Improve productivity in banking
Financial services such as banking institutions nowadays face a continuous and increasing demand to clean and structure its data records – from emails, receipts, customer registrations, loan applications, to contract documents – which exist as unstructured data: images, PDFs, texts, and even physical papers for machine learning automation. To perform this machine learning model at scale, building clean and labelled training datasets out of these unstructured data plays a vital role.
Kili is trusted to become their data annotation tool partner
Main banking AI use cases
Customer email processing
Kili Technology has enabled many banking institutions, both French and global, to save cost and speed up their customer email processing.This includes accurately classifying email attachments, understanding customer sentiment, and delivering relevant response accurately.
Customer service chatbot
Kili Technology has facilitated financial servcie clients to increase customer service engagement at scale thanks to advanced chatbot development. Identifying and analyzing customer questions, complains, and even transaction requests through text and voice data has proved to increase banking customers’ satisfaction and loyalty.
Banking contract processing
Kili has helped the streamlining of banking contract processing to become much faster and cheaper. Entity recognition and linking are done to quickly and accurately extract important clauses and contract terms.
We solve banking AI challenges
Kili Technology makes text and image annotation fast and simple to improve your banking operations. Import your banking records in bulk: emails, contracts, customer registration documents, loan applications, due diligence records, and more. Classify all your documents, identify sentences, clauses, and statements within contracts, and extract information from your scanned compliance records. Build your training datasets with highly customizable interfaces that allow you to combine tasks to improve productivity.
However, doing a manual data annotation can be expensive, laborious, and time-consuming. In addition, banks need to put an extra-detailed attention to on the data annotation quality, as missing details on customer records when assessing loan applications or compliance due diligence can have huge regulatory consequence.
This is where data annotation tool such as Kili Technology comes in. The powerful NLP and computer vision features along with customizable interface to perform text and image labelling such as OCR, chatbot annotation, document classification, and entity recognition will simplify the process while enhancing the quality of building training dataset for banking machine learning applications.
Create datasets from data annotation
Departments in banking institutions around the world are looking to use artificial intelligence models to automate tedious tasks such as contract processing, customer emailing and communication, Know-Your-Customer (KYC) process, among others. However, creating and training these models requires access to large amounts of annotated data of relevant texts, images, and audio.
It is not a big problem to find certain datasets. For instance, to train customer service chatbot you can search for “chatbot datasets” in your favorite search engine.
However, in order for a model to be able to make accurate predictions, it must be trained on a large amount of high-quality data that is specialized in the problem you want to address.
More specifically, as banking institutions deal with highly sensitive data, to perform on a real use case, you will have no choice but to collect data from your database and label it. Be aware that labelling can be expensive and of poor quality. That’s where Kili Technology comes in.
What makes Kili Technology different?
Kili Technology delivers a stellar performance to annotate text, PDF, image, and OCR. We offer specialized interfaces for all annotation tasks related to document classification, entity recognition to identify specific phrases in contracts and loan application files, relations extraction, image transcription for your banking records, and more.
Kili Technology’s state of the art quality management system allow an intensive collaboration and a rigorous review throughout the life of the project to ensure clean, high-quality medical imaging training datasets.
At Kili Technology, you can annotate data wherever you want with whoever you like. On premise or in SaaS, with your internal annotators or with our labelling workforce, remotely or in your premises, we adapt to your constraints!
Data annotation can be expensive. By allowing the use of online learning, active learning, weakly supervised learning or data augmentation, Kili Technology allows you to drastically reduce the cost of data annotation!
Kili Technology has access to a unique network of professionals around the world who are able to accurately translate, transcribe, and annotate financial data, so we can quickly create large, custom training datasets for use cases in banking institutions.
Training data interfaces for banking
Document classification for banking files
Add structure to all your banking records to identify emails, receipts, contracts, or other financial records. Leverage our nested classification feature to define granularity. We support the image, text, PDF, and audio format for AI in financial institution.
Entity Extraction for legal contracts
Add structure and semantic information to unstructured text at the document and word level. Take advantage of our weakly supervised learning service to use business rules such as regular expressions and dictionaries to annotate massively before human intervention.
OCR for compliance due diligence records
Crop parts of the text while saving the text to construct training data. Correct even the most subtle input errors, as for sensitive financial data, since even the smallest errors cannot be tolerated.
A last but not least, create your own interfaces for your specific tasks with Kili’s interface builder!
Ready to simplify labelling in your company?
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