Apparently, there is a student of artificial intelligence (AI) who thinks that humans and robots will be able to marry legally by 2050. We are not quite sure about this, but interesting trivia nonetheless. We have been writing about AI for a few years now. When we started, it was pretty cutting edge and expensive, so many community financial institutions (CFIs) weren’t too interested. These days, we see it all around us and the financial industry is implementing AI as it realizes some important benefits. Most recently, we discussed RegTech, which is an AI-driven way to manage regulatory compliance and can help bring down costs. Yet, AI can help in many more ways. Here are four main areas of AI.
- Natural language processing is when speech is used to gather information or assist through chatbots and virtual assistants. Alexa and Siri immediately come to mind. But, there are many more behind the scenes helping customers with basic needs and extending service hours, to enhance their customer experience. This allows bankers to focus on more complex activities.
- Computer vision involves the processing and authenticating of data. The AI program reads data and matches it with other data to confirm identities, automate invoice payments, and process checks digitally. This saves staff time and catches fraud more quickly than if these were done manually.
- Data analytics is the source of all AI and machine learning processing. This encompasses the comprehensive processing of all data so it really touches all divisions of AI. Specifically, it is instrumental in cybersecurity as large pools of data can be processed for red flags, transaction fraud, and KYC anomalies among others. Needless to say, this becomes especially critical as more data is gathered through digital banking, mobile onboarding, wire transfers, etc.
- Robotic process/automation is used for repetitive activities such as reconciliation, where speed and efficiency are especially important and provide cost savings because of those features.
AI example at PCBB
Of course, these areas do not exist in vacuums and can cross over from one to the other. As an example, our robust product platform uses automation and data analytics to process data quickly, flexibly, and securely for international wires and Canadian check deposits, among other things.
So, what steps should CFIs take, if they are interested in AI?
Of course, these areas do not exist in vacuums and can cross over from one to the other. As an example, our robust product platform uses automation and data analytics to process data quickly, flexibly, and securely for international wires and Canadian check deposits, among other things.
So, what steps should CFIs take, if they are interested in AI?
- Start by asking which efficiencies your institution needs. If AI can be used for those efficiencies, then it would be worthwhile to investigate further. While you are reviewing your processes, look for use cases among your peers to see if you have similar needs that could be satisfied through AI.
- Assess the cost-benefit analysis. With cloud and API technologies becoming more mature, costs have come down. The number of APIs has expanded dramatically, which will allow for more flexibility from off-the-shelf AI-charged products. This will help cost-sensitive CFIs.
- Identify how some of these products can be integrated with your core system and any third-party software. The key is to optimize your system for better efficiencies, including addressing exceptions and higher success rates. So be sure you have this goal in mind from the start.
- Look for the right internal staff to train on how the AI machine learns and processes along with the biases that can result. AI transparency can only result from a good understanding of these elements. Alternatively, you can outsource this activity to ensure your institution is covered from a compliance standpoint from the very beginning.
