Global financial institutions face an escalating battle with fraud, as attackers use generative artificial intelligence (GenAI) and malware toolkits to productionize and weaponize threats. Juniper Research projects that global banking fraud losses will soar from an estimated $25 billion in 2025 to $58.3 billion in 2030. As a result, nearly all (87%) of global financial institutions have deployed AI-driven fraud detection systems, up from 72% in early 2024.
Leaders at a tier-1 financial institution (FI) in the Middle East identified an opportunity to modernize its fraud operations. Managing complex operations at scale, the organization faces high data volumes; stringent regulatory requirements; and the need to enable real-time, cross-functional decision-making across risk, compliance, customer service, and operations.
The client was experiencing ongoing challenges processing client account-related documents. Teams used manual, multi-step processes to address suspected fraud, which introduced delays, errors, and risks into mitigation efforts. Leaders wanted to automate fraud detection and response, ensuring strong governance to meet industry and regional requirements. Tredence partnered with the FI to develop a unified document intelligence platform that established enterprise-grade document processing and workflow routing. The document processing framework was fully auditable, scalable across the enterprise, and governed with high standards of accuracy and compliance.
Evolving Fraud Detection Processes to Match Business Needs
The key business challenges included:
- Manual processing of over 3.6 million documents, each requiring human verification and data extraction
- Fragmented workflows spread across multiple systems, resulting in operational inefficiencies and processing delays
- Limited fraud detection capabilities, increasing exposure to document authenticity risks and regulatory non-compliance
- A growing document volume (approximately 5% annual growth) requiring scalability without compromising accuracy or compliance
- Time-consuming, multi-step validation processes for completeness checks and regulatory compliance
Together, these challenges limited the FI’s ability to detect fraud early, respond consistently across regions, and scale operations without increasing risk or cost.
Developing a Fraud Detection System Built for Scale with Agentic AI
Tredence designed and implemented a comprehensive, AI-driven document processing platform to address these challenges end-to-end. With the client, we:
- Built an enterprise-grade, AI-powered document platform to establish a strong automation foundation
- Developed unified document intelligence capabilities, enabling automated document classification, data extraction, and intelligent workflow orchestration across multiple data sources
- Enabled advanced AI capabilities, including fraud detection, multi-language processing (Arabic and English), and automated compliance checks with real-time monitoring
- Designed a scalable, secure architecture aligned with enterprise compliance and governance standards
- Integrated seamlessly with both internal and external systems to create a unified system of record
- Delivered near real-time dashboards for exception handling, discrepancy analysis, and operational performance monitoring
Building a Multi-Agent Architecture on Databricks
Tredence proposed and implemented a multi-agent AI architecture leveraging a robust data foundation built on Databricks. We developed a detailed future-state solution flow, enabling autonomous agents to collaborate across document ingestion, validation, fraud detection, compliance checks, and workflow orchestration. With the new agentic AI system, the FI would be able to drive higher efficiency, accuracy, and governance across its business lines.
The solution extracted key attributes from documents—including invoice details, vendor information, and customer data—and orchestrated a set of specialized AI agents to enable end-to-end automated document processing. These agents collaborated to handle end-to-end automated workflow, including data ingestion, extraction, quality assurance, workflow automation, and monitoring, providing the FI with a scalable, auditable system.
The agents provided intelligent data extraction capabilities, including a:
Document ingestion agent
- Ingested prioritized, multi-format documents such as PDFs, images, and scanned forms
- Classified documents using AI-driven document type detection (e.g., invoices, vendor documents, project records, legal documents)
Data extraction agent
- Leveraged Azure Document Intelligence to capture contextual data from unstructured content, including text, tables, and checkboxes
- Used Databricks Agentbricks to identify and extract encoded elements such as QR codes and signatures
- Applied natural language processing (NLP) and GenAI large-language model (LLM)-based techniques for intelligent entity recognition and extraction, including names, monetary values, contract dates, and identification numbers
Validation and workflow automation agents include a:
Quality assurance agent
- Completeness verification: Automating validation against predefined document requirements
- Within-document consistency: Providing logical validation of related data points within a single document
- Cross-document validation: Performing consistency checks across multiple related documents
- Business rule engine: Leveraging configurable rules to enforce compliance and policy adherence
- AI/machine learning (ML)-based anomaly detection: Recognizing patterns to identify unusual, inconsistent, or potentially fraudulent data
Workflow automation agent
- Agentic AI-driven routing: Automating routing based on document type, processing stage, and detected exceptions
- Seamless integration: Ensuring interoperability with enterprise systems, including FBMS Unifier, Oracle Database, and the Vendor Portal, to ensure end-to-end data flow and process continuity
Monitoring and reporting capabilities:
- Near real-time dashboards: Providing visibility into document completeness, compliance status, processing exceptions, and operational metrics
Leveraging Databricks to Create a Foundation for Modern Fraud Operations
The agentic solution was built on a Databricks-based data foundation. Core Databricks capabilities—including Agentbricks for document processing, Databricks Vector Database, and Databricks Foundation Models—were used to develop the LLM-powered application using LangGraph and LangChain. The application was deployed as a secure Databricks endpoint and governed through Unity Catalog, ensuring enterprise-grade security, lineage, and access control.
The foundation ensured that the platform could scale across regions, while maintaining lineage, auditability, and regulatory control – critical requirements for Tier-1 financial institutions.
Rising to Meet the Next Wave of Fraud Risks and Threats
With its scalable data foundation, automated risk-detection capabilities, and real-time dashboards, the FI has evolved from reactive fraud detection to modern, automated fraud detection with embedded guardrails and governance. As a result, it can mitigate risks swiftly, protecting its business, customers, and brand while strengthening margins.
AUTHOR - FOLLOW
Maulik Divakar Dixit
Senior Director, Data Engineering, <br>Databricks Champion<br>Databricks MVP
