In today's digital age, delivering exceptional customer experiences is crucial for banks to acquire and retain customers. Artificial intelligence and machine learning have huge potential to transform banking experiences by enabling hyper-personalization, predictive insights, and automated customer service. But the key to unlocking the power of AI in banking lies in high-quality, representative datasets.

Many ASR and ChatBot developers are building AI models for improving customer experiences by observing insights from what customers are sharing with their representatives. We have achieved good accuracy in the English language in many industries, including banking, but when it comes to building AI models in other languages, the lack of data is still a big challenge.

With the right datasets, we can develop ML models for various applications like predicting customer needs, powering personalized recommendations, improving customer support, and detecting fraud.

So, in this blog, we will discuss our banking training datasets and why they are best for your model training to improve customer experiences.

What is High Quality Training Data to Empower Customer Experiences in Banking?

High-quality, targeted training data will enable more personalized and frictionless customer experiences via chatbots and voice interactions. With robust data, AI systems can continually improve to handle real-world banking conversations. Let’s see what does it mean for Chatbot and ASR;

For Chatbots:

1Conversational text datasets with diverse user utterances help chatbots understand and respond to a wider range of customer queries and requests.
2Well-labeled intent data enables accurate classification of customer needs and routing them appropriately.
3Entity extraction data identifies key details like account numbers, transaction types, location names, etc. accurately.
4Sentiment analysis training data allows chatbots to detect customer emotions and frustrations early.
5Personality modeling data helps create more natural and human-like conversational experiences.

For Automatic Speech Recognition (ASR):

1Large volumes of speech data across accents and dialects train ASR to understand diverse customers.
2Labeled audio transcripts align speech input with correct text for better speech-to-text accuracy.
3Emotionally colored speech data helps detect sentiment from voice interactions.
4Context-specific data improves recognition of banking-related terminology during calls.
5Multi-speaker conversational data enhances performance for call center use cases.

If we summarize the above explanation, then it says that “ High-quality, diverse training data that represents real world scenarios helps chatbots' and speech recognition systems' understanding of customer needs and conversations. This enables more personalized, seamless customer experiences in banking powered by AI.”

What Type of Banking Training data is FutureBeeAI Offering?

We understand the importance of quality training data to build AI models that can improve customer experiences in banking. Now. let’s discuss what we are offering;

We offer well collected with informed consent, diverse, domain specific datasets for different use cases of banking in many formats. We offer Speech, and Text training data to build AI models.

Speech Data Solution

Speech Data Conversational Wake Words Voice Command Scripted monologues

Our speech data collection services include speech data for general conversations, call center conversations, wake words, voice commands, and scripted monologues across languages and industries. We provide speech data support in more than 50 languages across the globe. Let’s see what our data has and how it can help you build models.

Speech Data: Conversational, Wake Words, Voice Command, Scripted monologues

We provide speech data that helps you build applications for different use cases based on the type of speech. From conversational speech data to wake words, voice commands, and scripted monologues, we are giving developers an edge to build AI for any use case.

A few points to consider;

1More than 50 languages are supported.
2Coverage includes multiple accents and dialects for same language.
3Sample rate control: 8 kHz to 48 kHz
4Mono and stereo files availability
5Domain specific terminologies in datasets
6Diverse speakers, gender ratios, and age group availability
7With our speech data platform, we can onboard, track, and save time on speech data projects.

All these capabilities help us make smooth speech data collection for use cases like ASR, Conversational AI, Chatbots,TTS, Speaker Identification, Voice biometrics, Speech Analytics, etc.


Metadata is very important to build customer specific solutions; it serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of speech recognition models.

Our dataset provides comprehensive metadata for each participant. This metadata includes the participant's age, gender, country, state, and dialect. Furthermore, additional metadata, such as recording device details and the topic of the recording, can be helpful.


Human transcription is very important to build accurate speech recognition models. We provide a manual verbatim transcription in JSON format. The transcriptions capture speaker-wise transcription with time-coded segmentation along with non-speech labels and tags.

Speaker wise segmentation and tagging of non speech parts can be helpful in speaker identification and speech analysis.

Text Data Solution

Our text data solution include human to human chats and can be used to develop domain specific chatbots, keyword extraction, sentiment analysis, text prediction, etc.

A few points to consider;

1Chats consist of language-specific words and phrases and follow the native way of talking.
2Chats are specific to use case specific diverse topics.
3It contains various attributes, like people's names, addresses, contact information, email address, time, date, local currency, telephone numbers, local slang, etc., in various formats to make the text data unbiased.
4Our chats also use terminologies specific to the use case used in a given demography.
5As with speech, we provide text data in more than 50 languages.

Use case specific terminologies, different styles, and diverse topics are the backbone of any quality training data. So, if you are developing, consider visiting our website for more information.

Banking Training Data Procurement

Many AI developers are struggling to get quality data for their use cases, and some of them are also facing challenges in defining their data requirements. Some have time constraints, and others have diversity issues.

What type of data you need, diverse data, time constraints, etc. are making training data procurement difficult for many, but with our solutions, you can get your data ready with a proper understanding of what our data includes in terms of content, domain, and diversity.

We are making training data procurement easy with our 2 way solutions;

OTS Training Data for Banking

It all starts with defining your data needs. Our team of data contributors and domain experts makes it possible for us to build specific OTS datasets. Off-the-shelf (OTS) datasets mean they are ready-to-use for your model training.

According to our team, a training dataset can be considered ready-to-use high quality OTS data if

“High-quality off-the-shelf datasets are characterized by their accuracy, relevance, completeness, and consistency, ensuring that they faithfully represent the real-world phenomena they encapsulate. It is up-to-date, well-documented, and adheres to standardized formats and units, making it reliable and easy to work with. Data integrity is maintained, and data security and privacy measures are in place, guaranteeing the trustworthiness and ethical handling of information.

Moreover, a high-quality dataset should be representative of the target population, have clear licensing terms, and be readily accessible. In sum, a top-tier off-the-shelf dataset serves as a dependable foundation for data-driven applications, offering the necessary depth and breadth to support accurate analysis, research, or model development while adhering to ethical and legal considerations.”

As our team says, we execute these collections with the utmost care and proper understanding of the domain. From informed consent to the relevance of the data, we covered everything.

You can get these datasets easily and start your model POC process, fine tune or test your model. These datasets will give you an edge and speed up your training procedure. To get these datasets, get in touch with our team and explore more than 2000 ready-to-deploy training datasets.

Follow these simple steps:

1Explore our datastore, choose your required data, check samples, and get in touch with our team.
2Share your requirements with us.
3Discuss samples and data license.
4Start training your model.

If you didn’t find any OTS data helpful for your use case, then let's discuss another way to get the data ready.

Data Vendor for Custom Training Data Collection

We can be your data partner to fulfill all your data needs. Our expertise lies in collecting custom datasets specific to your needs and evaluating model outputs with humans in the loop.

Our process is very simple, and it starts with understanding your data needs. A good data partner first understands your needs and then defines the guidelines. After preparing guidelines, we train our team and get the desired samples for your approval.

Once you find the sample useful and approve it, scale the entire collection while maintaining the quality and diversity of the data. We offer batch wise delivery to make sure you have a hand on quality every time.

You will get multilingual, multiformat, and multidomain support throughout the collection.

Final Thoughts

Either you are working in the banking domain to improve customer experiences or in any other domain, high quality relevance training data is very important. But when it comes to training a model with data, time and budget also matter.

Sometimes you will get OTS data that may be suitable to your needs but may not have enough diversity, but it can be helpful to start. Explore all the resources available within your reach. Don’t hesitate to go for a custom collection if OTS data is not worth enough.

Considering a partner like us can be helpful in building unbiased and customer specific AI models. Our crowd, custom data, ready-to-deploy datasets, and end-to-end eco-system to prepared datasets are the fuel to revolutionize AI development across industries.