Gen AI, agentic systems, and data engineering for financial services.
Only 7% of insurers have scaled AI in claims. Here is what is holding the rest back, and how multi-agent systems on Databricks solve claims triage from FNOL to settlement.
Read more →64% of insurers cite document processing as their top AI priority. Here is how ai_parse_document and AI Functions on Databricks extract COPE data from messy, multi-format submissions.
Read more →Traditional rules engines overwhelm SIU teams with false positives. Here is how batch inference via ai_query and agentic investigation on Databricks build defensible evidence chains.
Read more →80% of banks cite compliance as their top challenge. Here is how ai_parse_document, AI Functions, and Agent Bricks on Databricks automate KYC from document verification to SAR generation.
Read more →Loan officers manually review income documents across branches with inconsistent standards. Here is how ai_parse_document, AI Functions, and Feature Store on Databricks create consistent, faster lending decisions.
Read more →Banks spend enormous manual effort on data lineage and reconciliation for BCBS 239. Here is how Unity Catalog lineage, AI Functions, and Agent Bricks on Databricks reduce manual reconciliation by 90%.
Read more →Analysts spend 60%+ of their time gathering data. Here is how AI Functions, Vector Search, and Agent Bricks on Databricks generate structured investment research briefs in seconds.
Read more →Manual trade monitoring cannot scale across instruments and venues. Here is how Spark Declarative Pipelines, batch inference via ai_query, and Agent Bricks enable real-time surveillance with 60% fewer false alerts.
Read more →Manual risk assessment cannot react to market events in real time. Here is how Feature Store, AI Functions, and Agent Bricks on Databricks enable automated rebalancing with real-time risk visibility.
Read more →Multi-step AI workflows are moving from pilot to production. Here is how Claude, OpenAI function calling, and the Agents SDK support use cases like underwriting and claims.
Read more →How retrieval-augmented generation and fine-tuning support policy Q&A, underwriting, and analyst workflows in regulated environments on Databricks.
Read more →Anthropic open-sourced MCP in November 2024. Here is how it helps connect internal systems and data sources to Claude and other AI applications in a standard way.
Read more →How firms are using Databricks Lakehouse, AI Gateway, and Lakeflow to run Gen AI use cases in wealth and capital markets with real platform capabilities.
Read more →Moving from a single use case to repeatable, governed Gen AI delivery: discovery, guardrails, evaluation, and the right team shape.
Read more →