High-Quality Training Data for Finance AI Models

Using data annotation expertise to deliver AI-enabled applications and predictive analytics for banking and finance, driving efficiency, lowering costs, and ensuring financial sustainability.

Enabling AI in Financial Services with Data Annotation

By integrating our data annotation and NLP expertise into banking and financial services, we enhance productivity and enable AI-powered predictive analytics to support fair and inclusive credit and risk management.

Our Financial Data Annotation Process Includes

Captioning, Transcription, and Translation

With perception models, crop detection can be simplified. By using 2D bounding boxes, high-quality training data can be generated for deploying AI to detect crops from weeds and eliminate the grass.

Data Extraction

Keypoints can help determine the shape and size of an object. Keypoint annotation allows AI to identify fruit and vegetable characteristics, like their shapes and sizes, i.e., indicatives of fructification and ripening.

Natural Language Processing for Sentiment Analysis

Splines are used in polyline annotations to classify and characterize objects. The use of polyline annotation techniques could lead to building AI for detecting diseases, analyzing irrigation patterns, and predicting harvest times on large farms.

Verification of Financial Data

The use of GIS & Geospatial annotation enables AI-integrated farm drones to monitor plant health & farm condition, track farming equipment, and increase the efficiency of agricultural production.

Text Data Annotation and Labeling for Chatbot Development

Digital KYC Verification involves trained content teams reviewing data from diverse sources to ensure accuracy, identify inconsistencies, and support companies in meeting compliance requirements while strengthening fraud detection.

AI Use Cases in Banking

By analyzing historical behaviors and predicting future outcomes, AI empowers banks to forecast market movements, detect lending trends, and personalize financial service recommendations. The following examples highlight AI’s impact on banking and finance:

Ensuring Compliance and Reducing Risks

Coral Mountain Data’s experts combine data annotation and language expertise to build AI-powered financial applications capable of analyzing transaction patterns, ensuring compliance, and delivering in-depth credit and debit analyses.

Forecasting with Predictive Analytics

By leveraging our data excellence, artificial intelligence can help banks reduce costs through automation, enhance customer service, and support smarter decision-making with predictive modeling and image recognition.

Automated Document Processing

Coral Mountain Data’s Natural Language Processing team helps you automate repetitive tasks—such as reporting and reconciliation—by extracting key information from documents including invoices, expenses, credit, shipping, and tax records.

Fund Management

The financial services team extracts, tracks, and verifies key fund details while enriching investment models by analyzing vast amounts of unstructured data, such as bank accounts and debit or credit card transactions.

Your Trusted Data Partner for AI in Banking

With well-structured and expertly annotated data, Coral Mountain Data supports enterprises looking to integrate AI into banking and financial operations. Our AI training data practice is grounded in a strong ethical framework, aligning with GDPR, CCPA, certification and compliance standards, as well as commitments to fair pay, diversity, and inclusion.