In the first blog in this series, we looked at some of the benefits for financial institutions of sharing customer exit data as part of efforts to fight financial crime. However, the effectiveness of any data sharing efforts – through which participating organisations or internal jurisdictions can upload and consume customer data — is also decided by the tools deployed to structure and analyse recorded data to find meaningful patterns and deliver knowledge-based outcomes.

Data driven analytics is already used extensively across different risk functions in financial institutions. Such deployments deliver benefits including streamlined operations, cost reductions and enhanced alignment with risk policy. Analytics tools can be extended to enhance customer exit management strategies by targeting the following behaviours in any bank or financial institution.

Typology Root Cause Remediation
A customer exited by a financial institution could continue money laundering activities by transacting with a related individual and/or entity in the bank. Risk functions continue to operate as separate units and do not operate using shared data sets or customer information. This permits varied interpretation of risk policy, potentially conflicting translation into operating standards, and misaligned customer treatment strategy. An institution is satisfied if an exited customer is prohibited from onboarding again and is treated as a screening challenge. Use of network analytics to create a list of related individuals and/or entities that an exited customer transacts with. These individuals and/or entities should be subject to additional levels of monitoring so that they pose no threat to the institution.
A customer exited from one business division of the bank to finds ways to re-enter the bank through another business division. Risk functions for different business divisions in a large bank or financial institution also operate as separate units. Introduction of an internal data sharing mechanism across multiple risk functions so that a customer flagged for exit by one function is visible to all other functions. The strength or score of the flag is increased if any other risk function can validate a similar behaviour. This generates a prioritised list of exited customers and associated behaviour patterns that is accessible to all risk functions for their specific reviews.
A customer changes minimum credentials and is re-onboarded into a financial institution because the screening tool does not flag the case for risk review. The data sets used to monitor customer exits are static so that a trigger is generated for risk review only for a customer with similar parameters as an exited customer (such as first name, last name, date of birth) is flagged by the financial institution’s filtering tool. Create a shared customer risk profile accessible to all risk functions in all business divisions of the firm that will be screened in addition to regulator approved lists and datasets.  The profile is enriched with the ongoing addition of peer-validated data generated from risk reviews.

Source: Accenture, August 2019

The data-sharing framework built within an organisation can serve as an effective prototype to deliver a detailed assessment of value to be gained from sharing data externally.  The experience gained from an internal deployment can also help devise best practices from which to model the external solution.

Through an iterative approach, this analytics-based approach can also be externalised. Financial institutions could screen incoming transactions from counter-parties for their own exited customers and enrich their customer risk profile. The adoption of appropriate entity-matching measures can also tighten the screening and monitoring processes.

In the next blog in this series, we will look at the business case for sharing exited customers’ data within the financial services industry.

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