The introduction of Apple’s transformative product in 2007 was a moment that changed the future. Like the now iconic iPhone, the launch of generative AI tools like ChatGPT and Google Bard have captured the public’s imagination, launching an explosion of applications and experiments across nearly every industry, from business and commerce, to art and government.
For many of these use cases, the “how” behind the AI model isn’t particularly relevant. If you ask ChatGPT to compose a wedding invitation in the form of a Shakespearean sonnet, you don’t necessarily need to understand how AI can instantly conjure the right rhyme scheme.
A wedding invitation is one thing; a financial institution’s credit lending decision is quite another. When it comes to such high stakes decisions, understanding the inner workings of the model is vitally important—even required. For those use cases, explainable AI is the solution.
Artificial intelligence (AI) and machine learning have received significant press in recent years, yet the promise and opportunity of these technologies is sometimes obscured by fear and uncertainty among the general public and financial services executives alike. Despite the financial industry’s rich history of embracing new technologies to improve customer service and operational efficiency, it has lagged retail, manufacturing, healthcare and other sectors in implementing effective AI solutions.
At nCino, we see several areas of exciting opportunity for AI in banking. Some, such as credit risk management, are relatively mature. Others, like customer onboarding and operational efficiency, have barely been touched. Forward-thinking technology providers and financial institutions have already or will soon begin exploring these areas in earnest to address real pain points in efficiency, productivity, customer experience, risk management and customer insights.
To borrow an analogy from the theater, these areas of opportunity can be broadly categorized within three application domains:
Back of the House: Banks and credit unions have explored enhanced cognitive technologies in the back office for decades, particularly in the areas of credit risk and fraud detection. In fact, one of the earliest use cases for AI and machine learning was the development of the FICO score for credit approvals. But back of the house offers so much more opportunity for the implementation of AI, from employee supervision and training, to operational efficiency and faster time to close.
Under the Lights: Customer engagement continues to be an important emerging area of opportunity within financial services. Consumers who are already comfortable speaking to their Amazon Alexa-enabled devices, getting help via online chatbots or asking Siri for directions to the nearest coffee shop expect their bank or credit union to create a user interface that is just as seamless, pleasant and easy to access. With the benefit of enhanced customer insights, AI can be used to help deliver a frictionless and expedited onboarding process, eliminating unnecessary steps for both the customer and the institution.
Behind the Curtain: Some of the most exciting and potentially transformative opportunities for financial institutions are occurring in the realm of cognitive insights. Through AI and machine learning, banks and credit unions can gain enhanced intelligence on their customers and prospects to make more accurate product recommendations and offer more relevant financial advice. To be effective, financial institutions must move beyond a broad demographic understanding of their customer base. Cognitive technology takes traditional customer segmentation to the next logical iteration, where a massive data set is fed through an AI-enabled system, allowing the bank to subdivide its customer base into a virtually unlimited number of highly targeted segments.
Banks and credit unions must take this opportunity to partner with innovative firms developing effective solutions for real-world use cases, especially in the areas of operational efficiency, customer engagement and customer insights. The key is to focus on seamlessly incorporating this technology into existing processes, while also maintaining a human touch with customers. The most effective way to achieve this ideal is through deployment of a single platform solution, seamlessly integrating and analyzing data from all customer channels and across the organization.