Banking Transformation: A Central Platform for Enterprise-Class Automated Analytics

Introduction

Global uncertainties remained in 2024, but the path forward for private equity became clearer, with a rebound in dealmaking and distributions.

To the casual observer, 2024 may have felt like yet another difficult year for private equity (PE) globally. Fundraising remained tough—down 24 percent year over year for traditional commingled vehicles, marking the third consecutive year of decline. Investment returns were muted, especially compared with buoyant public markets.

Our analysis reveals a more nuanced picture. After two years of murky conditions, private equity started to emerge from the fog in 2024.

For one, the long-awaited uptick in distributions finally arrived. For the first time since 2015, sponsors’ distributions to limited partners (LPs) exceeded capital contributions (and were the third highest on record).1 This increase in distributions arrived at an important time for LPs: In of the world’s leading LPs, 2.5 times as many LPs ranked distributions to paid-in capital (DPI) as a “most critical” performance metric, compared with three years ago. There was also a rebound in dealmaking after two years of decline, with a notable increase in the value and number of large private equity deals (above $500 million in enterprise value). Exit activity, in terms of value, started to whir again as well, especially sponsor-to-sponsor exits.

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Client 
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A prominent European banking institution providing comprehensive financial services across 40+ countries, specializing in retail banking, corporate financing, and innovative digital banking solutions.  

Industry: Finance  

Department: Data Mining, CRM Support & Marketing Automation  
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Problem
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• Customer-centric analytics requires C360 data model (thousands of customer-centric parameters)  
• Propensity to buy (ML) models too slow  
• SAS and Oracle-based analytics platforms were not scalable

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Solution
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• Implementation of Azure platform in accordance with CAF and WAF  
• A modern data platform based on Databricks service, implementing proprietary solutions Ingestion Framework, Feature Store, and Model Factory.
• Implementation of a complete MLOps framework – Mlflow  
• Full configuration of ISO 27001  

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Business impact
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• Dynamically scale computing resources for Customer 360 calculations  
• Fast model creation and serving mechanism (deploy new models in hours rather than weeks)  
• Single source of reliable information about customer data  
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Enable ML teams to use highly scalable Databricks clusters cost-effectively.

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