What are the different types of AI algorithms used in customer service chatbots?

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Customer service chatbots are becoming more common in financial technology, as they can provide quick and personalized responses to customers' queries, requests, and feedback. But how do these chatbots work? What kind of artificial intelligence (AI) algorithms do they use to understand and generate natural language? In this article, we will explore the different types of AI algorithms used in customer service chatbots, and how they can improve the customer experience and satisfaction.

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Ron Hadad

Solution Architect & Team leader | Senior DevOps Engineer | IaC & CI/CD expert | DevOps Lecturer
Anita NGONO?

Financial Markets Engineer ???? | Business Analyst/MOA Front Office????| Optimizing front office processes, trading &…
Sascha Dewald

Senior Vice President Corporate Strategy & Group Development bei DKB I Board Member Mastercard & Bitkom I Podcast Host…

1 Rule-based algorithms

Rule-based algorithms are the simplest form of AI algorithms used in customer service chatbots. They rely on predefined rules and keywords to match the user's input with the appropriate response. For example, if the user types "balance", the chatbot will reply with the user's account balance. Rule-based algorithms are easy to implement and maintain, but they have limitations. They cannot handle complex or ambiguous inputs, they cannot learn from new data, and they cannot generate original or creative responses.

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Ron Hadad

Solution Architect & Team leader | Senior DevOps Engineer | IaC & CI/CD expert | DevOps Lecturer
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While not the flashiest AI, rule-based algorithms are the backbone of many customer service chatbots. Easy to implement, they offer consistent, reliable responses, ideal for simple tasks and FAQs. However, their inability to learn or adapt limits their effectiveness for complex interactions and personalization. rule-based algorithms have their place, but alone, they struggle to create truly engaging conversations. Here are some specific examples of chatbots that utilize rule-based algorithms: Domino's Pizza ordering bot: Guides users through the ordering process, allowing them to customize their pizza and track their order. Sephora Virtual Artist: Analyzes facial features and recommends suitable makeup products based on user preferences.

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2 Machine learning algorithms

Machine learning algorithms are more advanced than rule-based algorithms, as they can learn from data and improve their performance over time. Machine learning algorithms use various techniques, such as natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG), to analyze and produce natural language. For example, a machine learning algorithm can use NLP to extract the meaning and intent of the user's input, NLU to find the relevant information or action, and NLG to generate a natural and coherent response. Machine learning algorithms can handle more complex and varied inputs, and they can provide more personalized and context-aware responses.

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Anita NGONO?

Financial Markets Engineer ???? | Business Analyst/MOA Front Office????| Optimizing front office processes, trading & portfolio management systems.
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En intégrant des techniques de traitement du langage naturel (NLP) et de compréhension du langage naturel (NLU), ces algorithmes peuvent analyser les sentiments du marché en temps réel. Ils vont extraire des insights pertinents à partir de vastes quantités de données non structurées, comme les actualités financières, les tweets, ou les rapports d'analystes. Cela permet aux chatbots de fournir aux clients des banques d'investissement des recommandations basées sur les tendances actuelles du marché.

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3 Deep learning algorithms

Deep learning algorithms are the most sophisticated form of AI algorithms used in customer service chatbots. They use neural networks, which are models that mimic the structure and function of the human brain, to process and generate natural language. Neural networks can learn from large amounts of data and capture complex patterns and relationships. For example, a deep learning algorithm can use a recurrent neural network (RNN) to encode the user's input and a decoder network to generate a response. Deep learning algorithms can provide more natural and human-like responses, and they can also generate new and original responses.

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4 Hybrid algorithms

Hybrid algorithms are a combination of rule-based, machine learning, and deep learning algorithms, that aim to leverage the strengths and overcome the weaknesses of each approach. Hybrid algorithms can use rule-based algorithms for simple and common inputs, machine learning algorithms for complex and varied inputs, and deep learning algorithms for creative and novel inputs. For example, a hybrid algorithm can use a rule-based algorithm to provide basic information, a machine learning algorithm to provide personalized recommendations, and a deep learning algorithm to provide engaging and empathetic responses. Hybrid algorithms can offer the best of both worlds, and they can adapt to different scenarios and customer needs.

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Anita NGONO?

Financial Markets Engineer ???? | Business Analyst/MOA Front Office????| Optimizing front office processes, trading & portfolio management systems.
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Dans les banques d'investissement, les chatbots hybrides peuvent traiter les taches répétitives telles que les requêtes sur les soldes de comptes ou les transactions récentes grace à des règles préétablies. Cela libère du temps aux opérationnels pour leur permettre de se concentrer sur des taches plus complexes et à plus forte valeur ajoutée.

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5 Reinforcement learning algorithms

Reinforcement learning algorithms are a type of AI algorithms that learn from their own actions and feedback, rather than from data or rules. Reinforcement learning algorithms use trial and error to find the optimal response that maximizes a reward or minimizes a penalty. For example, a reinforcement learning algorithm can use a reward function that measures the customer satisfaction, the chat duration, or the conversion rate, and adjust its responses accordingly. Reinforcement learning algorithms can improve their performance and efficiency over time, and they can also explore new and innovative responses.

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Rajesh Sampathkumar

Leader in the Data, AI and Machine Learning Space
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There is a use case for reinforcement learning (RL) algorithms in fintech centric chatbots, as with other chatbots. Generally these algorithms are not directly used as part of the chatbot architecture, which normally comprises of a front end and an ML-based backend, at least as of 2023. The chief benefit of RL in these applications is in generating better responses to the user-provided input, based on the sentiment inferred from the user's feedback. This is implemented in the continuous model training lifecycle by using RLHF - reinforcement learning with human feedback. This technique is being used in large language models such as those released by OpenAI and Anthropic.

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6 Conversational AI platforms

Conversational AI platforms are tools and services that provide ready-made solutions for building and deploying customer service chatbots. Conversational AI platforms use various AI algorithms, such as rule-based, machine learning, deep learning, and reinforcement learning, to create chatbots that can interact with customers in natural language. Conversational AI platforms also offer features, such as pre-built templates, drag-and-drop interfaces, analytics, and integrations, that make it easier and faster to create and manage chatbots. Conversational AI platforms can help financial technology businesses to create chatbots that can meet their specific goals and requirements, without having to code or develop their own AI algorithms.

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Sascha Dewald

Senior Vice President Corporate Strategy & Group Development bei DKB I Board Member Mastercard & Bitkom I Podcast Host ?Unplugged: AI in Finance“ ??? | Mentor I Angel Investor
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Conversational AI platforms are comprehensive solutions for crafting customer service chatbots, integrating various AI algorithms like rule-based, machine learning, deep learning, and reinforcement learning. These platforms enable the creation of chatbots capable of natural language interaction, tailored to specific business needs. They offer user-friendly features such as pre-built templates, drag-and-drop interfaces, and robust analytics. This accessibility allows financial technology businesses to efficiently develop chatbots that align with their unique goals, without the need for extensive coding or AI algorithm development. These platforms are instrumental in simplifying the chatbot creation process.

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Rajesh Sampathkumar

Leader in the Data, AI and Machine Learning Space
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Conversational AI in finance and generally analytics centric use cases likely requires the capability to operate in text-to-SQL modes. The benefit of text-to-SQL in the context of fintech is the ability to use natural language interfaces to databases and data stores. SQL queries may be executed on top of datasets present in databases, with the SQL queries themselves being generated by a large language model. As an example, Langchain and Llamaindex, which are Python frameworks used to build chatbots, have the ability to query a SQL database backend. This querying is done by passing natural language input to LLMs along with database schema information, and retrieving the appropriate SQL query to execute from the LLM.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the different types of AI algorithms used in customer service chatbots?

1

2

3

4

5

6

7

1 Rule-based algorithms

2 Machine learning algorithms

3 Deep learning algorithms

4 Hybrid algorithms

5 Reinforcement learning algorithms

6 Conversational AI platforms

7 Here’s what else to consider

Financial Technology

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What are the different types of AI algorithms used in customer service chatbots?

1

2

3

4

5

6

7

1 Rule-based algorithms

2 Machine learning algorithms

3 Deep learning algorithms

4 Hybrid algorithms

5 Reinforcement learning algorithms

6 Conversational AI platforms

7 Here’s what else to consider

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