CASE STUDY

The Situation

A Texas-based regional bank with $50 billion in assets under management had grown significantly over the prior decade through a combination of organic growth and three regional acquisitions. The acquisitions had left the bank operating across four separate core banking systems, with fragmented data architecture, inconsistent data governance standards, and a risk reporting function that relied heavily on manual reconciliation processes that consumed significant analyst time and introduced material operational risk. The Chief Data Officer, appointed eighteen months prior, had developed a comprehensive data transformation roadmap that the board had approved in principle. The roadmap identified three priority workstreams: consolidating the data architecture onto a single governed platform, building an AI-ready data foundation across the risk and lending functions, and implementing automated regulatory reporting pipelines to replace the manual processes that had drawn comment from the OCC in the prior examination cycle.

The challenge was not strategy. The CDO had a clear vision. The challenge was execution capability. The bank's internal technology team had deep knowledge of the legacy systems but limited experience with modern data platform architecture or machine learning infrastructure. The transformation required a defined programme of specialist capability that the permanent team could not provide and that the bank's procurement framework was structured to procure through a statement of work rather than a staffing arrangement.

The Challenge

The programme needed to deliver across three workstreams simultaneously without disrupting the day-to-day operations of a bank serving over two million retail and commercial customers across Texas and the wider Southwest. The data consolidation workstream required architects and engineers with direct experience migrating financial services data from legacy core banking environments onto modern cloud platforms, specifically within the regulatory constraints of a nationally chartered bank. The AI readiness workstream required ML engineers and model risk specialists who understood both the technical requirements of building production-grade AI infrastructure and the model validation and governance obligations that the OCC and the bank's own model risk policy imposed on any AI system deployed in a credit decision or risk management context. The regulatory reporting workstream required business analysts and data engineers with direct knowledge of call report preparation, CCAR data requirements, and the specific data lineage documentation that examiners expected to see. Finding people who understood all three dimensions, financial services domain knowledge, modern data engineering capability, and regulatory context, in the same programme team was the central delivery challenge.

The Approach

Valmont worked with the CDO and the bank's programme management office over a two week scoping period to define the full statement of work, covering scope, governance framework, milestone structure, team composition, and commercial terms. The engagement was structured as a defined programme with quarterly milestone reviews and a formal change control process to manage scope evolution without creating commercial ambiguity.
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Valmont mobilised a programme team of eleven specialists across the three workstreams. The data consolidation team comprised a lead data architect with prior experience at two US regional banks, two senior data engineers with cloud migration experience in regulated financial environments, and a data governance specialist whose background spanned both the technical and policy dimensions of financial services data management. The AI readiness team comprised two ML engineers with model deployment experience in credit risk environments, a model risk officer who had previously managed model validation programmes at a top-twenty US bank, and a data scientist with specific experience building feature engineering pipelines for lending and fraud applications. The regulatory reporting team comprised two senior business analysts with call report and CCAR data expertise and a data engineer focused exclusively on lineage documentation and audit trail architecture.
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The full team was operational within three weeks of the statement of work being signed. Weekly programme governance meetings were chaired by Valmont's engagement lead and attended by the CDO, the CTO, and the heads of each internal workstream. Monthly milestone reviews were presented to the bank's executive committee. A formal risk and issues log was maintained throughout and shared with the programme management office on a fortnightly basis.

The Outcome

The data consolidation workstream delivered a unified data platform on schedule at the end of month eight, replacing three of the four legacy data environments and reducing the manual reconciliation burden on the risk analytics team by 73 percent. The fourth legacy environment, associated with the most recent acquisition, was placed on a separate remediation track with a defined completion timeline extending six months beyond the main programme.
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The AI readiness workstream delivered production-grade ML infrastructure across the commercial lending and credit risk functions within ten months of programme initiation. Two AI models were deployed into controlled production environments before the programme concluded, both supported by full model risk documentation validated against the bank's internal model risk policy and OCC examination standards.
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The regulatory reporting workstream delivered automated pipelines covering call report preparation and CCAR data aggregation within seven months, eliminating the manual processes that had drawn OCC comment and reducing regulatory reporting cycle time by 61 percent. The subsequent OCC examination cycle included no adverse findings related to data quality or regulatory reporting.

The internal technology team participated actively throughout the programme and retained full ownership of all platform architecture, documentation, and operational runbooks on programme completion. The CDO described the knowledge transfer as one of the most valuable aspects of the engagement, noting that the bank's internal capability at the end of the programme was materially stronger than it had been at the start.