Fintech Banks On AI To Deliver.

AI and ML are transforming the way modern financial institutions deliver value - to their customers, employees, and others in the ecosystem. Label Studio supports data labeling needs which is the foundation for AI/ML applications.

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Use case: Institutional economic perspectives

Applications that can forecast short and long term investment trends for financial institutions can be architected by ingesting market data and fiscal policies. Use Label Studio to aid in data labeling and take advantage of its integrated machine learning capabilities to build the right models from data about fiscal and monetary policies along with streams from the market. 

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Use case: Buying and Market trading

Leverage AI and ML to aid with trading in real-time and split-second decision making. Using Label Studio, you can find novel ways to generate semantic and rich datasets. Structured data such as spreadsheets, databases, tables, charts, and time-series information can be ingested along with unstructured ones like SEC filings, earnings calls, and social media to form a cohesive model to help in predictive analytics. 

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AI frontrunners in financial services embed AI in strategic plans in an organization wide implementation plan. 

- Deloitte Insights


Use case: Conversational banking

Give customers an omni-channel personal financial management experience powered by AI. Usher in the ease of single-window systems into the digital world. Use Label Studio to process data about user behaviour, audio recordings, chat transcripts, and identify keypoints in banking interfaces. In addition, tailor self-serve options based on customer preferences, and provide predictive recommendations based on interactions and market information. 

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Use case: Assessing credit risk

Build applications that rely on artificial intelligence and machine learning to help assess borrowers who provide minimum to zero credit information or history. Take advantage of the many labeling and annotation capabilities of Label Studio to help clean available data points. Use this clean data to easily build models to predict risk and quantify assessments that other underwriting systems cannot match.

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