Chatbots with Artificial intelligence (AI) use machine learning to talk to people. The first AI bot was developed in the 1960s by Joseph Weizenbaum, a professor at the Massachusetts Institute of Technology (MIT). Today, chatbot technology is highly advanced, enabling the computer to analyze and understand what a person is asking. As a result, the bot facilitates interaction with people, understanding the context and meaning of words; bots can ask questions to create intent and help solve customer problems.
In recent years, AI chatbots have been highly demanded, especially from the financial sector. These innovative services are entering rapidly, thanks to the technology boom in artificial intelligence, machine learning and natural language processing (NLP) technologies.
The chatbot helps an organization communicate and improve the customer experience by providing humans with an environment where they can ask about specific products and services, even without having enough knowledge about them. The intelligent algorithms capture the customers’ intentions and the conversation becomes closer to natural speech. This makes it possible to go beyond the most common chat solutions, namely to help customers only if used on specific keywords and issues. The new generation of chatbots is becoming an active channel for sales and communication with customers. The virtual assistant can provide products and consultations quickly and conveniently to the client.
With almost 30 years of experience, Sirma is established as an innovator in creating chatbot solutions for various industrial verticals. Melinda is an intelligent customer service platform that incorporates a new generation, intelligent bot. It can understand individual intentions using natural speech patterns instead of specific search keywords. Melinda allows financial service providers to deploy chatbot services in activities that do not have predefined scenarios, such as sales, leads generation, active operations, and managing transactions. With this software, communication is more straightforward, hassle-free, and faster – 24/7.
Melinda’s typical applications are answers to various customer questions, problem-solving to help agents from contact centers, sales of additional services and products, personalized information about products and services, promotions, real-time transaction management, fraud detection, and customer satisfaction surveys and many others. Besides its traditional role in operational and customer support, Melinda is also an active Robo-advisor that can analyze and help individuals or business clients in personal finance management.
So, what we should know before the implementation of such an intelligent Robo-adviser in your organization? We have asked Momchill Zarev, Chief Commercial Officer at Sirma, who shared the main steps of the chatbot development process.
Step #1 Purposes and channels
Define your specific use case. The bot can be implemented on one or multiple platforms, depending on the requirements and needs of the business and the channels through which your organization wants to interact. For example, the immediate ones are your mobile banking application and the website, but also it could be in social networks, popular messengers, in all possible communication channels through which your organizations engage with customers.
Step #2 Interface and UI
Ease of use and user-friendly interfaces are vital factors that customers expect to get by default. No doubt, chat is more convenient than calling, and this is the main reason why messaging apps have become so widespread in the last two decades. Therefore, when you decide to create a chatbot, the best strategy is to keep its interface similar to the most popular communication platforms, facilitate quick adoption, and help people establish a communication similar to what they are getting used to. In this way, you’ll mimic human-to-human interaction and create a feeling of personalized service. Furthermore, it is advisable not to tie your customers only to text communication because the deep learning technologies gain traction and opens up many more opportunities for the voice-based chatbot.
Step #3 Data security
Security is paramount when it comes to financial assets and banking operations. People will be more confident and more comfortable in using artificial intelligence chatbots if they trust the provider. Trust is the result of advanced security, and robust authentication processes must be embedded in all areas of the financial organization, including the NLP architecture of the bot.
Step #4 NLP integration
There are different approaches to developing a chatbot and not all require natural language processing technology. The choice depends on the organization goals and the particular use case. It is known that a better user experience comes with the implementation of NLP, enabling bots to better understand and respond to customer inquiries in context. In addition, the technology allows the use of relevant data for training and improving the quality of services.
Step #5 Development approach
The right choice of chatbot development strategy allows companies to shorten the development time, reduce costs, and facilitate further scalability. The first one is to create a chatbot from scratch, choosing all functionalities for interactions and conversation. The advantage of customized solutions is flexibility and full compliance with your specific needs. However, this requires a team with extensive domain knowledge. The second one is the use of SaaS solutions that provide many basic integrations, standard functionalities and services. The disadvantages here are mainly related to configuration limitations and the dependence on the service provider. Finally, there is a third hybrid variant that we use in the implementation of Melinda. We develop a chatbot according to the specific requirements of the financial organization. Sirma has deep technological expertise on how to perform all necessary integrations, provide a team of highly qualified experts for the initial setup of the bot, then adjust the bot to be easily trained by employees and monitor the self-learning.
Step #6 Bot training and Knowledge Base
Once it recognizes the intention, the chatbot interacts with the knowledge base to retrieve information for the response. The knowledge base serves as a hub, as it contains all the information about the products, services or the company. It answers all frequently asked questions, guides and any possible information that the client may request. Creating such a knowledge base is key to the proper performance of the bot.
Step #7 Integrations with other systems
The seamless bot integration with legacy systems or third-party software solutions is critical. It allows to centralize the analysis information, to create a complete view for the client and on this basis to create personalized services. Furthermore, with the successful integration of the other existing systems, the bot can carry out active operations, such as transaction initialization, loan application, account opening, cost analysis and budgeting recommendations, account and payment transaction notifications, fraud prevention and notifications for suspicious activity, and many others.
The rising popularity of Robo-advisors for wealth management is justified because they are getting smarter and can serve as first-level advisors to clients with limited knowledge and funds. The bot is processing all the necessary data for decision-making, a credit recommendation, or a suitable investment product.
Step #8 Why quantity matters?
The bot literally feeds with any data to maintain the conversations and actively communicate through all possible channels. Information and constant training are its driving forces.
After the go-live stage, constant improvements and training are a must, along with daily monitoring of the bot in active mode. Using the real conversation scripts will help in the bot training. For example, monitoring the operating system may show that the systems are fine, but the bot may not respond to specific user calls or may not understand a particular query. The best way to improve it is to monitor its conversations with users.
As a result of the feedback, improvements are made in the training and confidence in the answers is increased. A team member should observe all unanswered questions and then train the algorithm to retrain the bot to understand better.
Step # 9 How fast will you grow?
Be aware of the stages in product development and when you will be ready to scale so that the bot can handle the increased workload. Include a review of performance and key performance indicators when your chatbot has reached the minimum viable product (MVP) phase. Then continue to develop it, considering the results of feedback from the product’s initial users and determine how quickly to achieve this scale.
Step # 10 The right technology partner
One of the challenges traditional financial organizations face when implementing new AI technology is attracting the right provider or in-house talent to lead these projects. However, partnerships between traditional companies and flexible technology vendors are helping to cope with the challenge because they provide cost-effective solutions in contrast to an internal project, which will involve development resources with limited technology experience in AI. Sirma Group has a proven track record of optimizing the use of digital technologies to increase efficiency, reduce transaction costs, operational risk, and eliminate security issues for all kinds of companies from the financial industry.