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Better Address Data, Better Payments: Putting AI Performance Into Practice
The timeline for structured address adoption may have changed, but the underlying data-quality challenge has not.
Financial institutions continue to process payment instructions containing incomplete, inconsistent, or free-text address information. That matters beyond regulatory readiness. Address quality can affect how efficiently payment data moves through operational processes, how much manual intervention is required, and how effectively downstream systems can use party information.
As payment environments become more automated and AI-enabled, the quality and structure of the underlying data become increasingly important. This is where address intelligence can create value beyond compliance.
Address quality is an operational issue
Unstructured or inconsistent address information can create additional work before downstream systems can use it effectively. Better-structured party data can support:
more consistent payment processing;
reduced reliance on manual intervention;
stronger inputs for sanctions screening and fraud-prevention processes;
better use of ISO 20022 data;
stronger foundations for automation and AI-enabled workflows.
The objective should therefore not be limited to meeting a formatting requirement. It should be to make address information more structured, consistent, and usable across the payment lifecycle.
Measuring address intelligence performance
Catalyst Data Intelligence uses AI to transform unstructured address information into structured data that can be used within payment workflows. In evaluation of our more advanced models, Catalyst Data Intelligence delivered:
90.39% overall critical-field exact match
86.71% overall single-field exact match
8.06 ms average single-address inference time
These metrics provide different perspectives on model performance.
The exact-match measurements reflect whether predicted address fields correspond exactly with the expected values in the evaluation data. The inference-time measurement reflects the average time required by the evaluated model to process one address. Together, the results provide a view of both output quality and processing performance.
Model performance is only part of the equation
For financial institutions, adopting AI is not only a question of whether a model can generate the expected result. Institutions also need visibility into the confidence associated with that result and control over how different outputs are handled.
Catalyst Data Intelligence provides confidence scoring for address transformations and enrichment. This enables institutions to differentiate higher-confidence remediation from cases that may require additional review. Institutions can also evaluate the solution against their own data and payment corridors, allowing performance to be assessed within their specific operational context. This combination of measurable performance, confidence scoring, and institutional control is particularly important when AI becomes part of financial operations.
Designed to fit into existing payment workflows
Improving address data should not require institutions to replace their core payment environment. Catalyst Data Intelligence is designed to operate as an additional processing layer within existing workflows.
Address information can be passed to the solution through an API, structured by the model, and returned to the payment process for further processing. This approach allows institutions to introduce address intelligence while maintaining their existing payment architecture. Catalyst Data Intelligence can also operate within the institution's own infrastructure, helping organizations retain control over sensitive payment data.
Better data supports greater automation
As payment operations become more automated, the importance of reliable data increases. Payment engines, screening systems, analytics platforms, and AI applications all depend on the quality of the information they receive. More structured and consistent address data provides stronger inputs for these systems and creates a better foundation for automated processing and decision-making.
Structured address adoption should therefore be viewed as more than a compliance exercise. It is also an opportunity to improve the data foundation on which future payment operations will depend.
The webinar will explore how better address data can support payment operations beyond compliance, including straight-through processing, exception management, screening, fraud prevention, and automation. The session will also include a live demonstration of Catalyst Data Intelligence, part of the Catalyst Product Suite.
September 23, 2026 10:00 a.m. EDT | 4:00 p.m. CEST
Performance figures are based on evaluation of Catalyst Data Intelligence models. Results may vary depending on data, configuration, infrastructure, and deployment environment.
Financial institutions continue to process payment instructions containing incomplete, inconsistent, or free-text address information. That matters beyond regulatory readiness. Address quality can affect how efficiently payment data moves through operational processes, how much manual intervention is required, and how effectively downstream systems can use party information.
As payment environments become more automated and AI-enabled, the quality and structure of the underlying data become increasingly important. This is where address intelligence can create value beyond compliance.
Address quality is an operational issue
Unstructured or inconsistent address information can create additional work before downstream systems can use it effectively. Better-structured party data can support:
more consistent payment processing;
reduced reliance on manual intervention;
stronger inputs for sanctions screening and fraud-prevention processes;
better use of ISO 20022 data;
stronger foundations for automation and AI-enabled workflows.
The objective should therefore not be limited to meeting a formatting requirement. It should be to make address information more structured, consistent, and usable across the payment lifecycle.
Measuring address intelligence performance
Catalyst Data Intelligence uses AI to transform unstructured address information into structured data that can be used within payment workflows. In evaluation of our more advanced models, Catalyst Data Intelligence delivered:
90.39% overall critical-field exact match
86.71% overall single-field exact match
8.06 ms average single-address inference time
These metrics provide different perspectives on model performance.
The exact-match measurements reflect whether predicted address fields correspond exactly with the expected values in the evaluation data. The inference-time measurement reflects the average time required by the evaluated model to process one address. Together, the results provide a view of both output quality and processing performance.
Model performance is only part of the equation
For financial institutions, adopting AI is not only a question of whether a model can generate the expected result. Institutions also need visibility into the confidence associated with that result and control over how different outputs are handled.
Catalyst Data Intelligence provides confidence scoring for address transformations and enrichment. This enables institutions to differentiate higher-confidence remediation from cases that may require additional review. Institutions can also evaluate the solution against their own data and payment corridors, allowing performance to be assessed within their specific operational context. This combination of measurable performance, confidence scoring, and institutional control is particularly important when AI becomes part of financial operations.
Designed to fit into existing payment workflows
Improving address data should not require institutions to replace their core payment environment. Catalyst Data Intelligence is designed to operate as an additional processing layer within existing workflows.
Address information can be passed to the solution through an API, structured by the model, and returned to the payment process for further processing. This approach allows institutions to introduce address intelligence while maintaining their existing payment architecture. Catalyst Data Intelligence can also operate within the institution's own infrastructure, helping organizations retain control over sensitive payment data.
Better data supports greater automation
As payment operations become more automated, the importance of reliable data increases. Payment engines, screening systems, analytics platforms, and AI applications all depend on the quality of the information they receive. More structured and consistent address data provides stronger inputs for these systems and creates a better foundation for automated processing and decision-making.
Structured address adoption should therefore be viewed as more than a compliance exercise. It is also an opportunity to improve the data foundation on which future payment operations will depend.
The webinar will explore how better address data can support payment operations beyond compliance, including straight-through processing, exception management, screening, fraud prevention, and automation. The session will also include a live demonstration of Catalyst Data Intelligence, part of the Catalyst Product Suite.
September 23, 2026 10:00 a.m. EDT | 4:00 p.m. CEST
Performance figures are based on evaluation of Catalyst Data Intelligence models. Results may vary depending on data, configuration, infrastructure, and deployment environment.
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