Category: Financial Services

Lumi is a leading Australian fintech lender empowering small businesses with fast, flexible, and transparent funding solutions. They use real-time data and machine learning (ML) to offer customized loans that fuel sustainable growth and solve the challenges of accessing capital. This post explores how Lumi uses HAQM SageMaker AI to meet this goal, enhance their transaction processing and classification capabilities, and ultimately grow their business by providing faster processing of loan applications, more accurate credit decisions, and improved customer experience.

In this post, we demonstrate how Octus migrated its flagship product, CreditAI, to HAQM Bedrock, transforming how investment professionals access and analyze credit intelligence. We walk through the journey Octus took from managing multiple cloud providers and costly GPU instances to implementing a streamlined, cost-effective solution using AWS services including HAQM Bedrock, AWS Fargate, and HAQM OpenSearch Service.

In this post, we share how we modernized Rocket Companies’ data science solution on AWS to increase the speed to delivery from eight weeks to under one hour, improve operational stability and support by reducing incident tickets by over 99% in 18 months, power 10 million automated data science and AI decisions made daily, and provide a seamless data science development experience.

The mission of Rich Data Co (RDC) is to broaden access to sustainable credit globally. Its software-as-a-service (SaaS) solution empowers leading banks and lenders with deep customer insights and AI-driven decision-making capabilities. In this post, we discuss how RDC uses generative AI on HAQM Bedrock to build these assistants and accelerate its overall mission of democratizing access to sustainable credit.

In this post, we discuss how FMs can reliably automate the classification of insurance service emails through prompt engineering. When formulating the problem as a classification task, an FM can perform well enough for production environments, while maintaining extensibility into other tasks and getting up and running quickly. All experiments were conducted using Anthropic’s Claude models on HAQM Bedrock.

In this blog post, we explore a client services agent assistant application developed by the London Stock Exchange Group (LSEG) using HAQM Q Business. We will discuss how HAQM Q Business saved time in generating answers, including summarizing documents, retrieving answers to complex Member enquiries, and combining information from different data sources (while providing in-text citations to the data sources used for each answer).

In this post, we explore Clearwater Analytics’ foray into generative AI, how they’ve architected their solution with HAQM SageMaker, and dive deep into how Clearwater Analytics is using LLMs to take advantage of more than 18 years of experience within the investment management domain while optimizing model cost and performance.

n this post, we create a virtual analyst that can answer natural language queries of stocks matching certain technical indicator criteria using HAQM Bedrock Agents.

In this post, we demonstrate how to deploy a generative AI application that can accelerate your financial statement analysis on AWS.

In this post, we present a solution that harnesses the power of generative AI to streamline the user onboarding process for financial services through a digital assistant.