The success of artificial intelligence (AI) solutions across industries stems from the ability to automate routine tasks, improve the quality of client support (CS), and handle large volumes of data. Enabling companies to expand the scope of operations, algorithm-based models facilitate achieving set objectives and boosting productivity without significant investments. With 43% of accounting, investment banking, and personal asset management businesses employing AI products, it becomes necessary to integrate LLMs into workflows. In this guide, we will explore how utilizing AI in customer communications for financial services facilitates reaching long-term goals.
What is the Role of AI in Customer Communications for Financial Services?
Success in the FinTech industry is based on an enterprise’s ability to implement AI tools to get an edge over competitors. Algorithm-based products shape the sector due to the untapped potential of machine learning technology. However, getting the most out of innovations requires financial institutions (FIs) to analyze how they utilize data to enhance financial services customer experience (CX), increase the efficiency of CS teams, and optimize processes.
Open banking solutions offered by FIs unlocked new opportunities and transformed the financial sector. Due to the integration with application programming interfaces (APIs), clients can contact banks and other organizations via preferred communication channels.
Businesses implement AI products to reduce response times, process tasks quickly, and expedite workflows by setting clear escalation rules. There are many upsides to utilizing AI in financial services:
- Processing increased amounts of data: AI tool deployment allows banks to analyze information about clients and learn how to fix issues affecting customer experience in financial sector.
- Building powerful CS systems: The provision of personalized support is instrumental to improving satisfaction, maintaining a retention rate, and offering relevant replies to complex queries.
- Training employees: CS agents and other staff members no longer need to undergo a protracted learning process to acquire new skills. Using extensive knowledge bases with advanced search tools, they access any information in a fraction of a second. While it may take some time for financial customer service teams to master AI tools, as a result, they will be better equipped to provide top-notch support.
- Minimizing expenses: Digitization enables FIs to reduce the amount of paperwork, mitigate the risk of human errors, and save money. Manual data entry is expensive, so organizations should deploy LLMs trained to gather and analyze information automatically.
Achieving consistent quality of CS fosters trust, enables FIs to build a strong brand image, and facilitates expanding market presence. Such companies as MetaDialog specialize in custom AI solutions that integrate with legacy CS systems. Allowing firms to automate up to 87% of interactions with clientele, these products are instrumental to reducing resolution times and boosting productivity.
How to Leverage AI to Improve CS for Financial Services
The overwhelming success of natural language processing (NLP) solutions made it possible to analyze past interactions and discover effective methods to boost engagement. ML algorithms facilitate establishing trusting relationships with clientele and embracing a personalized communication strategy. FIs heavily rely on information. Finding a new way to deal with large datasets is pivotal to enhancing CS in the finance sector.
Embracing the power of artificial intelligence in financial services and integrating algorithm-powered systems involves such steps:
- Exploring the preferences of the target audience interested in digital services: It may require analyzing which communication channels they like to use and what products they are interested in;
- Devising a strategy to build customer loyalty: It should expedite acquisition, foster engagement, and increase the efficiency of retention practices.
- Analyzing processes to discover optimization opportunities: FIs must improve their workflows to reduce expenses and discover the barriers hindering development.
- Building an AI-based CS platform: Experienced developers develop chatbots, train them to analyze user data, and deploy them online.
When virtual assistants communicate with customers, engineers collect feedback and use it to make further adjustments. AI models are capable of learning and improving performance over time. However, using high-quality datasets during the training is mandatory to achieve consistent improvement.
As it may be arduous and time-consuming to develop a custom LLM from scratch, reaching out to authoritative providers like MetaDialog can help a company save valuable resources. Outsourcing the development of AI CS systems to a team of experts does not require substantial investments and enables organizations to get a solution compatible with CS platforms and messengers.
Use Cases of AI in CS Provided by Financial Organizations
Personalized support allows companies to reduce the number of complaints by up to 40%. While most FIs now consider using AI to make CS services effective, the technology opens multiple improvement opportunities. Financial advisors deploying AI tools report a 35% increase in portfolio profits. Leveraging such tools allows banks to increase cross-selling rates by 65%.
As the financial industry is heavily regulated, MetaDialog sees its goal in helping FIs integrate LLMs and handle user data while maintaining full compliance. Devising data-driven strategies allows banks to form long-term relationships with clients. Deploying AI in customer communications for financial services is crucial, as it allows organizations to boost profits. Banks with high client satisfaction rates increase deposits faster by 84%. FIs utilize AI models for many purposes:
- Increasing contact center efficiency: NLP technology facilitates building chatbots capable of recognizing intent and fine-tuning responses, making them relevant to the context. When customers communicate with them via chat or email, they receive automated replies. AI systems analyze and route queries to speed up resolution.
- Automating CS processes: Sending notifications, informing clients about suspicious transactions, and recommending investment opportunities are among many tasks that can be completed by bots.
- Automated document processing: AI allows CS agents to reduce the time spent on paperwork.
- Personalized suggestions: AI models study customer preferences and offer recommendations based on the conversation and purchase history. Integrating such products enables organizations to ensure that up to 85% of respondents will order the recommended services.
- Virtual assistance: FIs minimize expenses by training digital CS bots to understand and solve complex queries. The usage of 3D avatars capable of maintaining intelligent conversations using natural-sounding tones is also on the rise. They operate without pre-established scripts and can improvise based on non-verbal information they receive.
- Anti-fraud solutions: AI bots recognize the signs of suspicious behaviors and notify managers about possible security breaches.
A higher level of engagement is achieved through a detailed analysis of intentions and collecting data about client preferences and needs. Personalization is the key to building trusting relationships with customers, as it allows banks to offer tailored solutions.
Bottom Line
Summing up, AI-driven bots enable FIs to analyze sentiment with high accuracy, provide recommendations based on past communication and feedback, and reach out to their target audience to drive high engagement. Deploying LLMs in CS helps organizations reduce risks, ensure compliance, improve safety, and enhance CX.
MetaDialog recognizes the potential of using AI in customer communications for financial services and assists clients with integrating custom LLMs to speed up resolution and improve satisfaction rates. Contact our team today and achieve consistent growth.