Autonomous Data Remediation Agents
CleverChain’s autonomous Agents dedicated to data remediation enable reviews of customer portfolios at scale to verify and refresh information, identify and remediate data gaps, and automatically screen for material changes in AML risk. The Agents adapt to the user existing data structures to normalize, validate and enrich data, keeping the customer and third-party book complete, accurate and up to date, as well as fit for regulatory reviews.
Core functionality
End-to-end remediation of specific data points (accuracy, completeness, up-to-dateness)
Intended use case
bulk, targeted reviews of customers and third-party data
Typical customer profile
financial institutions, industrial groups
Typical workflow it supports
periodic or event-drivenremediation exercises
Due diligence that understands the business.
What it does.
Autonomous Data Remediation Agents build on four foundational elements – triggers, actions, logic and data – to verify and monitor the accuracy, completeness and up-to-datedness of customer and third-party data across pre-defined data points. The user's policies, data sets and regulatory mapping are pre-wired into custom workflows to align with the internal review protocol. Data points include static fields such as trading name, shareholders and directors, as well as fields derived from an in-depth assessment, such as complex structures, nature of business – including exposure to money service business (MSB) – and industry risk. Rules are defined in advance to validate findings, support escalations and enable the optional customer outreach to confirm the updates. As of October ’26, CleverChain is remediating over 60 million data points per month.
What industry problems, challenges or unmet need does it address
Autonomous Data Remediation Agents address the structural challenge of verifying and monitoring the accuracy, completeness and up-to-datedness of customer data:
a. Ability to consistently remediate large customer books. These Agents have proven its ability to remediate at scale and in short timeframes entire customer books of millions of records, including small businesses, sole traders and charities, flagging discrepancies or mismatches and automating updates. At the same time, the ability to screen and assess the materiality of changes in legal entities and Key Account Parties (KAP) has supported internal escalation and risk assessment.
b. Standardization of data sets and remediation protocol. These Agents operate on an entirely configurable, bespoke set of data points, which reflects both the user’s individual data requirements, as well as the purpose of remediation. Processing, alerts, escalation and outreach are also components of configurable workflows.
c. Lack of normalized input, rules, output. These Agents normalize data, produce the evidence necessary to reconstruct an update, perform systematic quality assurance about the consistency and authority of the sources and formulate actionable summaries to support analysts’ review as we as potential outreach to customers to confirm the updates.
d. Lack of automation in customer outreach. If required, the Agents can support branded customer outreach to further automate the step of confirming updates.
Continuous monitoring after point-in-time remediation. The Agents operate both at a point in time and continuously. The same Agent performs event-driven and time-based monitoring, re-assessing entities across the pre-defined data points.