Do you remember RadioShack? The electronics retailer became a classic example of a company that failed to adapt and innovate as eCommerce changed many industries. But their slogan from the 90s — “You’ve Got Questions, We’ve Got Answers” — is still relevant today because so far, customer service is still a priority for many companies.
However, instead of an employee in a physical store — or a person sitting in front of a computer — questions are getting answered more and more by chatbots. These AI-driven applications are becoming a vital, cost-effective solution for offering 24/7 customer support and reducing waiting times, which can lead to better user experiences and higher ratings in customer satisfaction.
Rise of the Bots
Chatbots are AI-driven software applications designed to simulate human conversations, facilitating interactions between users and machines. These computer programs serve as virtual assistants, and the good ones are sophisticated enough to respond in a conversational manner.
Operating through text or voice, chatbots are frequently deployed across various platforms, including websites, messaging apps, and customer service portals. They utilize technologies such as natural language processing (NLP) and machine learning (ML) to understand user intents, process requests, and provide relevant responses.
In the finance industry, chatbots are often a great investment, especially when availability and security are key. But beyond handling frequently asked questions, chatbots in fintech can provide account information, assist with transfers or applications, offer personalized experiences, and help consumers file appeals to resolve disputes.
At Growth Acceleration Partners (GAP), we help fintech companies — and businesses across a variety of industries, from software development and IT companies to healthcare, energy, and hospitality organizations — make smart investments in revenue-generating and mission-critical applications core to their business.
Making AI an Appealing Process
In the financial sector, “appeals” refer to the formal process of challenging a decision (including credit determination and loan classifications) made by a regulatory authority. The appeal process must be fair and transparent, with specific guidelines in place to access all necessary information and documents.
However, financial institutions contend with malicious tactics, such as cases where an excessive amount of mail is sent in a short time period to purposely clog communication channels, overwhelm the system, and create administrative burdens.
To follow regulations, appeals have to be processed, and most financial institutions face a challenge of accelerating or redirecting these requests based on multiple unstructured data types. Unstructured data can include mail receipt images, PDFs, customer dispute contents, and support chat conversations with service agents.
This data all needs to be:
- Classified for the generation of customer profiles
- Redirected toward proper service channels
- Handled in a faster manner
To meet due dates (and avoid having to unnecessarily concede on appeals), companies often have a very large group of employees looking at mail manually to review disputes. Therefore, this process is a great candidate for automating the process by implementing machine learning-powered pattern matching algorithms.
Avoid Machine Takeover
One important thing to keep in mind: At GAP, we do not advocate for handling the entire process automatically — as we believe in supervision — but in interactive steps to each piece of mail and data or determine where it needs to go next.
By implementing AI in small but impactful ways across the entire pipeline, we can ensure data goes in the right direction, freeing employees up to work on other critical tasks. Classification actions that speed up small and repetitive semantics-sensitive tasks can be handled by lighter and less expensive models, which are powered by the same mechanisms that provide understanding to generation-focused LLMs, having been fine-tuned on text corpuses representing examples of correct labelings.
This approach of contextual direction (i.e., redirecting a question to the right vector based on context and relevance) is similar to what GAP has already executed with our own internal chatbot. Our expertise gives us a unique advantage in discerning how to extract appropriate context from each appeal of unstructured data in a fintech company’s intricate and dynamic network.
By decoding the language of appeals through machine learning, GAP has created a system of predictive routing based on deep pattern recognition.
Embracing technology such as AI-driven chatbots can enhance customer interactions and streamline operations, so companies can remain competitive in an ever-evolving market. By integrating these innovative solutions thoughtfully, we’ve seen businesses in the financial industry improve efficiency while maintaining the human touch customers value.
GAP values striving for greatness, being agile, and investing in people. Ultimately, the future of customer service lies in a harmonious blend of technology and personal engagement, allowing companies to answer the modern consumer’s questions effectively and promptly.
