AI-Powered Data Structuring for Financial Enterprises

White paper

Company profile: Artificial Intelligence

At Andersen, a custom finance software development company, we help financial enterprises transform unstructured data into actionable insights through cutting-edge AI tools. As a vendor of AI engineering and AI consulting services, we design intelligent data pipelines that integrate options like DocETL for AI-powered document extraction and Promptify for semantic categorization – streamlining automation, ensuring compliance, and accelerating decision-making across finance and insurance.

We focus on organizations that:

• Manage large volumes of unstructured or semi-structured data – such as invoices, contracts, transcripts, or emails;
• Require scalable AI tools for data normalization, structuring, categorization, and extraction to streamline financial workflows;
• Aim to reduce manual data handling and improve operational efficiency via tools for AI-based financial data analysis and handling in general;
• Operate in regulated environments and need transparent, auditable data pipelines;
• Seek to modernize legacy data systems with modular and flexible architectures.

By combining AI-powered extraction with semantic classification, our solution empowers enterprises to unlock value from unstructured data and drive smarter, faster decisions.

Business challenge: the case for Artificial Intelligence

Financial institutions and global enterprises struggle to extract meaning from vast volumes of unstructured documents. Manual review processes are slow, error-prone, and costly. Data teams are overwhelmed with requests to structure and interpret PDFs, scanned documents, or raw transcript logs. Building an AI data pipeline in finance is an optimal course of action under these circumstances.

Core industry pains faced by finance teams:

• Manual data entry bottlenecks, especially in document-heavy processes like onboarding, due diligence, compliance, and transaction reconciliation;
• Siloed or unstructured data that impedes efficient analytics and reporting;
• Lack of semantic understanding, where structured extraction alone is insufficient without context-aware classification.

Typical indicators that trigger the AI-powered change

Companies ready for transformation often report:

• Long turnaround time for document analysis;
• Inability to automate data ingestion pipelines;
• Multiple internal teams replicating data-cleaning workflows;
• High compliance and reporting overhead;
• Fragmented tools and models with no unified orchestration layer.

Solution overview

The ever‑expanding availability of multimodal data spanning text logs, user profiles, transaction records, images and beyond demands a new paradigm for discovery and insight. Petabyte‑scale repositories of unlabeled, heterogeneous content defy traditional ETL and keyword‑based searches, making it virtually impossible to identify high‑level patterns or categorize information at scale.

At the same time, organizations must minimize both latency and cost as they orchestrate complex, LLM‑driven workflows across diverse formats. This convergence of massive volume, modality diversity and cost‑sensitivity calls for an indexing framework that can automatically understand and group raw data into meaningful buckets, while optimizing computing and API‑tier expenses from end to end.

Modules:

1. Data ingestion and pre-processing with DocETL

DocETL serves as a foundational layer, specifically designed to optimize complex data processing pipelines while addressing the inherent limitations of LLMs and enhancing overall accuracy. Its primary function is to extract diverse multimodal content and transform it into a structured format that is suitable for subsequent AI processing.

2. Semantic understanding and categorization with LLMs

Promptify serves as the semantic layer for multimodal data categorization, providing an interactive prompt engineering environment specifically designed for LLM-driven classification tasks. The application employs a suggestion engine that analyzes data patterns and automatically generates contextually relevant prompts for categorical indexing across diverse data formats.

AI data solution for financial services: architecture

This is what our proposed AI data solution for financial services is based on and how it works:

• The system employs a two-stage processing architecture where DocETL first extracts and summarizes data chunks from the big data store through its agentic pipeline, creating semantic abstractions of raw multimodal content;

• These summarizations are then fed into Promptify for categorical classification and indexing. DocETL's LLM-powered operators handle the heavy lifting of content extraction and initial summarization, while Promptify applies optimized classification prompts to transform these summaries into searchable categorical indexes.

What partners gain with an AI finance solution from Andersen

Operational efficiency and cost savings with AI

Automating document understanding across large-scale, multimodal sets of financial data drastically reduces time spent on manual processing, enabling users to reallocate resources and cut operational costs.

Better customer experiences with AI

With faster access to structured, meaningful data, finance teams can deliver more personalized services, faster resolutions, and smarter automation across customer-facing channels.

Faster time-to-revenue with AI

By accelerating onboarding, risk assessment, and compliance workflows, enterprises can shorten cycle times, launch products faster, and speed up decision-making across the organization.

Improved compliance and audit readiness with AI

Built-in traceability and explainability features ensure that every step of the pipeline is auditable – helping organizations meet evolving regulatory requirements with less overhead and lower risk.

Scalability without linear cost increase with AI

The modular, compute-efficient architecture enables enterprises to scale processing capacity without a proportional increase in infrastructure or API costs – a critical advantage in volatile market conditions.

Real-life customer success story

Research indexing platform for University of Cape Town

Challenge faced by the customer

The customer was a scientific research center at a university in South Africa. The decentralized research teams at the center were working on diverse vaccine development initiatives and struggled to consolidate knowledge from dispersed departments. Research papers, lab notes, and experimental results were siloed, poorly searchable, and lacked version control. They needed a platform to keep all information in one place, organize it, systematize it, and safely share relevant data with other departments.

Business value delivered by Andersen

Andersen designed a secure AI-powered semantic indexing and knowledge management platform, allowing researchers to safely share, trace, and discover relevant data within the organization without exposing sensitive IP or violating grant restrictions. Measurable outcomes of the proposed solution were:

• A 50% reduction in duplicated research efforts;
• Improved visibility of ongoing research projects by 3 times;
• Search-to-retrieval time cut from 15 minutes to 2 minutes;
• Full alignment with funders’ data traceability requirements.

Real-life customer success story

Data organization platform for FinTech

Challenge faced by the customer

A financial technology company, based in the United States, struggled with managing a vast amount of uncategorized and unsorted data generated from diverse sources. This data encompassed transaction records in text, financial charts and screenshots, and video-based client consultations. The lack of organization resulted in employees spending up to 40% of their time searching for critical financial data. Siloed information across teams led to delayed decision-making, increased risks of regulatory non-compliance, and missed opportunities in fraud detection due to inaccessible historical data.

Business value delivered by Andersen

Andersen developed a secure, intelligent semantic indexing, AI, and data management platform leveraging large language models (LLMs). The solution utilized advanced semantic understanding to categorize and label data across various formats, including text and visual content like screenshots, using natural language processing and computer vision techniques. Measurable outcomes include:

• 60% reduction in time spent searching for data;
• Enhanced cross-departmental collaboration with a 4-fold increase in data accessibility;
• Automated categorization reduced manual labelling efforts by 75%;
• Full compliance with financial regulatory standards, ensuring secure data sharing.

What comes next

Once data is extracted, structured, and semantically categorized, organizations face a new challenge: How to continuously improve AI models without centralizing sensitive information?

The next step in the transformation journey is the adoption of federated learning – a paradigm that allows enterprises to train Machine Learning models across distributed data sources while preserving privacy, data ownership, and compliance.