From Days to Minutes: 95% Faster Turn-Around with AI-Powered Financial Document Intelligence
For a Global Financial Technology Platform.
“Staying ahead in a competitive financial technology market depends on turning mountains of documents into trusted insights, faster than anyone else.”
– A financial technology leader seeking to scale analyst intelligence with AI.
When a global financial technology platform set out to automate how its analysts summarize financial documents, an RAG-based GenAI program delivered measurable results:
95%
Faster turn-around, with summaries now delivered in minutes
~$XXM
Annual cost savings through reduced analyst effort
5x Increase
Analysts now work across 200+ data sources with far less manual effort
Trusted, Grounded Summaries
Accuracy and relevance improved through RAG and hallucination detection
Business Problem
The company processed large volumes of complex financial documents but relied heavily on people to make sense of them.
Analysts spent significant manual effort generating financial summaries, and the cost and time involved in processing large, complex documents kept climbing. At the same time, AI-powered summarization platforms were raising the competitive bar. Because the company lacked a scalable, accurate and automated document intelligence capability, turn-around times stayed slow and analyst capacity stayed stretched, putting its market position at risk.
The organization needed a reliable way to automate summarization across formats, keep outputs accurate and trustworthy, and free analysts to focus on higher-value work.
Customer Challenge
Financial documents arrive in many formats, often as large and intricate PDFs, and every one of them needed analyst attention. Without automation, summaries depended on individual effort, which made them slow, costly and hard to scale as document volumes grew. Every delay risked giving competitors with AI-powered platforms the edge.
The organization required one solution that could automate the summarization of financial documents across multiple formats. It had to ensure high accuracy and reduce hallucinations in generated outputs, and it had to be built as a scalable GenAI solution on open-source technologies. It also needed to generate context-aware insights that financial analysts could act on with confidence.
Movate Solution
Movate built a RAG-based document summarization system. OCR and custom chunking handle large PDFs, embeddings are generated, and ANN-based semantic retrieval is enabled so the most relevant content surfaces quickly.
LLMs then produce grounded summaries, supported by multi-agent workflows for processing, retrieval and evaluation. Hallucination detection and benchmarking frameworks are built in, which gives analysts confidence in every output. Work that once took manual effort now takes minutes, with a 95% faster turn-around.
Movate Applied AI Differentiator
Movate combined deep GenAI expertise with a rigorous focus on accuracy to turn heavy, manual document analysis into a scalable, trusted intelligence engine.
With RAG and hallucination detection in place, the company has achieved a 95% faster turn-around, a 5x increase in analyst productivity across 200+ data sources, and significant annual cost savings. Data-grounded confidence has replaced manual effort, so analysts can spend their time on insight rather than summarization, and the platform can process large volumes of financial documents on scale.
Get in touch with us
"*" indicates required fields