Executive Summary
The Banking, Financial Services, and Insurance (BFSI) sector, along with its integral legal and risk management functions, is navigating an increasingly complex data landscape. Beyond traditional documents, critical information now resides in diverse multimodal formats: images of damaged property, video recordings of incidents, audio logs of customer interactions, intricate medical bills detailing treatments, and documented medical procedures. Effective BFSI data curation—the process of extracting, structuring, and correlating these rich but chaotic multimodal datasets—is paramount for accurate risk assessment, efficient claims processing, regulatory compliance, fraud detection, and enhanced customer service.
A leading insurance company was struggling with manual processing of complex claims involving photos, videos, audio recordings, and medical bills, resulting in delayed settlements and inconsistent risk assessments. This case study explores how Orbifold AI’s multimodal data curation platform helped transform their chaotic data streams into structured intelligent document processing for the BFSI sector.
Key Results:
- 50–95% reduction in manual data extraction and review time for complex insurance claims.
- Faster claim settlements and improved customer satisfaction (higher NPS scores).
- Reduced KYC/Customer onboarding times with stronger fraud prevention and full KYC/AML compliance.
- Enhanced fraud detection by correlating multimodal evidence across documents, images, video, and audio.
- Significant reduction in manual legal document review time, accelerating case preparation and e-discovery.
About the Client
Our client is an insurance company processing over millions claims annually across auto, property and health insurance lines. With operations in multiple states, they needed advanced Financial Services Technology to handle the increasing complexity and volume of multimodal claims data while staying compliant and processing fast.
The Challenge: Modern BFSI Operations Require Complex Data Processing
Modern BFSI digital transformation must address data processing challenges that traditional systems can’t handle:
1. Multiple and Complex Multimodal Data Sources
- Insurance Claims Processing: Modern insurance claims extend beyond forms, requiring BFSI data curation of multimodal evidence: photos of vehicle or property damage, dashcam/CCTV footage, audio witness statements, detailed medical bills, and documented medical procedures.
- Banking & Financial Services (KYC & Fraud Prevention): KYC verification and onboarding require processing ID document images, live video calls, and biometric data. Fraud investigations may involve analyzing transaction patterns alongside security footage or recorded calls.
- Legal & Risk Management: Legal teams depend on BFSI data curation to process scanned documents, photographic evidence, video depositions, audio recordings, and expert reports. Structured analysis of these diverse sources supports stronger legal cases and regulatory compliance.
2. Information Extraction from Unstructured Multimodal Data
- Limited Value from Basic OCR: In BFSI data curation, simply running OCR on documents falls short. True understanding requires extracting key entities, events, and relationships from images (e.g., damage severity, vehicle make/model), audio (e.g., sentiment analysis, fraud indicators, statement verification), and video (e.g., accident sequence, behavioral cues).
- Complexity of Cross-Modal Correlation: Manually linking information across modalities—such as matching an injury description in a medical report with photographic evidence and recorded testimony—is time-consuming, error-prone, and difficult to scale without advanced AI-powered data curation.
3. Decision-Making Accuracy
- Inaccurate Data Extraction Risks: Errors in extracting information from medical bills can lead to incorrect claims payouts, regulatory non-compliance, and financial losses. Misinterpreting visual evidence in fraud cases can also cause significant legal and monetary repercussions.
- Lack of Fine-Grained Detail: Missing specifics, such as the exact nature of a medical procedure or precise damage points on a vehicle, limits accurate risk assessment and undermines confident decision-making.
4. Scalability and Processing Speed Bottlenecks
- High-Volume Data Bottlenecks: The massive volume of multimodal data in claims processing and large-scale due diligence makes manual review slow, expensive, and prone to backlogs—delaying customer resolutions, hindering regulatory reporting, and reducing responsiveness to emerging risks.
5. Consistency and Compliance
- Inconsistent Data Interpretation: Ensuring uniform application of business rules across vast, diverse datasets and multiple reviewers is difficult, often resulting in compliance gaps, operational inefficiencies, and increased risk exposure.
The Impact: These challenges slowed claim processing, hindered accurate risk assessment, and prevented the company from meeting operational efficiency goals while maintaining compliance.
The Solution: Orbifold AI’s Multimodal Data Curation Platform for BFSI
Orbifold AI delivers an advanced multimodal data curation solution that ingests, interprets, structures, and correlates complex multimodal data at scale for the BFSI, legal, and risk management sectors. By leveraging cutting-edge AI technologies, it transforms raw, diverse datasets into a unified, high-fidelity intelligence layer that is actionable, accurate, and business-ready.
1. Multimodal Data Ingestion & Preprocessing
- Format Support: PDFs, images (JPEG, PNG, HEIC), videos (MP4, AVI), audio files (WAV, MP3)
- Quality Enhancement: Image and video enhancement (denoising, stabilization, super-resolution) for analysis
- Audio Processing: Professional transcription with speaker diarization and background noise removal
2. Deep Multimodal Information Extraction
- Visual Analysis: Advanced object detection to identify vehicles, property damage, and key items in accident scenes; damage severity assessment from images and videos; scene understanding; compliant facial recognition for verification; and anomaly detection to flag irregularities.
- Textual Analysis: High-accuracy OCR for documents and embedded text in images/videos; Natural Language Processing (NLP) for entity and relationship extraction (names, dates, policy numbers, medical terms, legal clauses); sentiment analysis; and summarization of text, transcripts, and reports.
- Audio Analysis: Accurate transcription, keyword spotting, and sentiment detection from customer calls or recorded statements, plus compliant voice biometrics for verification.
- Medical Data Extraction: Specialized parsing of medical bills, including CPT/ICD codes, service descriptions, provider details, and charges, along with identification of documented procedures, treatment timelines, and their correlation to claimed injuries.
3. Cross-Modal Data Correlation & Knowledge Graph
- Cross-Modal Intelligence: Seamlessly link information extracted from text, images, videos, and medical records—for example, matching an accident description in a report with photographic/video evidence and related medical billing details.
- Dynamic Knowledge Graphs: Create an evolving knowledge graph mapping entities (persons, vehicles, properties, policies, claims, medical procedures) and their relationships, as verified across all ingested multimodal data.
4. Data Validation, Enrichment, Structuring
- Automated Data Validation: Verify extracted information against business rules, internal databases, and trusted external sources to ensure accuracy and consistency.
- Contextual Data Enrichment: Augment records with relevant context, such as weather conditions during an incident, market valuations for damaged property, or standard medical treatment protocols.
- Structured Output for BFSI Systems: Deliver clean, highly structured data (JSON, XML, or database-ready formats) optimized for BFSI platforms, analytics tools, and AI model training.
5. Human-in-the-Loop Continuous Learning
- Human-in-the-Loop Review: Enable expert review and correction of AI-extracted data, with feedback loops that continuously enhance model accuracy, reliability, and robustness.
Implementation & Results: BFSI Operational Transformation
1. Insurance – Faster, More Accurate Claims Processing
Scenario: An insurer processes complex auto accident claims involving police reports (PDF), photos of vehicle/property damage, dashcam footage, and detailed medical bills.
With Orbifold AI: All data is ingested, analyzed, and cross-validated—extracting key details, assessing damage severity, and correlating evidence across documents, images, videos, and medical data.
Impact:
- 50–95% reduction in manual data extraction and review time per claim.
- Faster settlement times, boosting customer satisfaction and NPS scores.
- More accurate damage and injury assessments for better initial reserving.
- Enhanced fraud detection by correlating multimodal evidence.
2. Banking & Financial Services – Streamlined KYC and Fraud Prevention
Scenario: A bank verifies customer identity using ID documents, live photos, and short video interactions.
With Orbifold AI: The platform extracts data from IDs, matches facial images, and analyzes liveness cues from video to deliver comprehensive KYC verification.
Impact:
- Reduced onboarding time and manual review effort.
- Improved fraud prevention during customer acquisition through cross-modal ID verification.
- Enhanced compliance with KYC/AML regulations via automated, consistent checks.
3. Legal & Risk Management – Efficient Case Review and E-Discovery
Scenario: A legal firm handles a personal injury case with medical records, accident scene photos, video depositions, and expert witness reports.
With Orbifold AI: All case materials are processed, entities and timelines extracted, and relevant clauses identified for rapid case preparation.
Impact:
- Drastic reduction in manual document review time.
- Faster preparation and early identification of key evidence.
- Consistent analysis across large volumes of multimodal case files.
4. Cross-Sector Risk & Compliance – Proactive Oversight
Scenario: A BFSI organization monitors transactions, customer communications, and market data to detect fraud or non-compliance.
With Orbifold AI: The platform structures and analyzes diverse data to flag suspicious patterns early.
Impact:
- Early detection of patterns indicating financial crime, operational risks, or non-compliance.
Structured intelligence for proactive intervention and improved regulatory reporting.
Conclusion
The future of the BFSI sector—along with its legal and risk management functions—will be shaped by its ability to unlock intelligence from an ever-expanding universe of multimodal data. Manual processes can no longer deliver the speed, accuracy, and depth of insight required to stay competitive.
By precisely extracting, structuring, and correlating information from documents, images, videos, and audio, organizations can transform complex data into a strategic asset. This capability enables the automation of core processes, sharper decision-making, more effective risk management, and enhanced customer experiences—laying the foundation for the next generation of intelligent financial, insurance, and legal services.
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To understand how Orbifold AI’s multimodal data curation can transform your organization's data challenges into opportunities, visit www.orbifold.ai or contact us for a consultation at solutions@orbifold.ai.
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