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Conversational AI in Financial Services in 2024

With the proliferation of digital channels and increasing customer expectations, financial services organizations are under pressure to deliver convenient, personalized, and efficient experiences while managing costs and complying with regulations. 

To solve these problems, the banking sector and other financial institutions are turning their face towards AI supported technologies. According to Accenture, by 2035, artificial intelligence technologies will add more than 1 billion dollars value to the financial services industry.1

Figure 1. The impact of AI on industry growth (green = baseline; blue = AI steady state)

Source: Accenture

Especially the use of conversational AI in financial services is an effective way to streamline operations, improve customer experiences, and deliver innovative and cutting-edge services. This technology offers conversational banking to customers and at the same time helps reduce customer support costs. In this article, we will explain how to leverage conversational AI in financial services.

What is conversational AI?

Conversational AI refers to the development of computer programs and systems designed to simulate human-like conversations with users. It typically involves natural language processing (NLP), machine learning, and speech recognition technologies to enable an AI system to understand, interpret, and respond to user inputs, usually in text or voice form.

Conversational AI systems can range from simple rule-based chatbots to advanced machine learning models that can understand complex language patterns and carry out sophisticated interactions. The primary goal of conversational AI is to facilitate effective, human-like communication between humans and machines to perform various tasks, answer questions, or provide information.

What are the reasons for preferring conversational AI over chatbots in financial services?

It’s essential to understand that conversational AI is a more advanced form of chatbots, and the terms are sometimes used interchangeably. However, the distinction lies in their sophistication and capabilities. 

Traditional chatbots are often rule-based systems with limited understanding and a fixed set of responses. In contrast, conversational AI uses advanced natural language processing, machine learning, and contextual understanding to provide more accurate, personalized, and human-like interactions.

In financial services, both conversational AI and chatbots have their advantages and disadvantages.

Conversational AI

Pros

  • Enhanced customer experience: A conversational AI chatbot can handle more complex queries and provide personalized, context-aware responses, leading to improved customer satisfaction rates.
  • Scalability: Conversational AI can handle larger volumes of simultaneous interactions and adapt to a wider range of topics.
  • Continuous learning: Machine learning models in conversational AI learn from user interactions, improving their understanding and responses over time.
  • Integration with other systems: Conversational AI can be easily integrated with other data sources and systems within financial institutions, providing more holistic support and deeper insights.

Cons

  • Development and maintenance: Developing and maintaining conversational AI systems can be more complex and resource-intensive than traditional chatbots.
  • Data privacy concerns: Conversational AI may require access to sensitive customer information, raising data privacy and security concerns.
  • Misunderstandings: Despite advances in NLP, conversational AI can still misinterpret user inputs or provide incorrect responses, potentially leading to frustration.

Chatbots

Pros

  • Simplicity: Chatbots are easier and less expensive to develop and maintain compared to conversational AI systems.
  • Quick response time: For predefined and simple queries, chatbots can provide instant responses.

Cons

  • Limited capabilities: Chatbots typically have a limited set of predefined responses and may struggle with complex queries or understanding context.
  • Lack of personalization: Chatbots often lack the ability to provide personalized responses based on user-specific information or preferences.
  • Poor user experience: Rigid, rule-based chatbots can lead to frustrating customer experiences if they are unable to answer questions or understand inputs.

Figure 2. Traditional chatbot vs conversational AI chatbot in a financial customer inquiry

Source: Yellow.ai2

Sponsored:

Zoho SalesIQ is amongst domain-specific platform with its specifically tailored services in financial services and client engagement. The software operates 3K+ chatbots with 465K+ portals. Some of the services SalesIQ offers in finance includes:

  • Automated triggers and custom banners and chats with tailor-made finance solutions for customers visiting specific pages
  • Proactive interaction with the customers with chatbots
  • Through integration with SalesIQ, businesses can allow their customers to view certain sensitive information, like account details or balance, from the chat window. Chatbots can engage with prospects and customers 24×7 from your website or mobile app
  • Companies can use chatbots to put experts in touch with customers
  • Allows audio and visual calls (Figure 3)
  • Real-time translation in customer interaction
  • Access answers to FAQs and articles from their live chat window
Figure 3. Source: Zoho SalesIQ 3

What are the use cases of conversational AI in financial services?

1- Account management

Conversational AI can access customer account data and interact with backend systems, enabling users to perform tasks through simple text or voice commands, such as: 

  • Checking balances
  • Transferring funds
  • Managing credit cards

For example, a customer could ask a virtual assistant to transfer a specific amount to another account or pay a utility bill.

2- Customer support

Conversational AI understands and processes natural language, allowing it to interpret customer queries, search for relevant information, and provide accurate answers in a human-like manner. It can handle a wide range of questions, from simple customer inquiries about account balances to more complex requests related to loans and mortgages.

Check our article on the use cases of conversational AI for customer service to learn more about this.

3- Financial advice

Conversational AI can act as a financial advisor by helping users set budgets, track spending, and make informed decisions. It can analyze customer data to provide personalized advice, such as suggesting ways to reduce expenses or identifying appropriate investment opportunities based on risk tolerance and financial goals.

4- Finding the nearest ATMs and branches

Conversational AI can help users find the nearest ATMs and bank branches. By accessing the user’s location data and integrating it with geolocation APIs, the conversational AI system can determine the closest bank branches and ATM locations based on the user’s current position. It can then provide the user with relevant information such as: 

  • Addresses
  • Distance
  • Directions
  • Operational hours

5- Fraud detection and prevention

Conversational AI can analyze transaction data and patterns, identifying suspicious activities or anomalies that may indicate fraud. It can then notify customers and financial institutions to take necessary action, such as locking accounts or flagging transactions for further investigation.

6- Market updates and investment information

Conversational AI can gather real-time market data and provide users with information about stock prices, market trends, and relevant news. Customers can simply ask for specific information, like the current price of a particular stock or the latest news about a company, and the conversational AI will provide the requested details.

7- Customer onboarding and KYC

Conversational AI can guide new customers through the onboarding process by providing step-by-step instructions for account opening, document submission, and identity verification. It can also validate information and cross-check it with external databases, streamlining the KYC and KYS processes.

8- Payment reminders and collections

Conversational AI can send personalized reminders to customers when payments are due and help them set up payment arrangements or negotiate repayment plans. By automating this process, it reduces the need for manual intervention and streamlines the collections process.

9- Product recommendations

By analyzing customer data, such as spending habits, credit history, and preferences, conversational AI can provide tailored suggestions for financial products and services, like the most suitable credit card or the best investment plan to meet the user’s goals.

10- Voice-based services

By integrating voice recognition technology, conversational AI can enable voice-based interactions with financial institutions. Users can perform tasks like checking account balances, transferring funds, or paying bills using voice commands, providing a more seamless and convenient experience.

Why should financial service providers implement conversational AI solutions?

Financial service providers should implement conversational AI solutions for:

  • 24/7 availability
  • Automation in various tasks
  • Cost savings by automating routine tasks and reducing operational costs
  • Continuous improvement due to machine learning and generative AI models
  • Competitive advantage by offering innovative customer service experiences
  • Integration with other data sources and systems for holistic support and deeper insights
  • Scalability in handling a larger volume of queries and adapt to a wider range of topics
  • Personalization by offering recommendations and support based on user profiles

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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