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By embedding unstructured-to-structured data translation capabilities, financial institutions unlock greater value and power automation.
The financial services industry generates and processes an astronomical volume of data daily. Complicating matters, much of this exists in unstructured formats: sources that resist automation and slow critical business processes.
Yet within this apparent chaos lies tremendous value waiting to be unlocked. Converting unstructured information into standardized, machine-readable formats, and intelligent parsing of data structured in old formats — have become vital for financial institutions seeking to automate workflows and stay compliant with emerging regulations.
Why ISO 20022 Migration Demands Structured Data
For decades, SWIFT MT messages have carried the world’s cross-border payments using rigid, text-based formats. These messages compact critical information, such as names, account details, and addresses, into strict character limits, prioritising brevity over structure. While effective for their time, MT messages resist automation and limit the potential for validation, analytics, and straight-through processing.
ISO 20022 represents a fundamental shift, introducing a rich, structured format designed for modern financial systems. As the CBPR+ and HVPS+ guidelines evolve, banks are under growing pressure to ensure that address data in financial messages is fully structured. The hybrid transition period is now underway, with the outright rejection of unstructured addresses expected in 2026. Institutions must act quickly to align with this new standard, or face message rejections and potential regulatory scrutiny.
This shift introduces a two-sided challenge:
To comply, banks must update onboarding portals, application forms, and payment interfaces — and just as critically, educate customers on why additional address fields are now required. These changes, while ultimately beneficial for data quality and processing efficiency, can be time-consuming and disruptive when implemented across large customer bases.
This is where AI-driven unstructured-to-structured data translation becomes invaluable. Advanced models can interpret free-text addresses – regardless of format, language, or regional conventions – and accurately map them to ISO 20022-compliant components (e.g., street name, city, postal code, and country). This translation enables:
By implementing intelligent translation capabilities, financial institutions can bridge the gap between legacy input and modern compliance standards, ensuring readiness for the 2026 deadline while maintaining customer experience and operational continuity.
Automating Invoice Processing Through Structured Data
Every day, financial institutions and corporate treasuries wrestle with millions of invoices arriving in an bewildering array of formats. Each format demands different handling, creating a patchwork of manual processes that drain resources and introduce errors.
Beyond the obvious inefficiencies, manual processing introduces systematic risks: misread amounts that trigger incorrect payments, missed due dates that damage supplier relationships, and transcription errors that require costly reconciliation.
Emerging solutions leverage a sophisticated combination of optical character recognition, natural language processing, and machine learning to extract payment-critical information automatically.
Bolted on to financial systems, unstructured-to-structured data translation allows information to be extracted in real-time and compared against vendor master files and ERP systems, catching discrepancies before they propagate through payment workflows.
Structuring KYC Data for Seamless Client Onboarding
Client onboarding remains one of the most persistent pain points in financial services. New customers submit identity documents in a wide range of formats, all requiring careful review to extract know-your-customer (KYC) data. Manual processing and verification can stretch timelines to weeks, frustrating clients and increasing the risk of drop-off during onboarding.
AI-driven document parsing automates this process, extracting KYC attributes like names, dates of birth, and document types from a global array of ID formats. Real-time validation cross-checks extracted data against official registries and internal systems, flagging issues instantly.
When integrated into onboarding portals and CRM systems, the result is a seamless experience where clients can onboard in minutes. Automated audit trails ensure compliance, while reduced friction significantly boosts conversion rates.
By embedding unstructured-to-structured data translation capabilities across internal systems, financial institutions unlock greater value from existing data and power automation, enabling faster onboarding, more reliable payments, and deeper analytics. Over time, this permits quicker regulatory response, operational scalability, and adaptability to evolving business needs.
The financial services industry generates and processes an astronomical volume of data daily. Complicating matters, much of this exists in unstructured formats: sources that resist automation and slow critical business processes.
Yet within this apparent chaos lies tremendous value waiting to be unlocked. Converting unstructured information into standardized, machine-readable formats, and intelligent parsing of data structured in old formats — have become vital for financial institutions seeking to automate workflows and stay compliant with emerging regulations.
Why ISO 20022 Migration Demands Structured Data
For decades, SWIFT MT messages have carried the world’s cross-border payments using rigid, text-based formats. These messages compact critical information, such as names, account details, and addresses, into strict character limits, prioritising brevity over structure. While effective for their time, MT messages resist automation and limit the potential for validation, analytics, and straight-through processing.
ISO 20022 represents a fundamental shift, introducing a rich, structured format designed for modern financial systems. As the CBPR+ and HVPS+ guidelines evolve, banks are under growing pressure to ensure that address data in financial messages is fully structured. The hybrid transition period is now underway, with the outright rejection of unstructured addresses expected in 2026. Institutions must act quickly to align with this new standard, or face message rejections and potential regulatory scrutiny.
This shift introduces a two-sided challenge:
To comply, banks must update onboarding portals, application forms, and payment interfaces — and just as critically, educate customers on why additional address fields are now required. These changes, while ultimately beneficial for data quality and processing efficiency, can be time-consuming and disruptive when implemented across large customer bases.
This is where AI-driven unstructured-to-structured data translation becomes invaluable. Advanced models can interpret free-text addresses – regardless of format, language, or regional conventions – and accurately map them to ISO 20022-compliant components (e.g., street name, city, postal code, and country). This translation enables:
By implementing intelligent translation capabilities, financial institutions can bridge the gap between legacy input and modern compliance standards, ensuring readiness for the 2026 deadline while maintaining customer experience and operational continuity.
Automating Invoice Processing Through Structured Data
Every day, financial institutions and corporate treasuries wrestle with millions of invoices arriving in an bewildering array of formats. Each format demands different handling, creating a patchwork of manual processes that drain resources and introduce errors.
Beyond the obvious inefficiencies, manual processing introduces systematic risks: misread amounts that trigger incorrect payments, missed due dates that damage supplier relationships, and transcription errors that require costly reconciliation.
Emerging solutions leverage a sophisticated combination of optical character recognition, natural language processing, and machine learning to extract payment-critical information automatically.
Bolted on to financial systems, unstructured-to-structured data translation allows information to be extracted in real-time and compared against vendor master files and ERP systems, catching discrepancies before they propagate through payment workflows.
Structuring KYC Data for Seamless Client Onboarding
Client onboarding remains one of the most persistent pain points in financial services. New customers submit identity documents in a wide range of formats, all requiring careful review to extract know-your-customer (KYC) data. Manual processing and verification can stretch timelines to weeks, frustrating clients and increasing the risk of drop-off during onboarding.
AI-driven document parsing automates this process, extracting KYC attributes like names, dates of birth, and document types from a global array of ID formats. Real-time validation cross-checks extracted data against official registries and internal systems, flagging issues instantly.
When integrated into onboarding portals and CRM systems, the result is a seamless experience where clients can onboard in minutes. Automated audit trails ensure compliance, while reduced friction significantly boosts conversion rates.
By embedding unstructured-to-structured data translation capabilities across internal systems, financial institutions unlock greater value from existing data and power automation, enabling faster onboarding, more reliable payments, and deeper analytics. Over time, this permits quicker regulatory response, operational scalability, and adaptability to evolving business needs.
