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data governance framework

Implementing a Data Governance Framework for a Leading Financial Conglomerate Headquartered in Europe

About the Client

A major banking and financial services provider based out of Western Europe with revenues in excess of £20 million and more than 30,000 employees. The client was looking at deploying a data governance framework to address the core data management issues faced by them.

The Business Challenge

Data has turned out to be a valuable asset that needs to be well maintained and secured across industries. For banks, collecting, safeguarding, and analyzing data is a core competency that differentiates them from their peers. Yet several banking firms and asset managers are still in the infancy of taking full advantage of their data governance model. As technology and technological transformations expand an institutions’ ability to profit from data, it also ushers in new vulnerabilities. As such, protecting data from vulnerabilities remains a pressing issue facing financial institutions today.

The unrelenting pressures from non-traditional banks is another factor that is driving financial services firms to transform themselves digitally by deploying an effective data governance framework. However, to completely transform themselves into a data-driven organization, banking service providers needs to address the top three data management trends revolving around: data volume, ubiquity, and user demands. As a result, the process of understanding data quality, deploying a data governance model, ensuring data security, monitoring data management infrastructure, and maintain a data governance framework is gradually gaining importance.

Request a Demo to know why a data governance framework is a crucial component in an effective data management approach.

Most of the data quality issues faced by banking firms result from poor data governance framework, lack of well-planned tactical solutions, and weak data governance frameworks governing a firm’s data management system. A leading financial services provider headquartered in Europe found itself grappling with multiple regulatory compliance issues. The financial services provider was striving hard to drive data governance and data analysis across its enterprise and were on the lookout to implement a data governance framework that would help them address their challenges.

Top Challenges Faced by the Financial Services Provider


predictive modeling

A Leading International Bank Improves New Account Activity and Customer Retention Using Predictive Modeling


The banking and financial services sector has transformed tremendously over the past few years. The recent advances in analytics and predictive modeling techniques have further propelled businesses by offering powerful analytics tools to gain insights into the changing customer needs and behaviors. With the rise in the use of advanced analytics and data visualization techniques, these analytics advances have begun to accelerate rapidly across industries. The potential benefits of these sweeping new advances and predictive modeling techniques are reflected in a variety of areas such as enhanced anticipation and prediction of possible customer churn, improved effectiveness of cross-selling and marketing activities, and greater efficiency and accuracy in anti-money laundering, and other compliance initiatives.

In such a complex business scenario, satisfying the growing customer base turns out to be a daunting task even for well-established banks. Though banks have been adopting various tools to address these challenges, factors such as ensuring long-term loyalty, customer retention, fraud detection, and credit risk management have always been key areas of concern. Facing similar challenges the client in this study realized that predictive modeling would help them address such issues. The client chose to partner with Quantzig to effectively address their challenges and to expand their knowledge of how modern tools and predictive modeling techniques could improve the efficacy of their existing business models.

Request a demo to know more about predictive modeling techniques.

Predicaments Faced

The banking client had been running a new customer acquisition program that focused on new customers relocating near branches with a cash incentive to open new bank accounts. Although the program had generated reasonably good results, the bank was anticipating reductions in available marketing budgets and wanted to lower program management costs while improving overall business outcomes.

Our Solution

Our experts worked closely with the client to develop a new predictive modeling process that makes accurate forecasts to best serve their business budget and operation planning needs. The devised predictive modeling process helped the client to identify influential attributes of new responders by categorizing the prospects into five groups based on their probability of response. The client targeted the top five categories that consisted of 60% of the new and most responsive user groups.

The use of a new predictive modeling approach delivered detailed insights and accurate predictions that helped resolve business uncertainties into profitable probabilities. To take advantage of the insights offered by the new predictive modeling approach and to make better, more profitable decisions, the client wanted to deploy predictive analytics models in their operational systems. A new business plan coupled with a robust predictive modeling platform delivered:

  • A collaborative environment and shared framework for problem definition to ensure the analytics is solving the right problem
  • A repeatable, industrial-scale predictive model

Our Predictive Modeling Solutions Can Help You Gauge Business Success

Business Impact

The solutions offered resulted in a stable predictive model with a performance that exceeded the client’s existing system, despite the considerable effort that had been invested in their existing model. Also, it’s essential to note that by focusing on the most responsive, new targets the client significantly increased customer acquisition rates and associated transactions while cutting down on their marketing costs. The predictive modeling solutions also empowered the client to fine-tune the audience based on various criteria to accurately predict acquisition campaign results. This, in turn, enabled the bank to optimize program performance on a continuous basis.

The adoption of predictive modeling techniques offered the following outcomes:

  • Customer acquisition rates increased by 25%
  • New account activity improved by 30%
  • Significant reduction in marketing costs

What are the different types of predictive models?