounts of data to train algorithms. Community financial institutions working with third-party providers need to ensure that how data is being collected, stored, and used is responsible and ethical.

Lack of Transparency

AI algorithms can be complex and difficult to understand, leading to a lack of transparency in decision-making. This complexity can make it challenging to explain to customers, and members why certain content was presented to them or certain decisions were made (e.g., about loans).

Bias

AI algorithms can be biased if the data used to train them is biased. This can result in discriminatory practices that disproportionately affect certain groups of customers. Community financial institutions must ensure that their AI partners use unbiased data sets and regularly audit their algorithms to detect and correct any biases.

Dependence on Data Quality

In other words, garbage in, garbage out. AI algorithms rely on high-quality data to provide accurate predictions and recommendations. The results may be unreliable if the data used to train the algorithms is inaccurate or incomplete. If you see counter-intuitive results, it may be that the AI algorithm has found a unique association in the data or is being given low-quality data.

Technical Challenges

Many believe implementing AI technology in banking requires significant technical expertise and resources. This can keep community financial institutions on the sidelines when it comes to unlocking the power of AI. However, by seeing vendors with cloud-based delivery models wrapped in extensive subject matter and service level expertise (Software AND A Service), the need to acquire expensive technical skills to develop and maintain these systems can be mitigated.

AI in digital banking offers many benefits if community financial institutions are well-versed in the potential limitations and challenges that might arise. By being aware of these limitations and addressing them proactively, banks and credit unions will be able to unlock the power of AI and big data and level the competitive landscape while delivering better service to customers and members.