speech from verbal to text. Each of these artificial intelligence branches are utilized throughout financial institutions and countless other industries around the world. The Benefits of Artificial Intelligence Artificial intelligence is used in various fields and applications ranging from online shopping, advertising and machine translation enabling cross-language communication, to improving the overall operations and cost efficiency of financial institutions. The use of AI technologies in financial institutions can drastically reduce operational costs while significantly increasing productivity. With its broad range of uses, AI can potentially aid financial institutions in reducing costs associated with products and services, and it can enhance the overall customer experience as it bridges the gap between customer convenience and relationships. AI can benefit a financial institution’s lending process as it can expand credit access, assist in financing decisions, decrease underwriting times and costs and enhance both the borrower and lender experience. AI can be beneficial throughout other areas within financial institutions, such as identity validation and real-time anti-fraud monitoring. The opportunities and benefits when it comes to AI and financial institutions seem to be endless. But there have to be challenges, right? Artificial Intelligence Challenges Artificial intelligence isn’t perfect. Like any other enhancing technology, AI comes with its own set of risks and challenges. Some of those risks and challenges include system integration and a gap in skills. With system integration, the data behind AI is equally as critical as the technology itself. In order for the utilization of AI to be beneficial and effective, the data quality and quantity need to be accurate. This involves organizing data and preparing for integration. This means that financial institutions with a core processor will have to coordinate between their core system and their AI technologies. This can often be a complex and costly undertaking and financially burdensome, especially for small financial institutions and community banks. Financial institutions may also run into a more complicated integration process if their core processors and AI solutions vendors are competitors of the same or similar products and services. This challenge often leads to increased fees and costs for integration. Even if financial institutions are able to work out all the kinks related to system integration, there is always the challenge of obtaining expertly trained staff who are knowledgeable in building and deploying AI solutions. With the rapid advancement and use of AI technologies, it has led to a shortage of skilled AI experts in the broader labor force. While this is a challenge that is expected to improve in the future, at present, it leaves financial institutions competing with large tech companies such as Apple or IBM when recruiting for AI talent. An even more challenging area associated with artificial intelligence and financial institutions is meeting compliance expectations on technologies that are surrounded by so much regulatory uncertainty. Financial institutions are expected to identify and manage all risks related to AI and how it is used within the organization. It’s not enough for financial institutions to simply employ the technologies of AI, but rather they are expected to understand the data or inputs that drive the outcomes. Financial institutions are expected to ensure that all data used within the various branches of AI align with regulatory compliance requirements. For example, if the machine learning branch of AI is used in the decision-making for credit, the bank should understand and be prepared to explain what the contributing factors were that the AI system used to make that decision (i.e., what data was inputted to receive the outcome/decision). It 19 West Virginia Banker
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