Financial Services

CUSTOMER STORY | ANA Seguros turns insurance data into faster decisions with AI/BI and Genie

Financial data agents are going to play a vital role in modern banking systems.

submitted by /u/ConstantNo2668 [link] [comments]

Property & Casualty Insurance Lakehouse

Community BrickTalk | Real-Time Data & AI: Tripwise Demo

Hey r/Databricks ! Join us for community BrickTalk on Thursday, September 24 , focusing on real-time data streaming, AI agents, and governance using Databricks. BrickTalks is a community event series where Databricks experts share real-world use cases, demos, and practical insights for building with Data and AI, giving customers a direct line to the people behind the products. In this session, we'll walk through a live demonstration of the Tripwise Demo , featuring: Sub-Second Transactions & Streaming: Device registration into Lakebase with sub-second reads/writes, plus telemetry streaming via Zerobus through a governed Medallion architecture. AI-Generated Offers & Pricing: Generating real-time agent offers using Foundation Model APIs and scoring behavioral data for usage-based renewal pricing. Natural Language Analytics: Enabling underwriters, product managers, and marketing teams to query governed insurance data in seconds using AI/BI Dashboards and Genie. Unified Governance: Managing safety, compliance, and control end-to-end with Unity Catalog. This is a great chance to see real-world architecture in action and ask questions directly to Databricks experts. When: Thursday, September 24 9:00 AM PT 12:00 PM ET 5:00 PM BST (London) 9:30 PM IST Register here and save your spot submitted by /u/Subject_Ant1789 [link] [comments]

Insurance Intelligence Copilot – Powered by Databricks Genie

Launch HN: Parsewise (YC P25) – Reason Across Documents with an API

Hi all, it’s Greg and Max, founders of Parsewise here (https://www.parsewise.ai/api). Parsewise transforms a bucket of unstructured data into schema compliant data, retaining lineage for values resolved across documents. Imagine giving Claude a bunch of files and asking for a CSV or JSON output. If you have tried this, you know both the system limitations (number of files, type of inputs, cost, latency) but also the human-facing challenge of having no way to validate the results quickly. We solve both. We help tech teams simplify their unstructured data ETL, and loop in business experts for the definitions and for instant validation. Here is a video with a few use cases: https://www.youtube.com/watch?v=dbRllnnh47w Parsewise in the words of someone coming to us: ”I need to extract information from insurance policy PDFs, phone calls that have been transcribed, emails, etc. I am NOT looking for something that would just extract data point by data point, page by page into a structured well-defined schema but more something more agentic that can understand that information might be across documents and that it should reason over what to extract.” We started the company based on a decade of experience (and pain) in complex data transformation and data analysis / synthesis. Greg was building both classical ETL and implemented AI workflows at Palantir. At Bain, Max did highly complex data analysis in the financial sector, similar to many of our customers. Parsewise works by taking in a bucket of data (think hundreds or thousands of pdfs, excels etc.), and outputting schema compliant data where every single value is traceable down to word level citations across multiple documents in the bucket. We provide API customers with ways to show the lineage in their own applications, or they can use our platform for internal operations. At the core of the data processing we have self-improving agent definitions. They define the acceptable sources, the logic for resolving or combining values, and the rule for highlighting uncertainty to the end user. The underlying tech is model and cloud agnostic and can be deployed in private networks. We have seen the best results with Gemini models for visual reasoning, achieving SOTA (beating Claude Fable) on the strongest grounded reasoning benchmark we have found (Databricks OfficeQA). Notably, we focused more on the “human harness” rather than the model harness, leaning into the actual friction we saw in uptake, which is around verifiability. That means optimizing the time and clicks required to trust the outcomes. We use vLLMs for parsing, and then we use small models for efficient large scale exhaustive search. Unlike RAG, we do not sample; instead, we exhaustively find all relevant values for a given query. We use larger models for decision making around resolutions and flagging inconsistencies to users. This exhaustiveness and explicit value sourcing is unique to our platform, and it goes beyond the first step of data parsing that many existing providers cover. We would love to welcome builders and tinkerers to try Parsewise on your complex document challenges. We have a ton of ideas on how we can expand the product and make it better, but would appreciate feedback and ideas from the community! --- top comments --- [whinvik] Document parsing is top of my mind lately because in some of the areas we work on the bottleneck is starting to become being able to query documents the same way one queries an api. I keep thinking the most obvious analogue is we need some way to represent documents the same way we can represent structured data in parquet. Parquet allows easy range bases queries and there is so much tooling built around Arrow. But for documents I keep hitting a wall to figure out what the right abstractions are. Parquet allows filterable metadata. But what such metadata is there for documents. Then there is the arbitrrariness of chunking, vectorization. If we could just do this in a […truncated]

How Databricks Genie democratizes data access in financial services

Agent Bricks in Action: Automating Insurance Underwriting with a Supervisor Agent-Led Architecture

Have a phone screening tomorrow with the recruiter for the RSA position. Anything I should know?

I applied to the RSA position within Financial Services and Public Sector (currently holding a secret clearance) and not sure which department the screening is for. As it’s just a screening is there anything I need to do to prep? I’m currently a senior consultant with the Big4 in the public sector and I’ve used Databricks in the past and enjoyed the tool and would love to get immersed in the tool. Databricks is on my list of dreams companies to work for. If I get past the screening I’ll use the community edition over the weekend to get refreshed with the platform.