FiggWealth collaborated with Smile IT Solutions to build a GenAI-powered, AWS-native platform designed to transform personalised financial decision-making. The solution delivers advanced analytics and intelligent document processing (IDP), enabling users to access personalized, real-time financial simulations and actionable AI-driven insights.
The Challenge
Modern financial decision-making has become increasingly complex, requiring individuals to process large volumes of data, simulate outcomes, and adapt quickly to changing conditions. Traditional financial tools are often static, slow to update, and disconnected from real-time market movements, offering limited personalization and actionable insights. Manual input of financial data, such as from bank statements, investment summaries and personal assets, further slows decision-making and increases the risk of relying on outdated information.
The FiggWealth platform was developed to address these challenges by leveraging advanced analytics to unify user assets, goals, and ambitions into personalized financial pathways. By combining real-time insights, interactive planning tools, and intelligent document processing, FiggWealth empowers users to make better decisions while reducing operational costs by an estimated 20–30% through automation and streamlined data management.
The Solution
To address the growing complexity of financial decision-making and deliver a seamless, personalised user experience, FiggWealth partnered with Smile IT Solutions to develop a fully serverless, GenAI-powered platform built on AWS.
At the core of the solution is Amazon Bedrock, which powers real-time financial simulations and personalised recommendations by leveraging advanced foundation models. This enables users to explore tailored “what-if” financial scenarios based on their assets, goals, and ambitions, providing insights that were traditionally difficult to access without manual forecasting or static tools.
To eliminate manual data input and accelerate onboarding, the platform integrates Amazon Textract, which automatically extracts structured financial information from uploaded documents such as bank statements and investment summaries. This extracted data is then securely stored and managed in Amazon Aurora Serverless, a scalable relational database that holds user profiles, structured financial data, and simulation outputs. The dynamic nature of Aurora Serverless ensures that the database scales automatically with usage patterns, optimising both performance and cost.
Document uploads, model orchestration, user input processing, and predictive simulations are all managed through AWS Lambda, enabling serverless execution of backend logic without the need for traditional infrastructure management. Real-time and historical financial data, AI-generated outputs, and archived reports are stored securely and reliably in Amazon S3, ensuring high durability and easy retrieval for analytics and visualization.
To further enhance personalization and user engagement, the platform integrates an Amazon Bedrock Agent, allowing users to interact with an AI-driven assistant through natural language conversations. By chatting with the Bedrock Agent, users can receive tailored portfolio analysis, financial recommendations, and actionable insights based on their real-time financial profiles and predictive models. This conversational capability provides an intuitive, hands-free way for users to access complex financial planning support, making advanced decision-making tools even more accessible and user-friendly.
The Results
- 65% reduction in time spent on manual financial data input, achieved through document automation powered by Amazon Textract.
- 70% of uploaded financial documents (including bank statements and investment summaries) successfully processed and integrated within two minutes, enhancing real-time decision-making capabilities.
- High success rate in user portfolio unification, with assets, goals, and ambitions seamlessly mapped together during the first login experience, significantly improving personalization and user engagement.
- Operational costs reduced by 30% through serverless infrastructure and automation.
Lessons Learned
- Integrating Amazon Textract streamlined financial onboarding by reducing manual data entry by 65%, significantly improving platform usability and accelerating user engagement. Additionally, achieving 70% real-time document processing validated the critical importance of fast data ingestion, enabling users to quickly access personalised insights and reinforcing the need for real-time infrastructure in financial planning.
- Achieving a 25% reduction in operational costs confirmed that serverless architecture was not only a technical choice but a strategic business decision. Early investment in AWS serverless services (Lambda, Aurora Serverless, S3, Amplify) ensured scalability, reliability, and significant cost savings without compromising performance.
- Leveraging predictive analytics powered by Amazon Bedrock allowed FiggWealth to automatically map and unify user assets, financial goals, and ambitions during the onboarding process. Instead of requiring manual input and categorization, the platform intelligently interpreted uploaded financial data and structured it into personalised financial pathways. This AI-driven approach significantly improved early user satisfaction, accelerated portfolio creation, and reduced user effort.
