Increasing digital transformation has completely disrupted the finance industry. Customer expectations drive smarter solutions, and conversational AI is one of the top ones.
Online banking and other financial transactions are fast becoming the norm. Every operation in the finance industry is going digital, and most importantly, customer connection is now more digital than physical.
Under this new paradigm,
conversational AI is critical in keeping a finance industry enterprise connected with its customers. This sector deals with a critical asset- money and investments. Satisfied customers and secure data are two of the most critical aspects of this business. Conversational AI takes care of at least one of them.
What is conversational AI?
Conversational AI (CAI) can be referred to as advanced chatbots. They have several advantages over chatbots for human-machine interaction.
While chatbots offer trained, generic responses to user queries, CAI tools can respond with a complete understanding of the context. As a result, CAI tools can maintain a better customer connection.
They leverage natural language processing (NLP) and machine learning algorithms to provide a much better quality of interaction. CAI tools or chatbots can better interpret a query more accurately and provide a relevant, clear response.
Conversational AI in Finance
Rapidly increasing digitization has changed the way the finance industry works.
Customers demand convenience and personalized, around-the-clock service from their financial service provider.
At the same time, there is a need for complete privacy of their data and transactions. Internally, the industry brands also have it tough from the compliance and regulatory perspective. They have to comply with privacy security and data regulations, among others. Cost is another huge factor for enterprises in this sector.
Effective implementation of AI-powered tools can meet most of these needs. Accenture’s report, The Art of AI maturity : Advancing from practice to Performance, says that
The biggest adoption of AI for customer connection is through conversational AI or chatbots.
Using Conversational AI in the finance industry has both positives and negatives.
The advantages of Conversational AI in Finance:
Conversational AI can engage with customers seamlessly to resolve their queries, give financial advice, or even help perform transactions. It has access to all the customer data the company has to provide the most accurate inputs for customers. It can pull out details of earlier transactions, payments, pending payments, etc., whenever asked.
- 24*7 availability: which is expensive and mostly inefficient with human customer support. It tirelessly and accurately allows around-the-clock responses to data queries, transactions, and any other requirement a customer may have.
In fact, with the adoption of AI, customers have come to expect instant and convenient service since this is the biggest strength of a CAI chatbot.
- 100% personalization for each customer: With complete access to data points about every customer, this chatbot can provide completely personalized services and responses every single time.
It can offer advice and financial recommendations tailored to the customer’s needs. This is another factor that will drive loyalty.
- AI-based Automation of Operations: AI automates repetitive tasks like account inquiries, transactions, and customer support.
Conversational AI chatbots and tools can resolve urgent and more complex customer queries, delivering cost savings.
- Completely tailored responses to customer queries create and establish customer loyalty and enhance satisfaction.
- Each query is also a learning for the CAI tool, so, with more user interactions, it learns better ways to respond.
Based on NLP, it will take better input from each interaction to improve its responses.
- The CAI can analyze and use large amounts of data: In a very short time frame to give the best response. This is completely not possible in a human interaction scenario. The added advantage is that it can adapt to a much larger list of topics in query over time.
- Easily integrated with other systems in the bank: It is easy to integrate CAI tools with other systems and data sources to best support customer prompts.
- Providing a clear market differentiation: Deploying a CAI chatbot can give any financial institution a competitive advantage. The market connection is much faster and provides higher customer satisfaction.
However, there are also a few challenges that Conversational AI tools face when used in the finance industry.
Disadvantages of Conversational AI in the finance industry
- Cost is the biggest challenge any company faces: For a new tech adoption. Conversational AI is no different. It needs big investments in development and maintenance. The hardware and software needed for the NLP training is expensive.
- Complexity of processes: The complexity that CAI entails often needs special skills. That is an additional cost, and there is still a risk with the system performing optimally.
- Customer Data privacy concerns: CAI often needs to access personal customer data to respond with accurate information. This is a privacy risk and always a threat. Besides, banks must strictly comply with data privacy rules before allowing this.
So, another layer of complexity is added to the operations.
- Bias and Misunderstandings: Even though it’s trained on sophisticated language algorithms, there is still a chance of misunderstanding a query or inadvertently displaying a bias. The smallest such mistake can create a very negative impact on customer satisfaction.
- Finding the right fit CAI bot for your needs: The banking operation needs a very mature and sophisticated conversational AI tool.
So, banks need to identify the tool according to the process where it will be deployed. For simple queries, simple chatbots will work.
Then, CAI tools can be used only for more complex or frequently occurring customer issues. This will optimize costs and ensure the smarter machine fields the toughest questions.
Also Read: How AI-powered Chatbots are Revolutionizing the Banking Sector
- Deployment without proper Requirement analysis: Whenever a new technology arrives on the enterprise scene, technology leaders rush to adopt it. They do it for a competitive advantage. They need to study a whole list of questions before deciding.
The first question is whether it will be interoperable with my existing systems and databases. The lack of proper integration can cause the whole process to fail, wasting time, money, and effort. So, this should be a big red flag.
Conclusion
All enterprise-level deployments have their advantages and disadvantages. However, there is always a way CIOs can ensure the challenges are met and risks mitigated.
A technology like conversational AI can be a game changer for any brand. Any effort to resolve its risks and challenges will add value to a banking or financial services company. It’s worth the effort and investment in the long run.
