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In this solution, we offer a novel approach to sustainable finance by combining NLP techniques and news analytics to extract key strategic ESG initiatives and learn companies' commitments to corporate responsibility
Perform fine-grained forecasting at the store-item level in an efficient manner, leveraging the distributed computational power of the Databricks Data Intelligence Platform.
Use Databricks to improve the Claims Management process for faster claims settlement, lower claims processing costs and quicker identification of possible fraud
Translating text attributes (like name, address, phone number) into quantifiable numerical representations Training ML models to determine if these numerical labels form a match Scoring the confidence of each match
Ingest sample retail data, build visualizations to explore past purchase behavior and use machine learning to predict the likelihood of future purchases
Shows how banks can modernize their risk management practices by back-testing, aggregating and scaling simulations by using a unified approach to data analytics with the Lakehouse.
In this solution accelerator, we demonstrate how generative AI, retrieval augmented generation (RAG) and multi stage reasoning can be used to better navigate through the complexities of regulatory filings, bringing more transparency for companies to disclose their societal and environmental impacts.
This repo demonstrates a bank’s transaction clearing process, utilizing generative AI for real-time fraud detection and AML compliance, enhancing the detection of evolving fraudulent techniques.
Preempt fraud with rule-based patterns and select ML algorithms for reliable fraud detection. Use anomaly detection and fraud prediction to respond to bad actors rapidly.
This series of notebooks shows how the Lakehouse for Financial Services enables banks, open banking aggregators and payment processors to address the challenge of merchant classification