Use Cases / M&A Due Diligence | Data Integration / Case Study
CASE STUDY
Mergers, Acquisitions & Data Integration.
Two customer examples showing how DataBelt supports acquisition due diligence and complex data consolidation.
Industry
Pharmaceuticals & Life Sciences
Financial Services & Pensions
Use Case
M&A Due Diligence
Data Integration
CUSTOMER 01: PHARMACEUTICALS & LIFE SCIENCES
Assessing Research Data Value During an Acquisition.
Evidence-based acquisition due diligence through comprehensive discovery, classification and assessment of valuable research information.
The challenge.
An international merchant bank needed to investigate potential fraud involving business loan schemes introduced during the COVID-19 pandemic.
The investigation required information from multiple sources to establish how applications had been assessed, which due diligence procedures had been followed and what communications had taken place throughout the lending process.
The DataBelt solution.
DataBelt searched and analysed structured data, unstructured documents and email communications to reconstruct application histories, approval processes and subsequent correspondence.
The information was enriched with external company intelligence, including Companies House and Experian datasets, to establish relationships between applicants, businesses and company directors.
DataBelt’s AI capabilities supported the assessment and prioritisation of potentially fraudulent applications.
Findings were visualised through DataBelt wallboards, providing investigators with a connected view of relevant information and relationships.
The outcome.
The investigation helped distinguish potential fraudulent activity from legitimate lending decisions and supported the assessment of possible internal involvement.
Higher-risk cases were referred to the City of London Police for further consideration, while the findings also supported internal counter-fraud training.
CUSTOMER 02: FINANCIAL SERVICES & PENSIONS
Integrating Large Pension Datasets to Support Scheme Consolidation.
Complex data consolidation through structured cleansing, transformation, mapping and validation.
The challenge.
A pensions administration organisation needed to consolidate large, structured datasets from multiple pension schemes to support the development of larger trust arrangements.
The project required accurate, consistent and complete information across different database structures.
Variations in schemas, missing information and inconsistent records presented significant challenges for the consolidation process.
The DataBelt solution.
DataBelt was used to assess, cleanse and prepare the datasets before integration.
The platform examined database schemas, mapped tables and fields, and identified gaps, inconsistencies and other data-quality issues.
DataBelt’s transformation tools and AI-assisted capabilities supported the standardisation and completion of records.
Once the datasets had been prepared, DataBelt merged the information into a separate target schema.
Validation checks were then undertaken to assess the completeness, accuracy and consistency of the consolidated records.
The outcome.
The organisation established a consolidated dataset suitable for supporting its planned pension scheme integration.
The project provided a more reliable information foundation for subsequent operational and administrative activities.
