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February 24th, 2023  |  7 min read

Master Data Management for the Banking Industry

In a world where our personal data is held by a multitude of different organizations, banks hold the deepest and most personal datasets. Forget Google and Facebook, their datasets pale into insignificance when compared with the sheer volume of data held by banks. From employment and property history to investments, savings, credit scores, and transactions, banks have it all.

Data challenges in the Banking Industry

With a wealth of customer and other data at their disposal, banks should be in the best position to offer their customers personalized advice, products, and services. In reality, banking customers rarely receive the kind of tailored offers and bespoke advice they should. Banks are also struggling to streamline processes, manage costs, and drive efficiencies – there is still a lot of manual work required to integrate and clean data, which inhibits a bank’s ability to gain insights and apply intelligence-based technologies.

One of the main challenges for banks is the volume of data they have. Integrating, cleaning, and enriching so many different types of data from multiple systems is not an easy undertaking. This is probably why most banks are still grappling with creating a unified view of internal, structured data. In the meantime, the market has already moved on to addressing unstructured data and using external sources to enrich it in readiness for delivering insight.

Another major consideration for banks in relation to how they manage their data is meeting regulatory requirements and ensuring high levels of compliance at all times. Banks are subject to laws and regulations addressing everything from capital requirements, financial instruments, and payment services to consumer protection and promoting effective competition. All of which place restrictions and conditions on how banks manage their data and ensure its integrity.

Drivers of digital transformation and data modernization

The imperative for banks to evolve into more digitally-enabled, data-driven institutions comes from several distinct, but undeniably related areas.

The emergence of Cloud-native, agile new market entrants is forcing banks to follow their lead and take a more holistic view of their customers and their data. Customers don’t just want to be told which product to buy next – they want personalized advice in real-time. It’s not enough for a bank to know what its customer did, they need to know why they did it and what they are likely to need in the future. In general, it is estimated that banks have the potential to reduce churn by between 10% and 20% and increase customer activity by an average of 15%. This would substantially impact revenue and is why managing customer data and preparing it for use is one of the most important use cases for Master Data Management (MDM) in the banking industry.

Building lean, efficient, and highly effective processes is also a top priority for banks that want to enhance efficiency and reduce costs. Automation, Machine Learning, and AI all have an important role to play in this effort and there is a high degree of interest in these technologies amongst banks and other financial institutions. While results to date have been mixed, partially because of a lack of trusted, governed data to fuel such projects, analyst firm McKinsey is predicting a second wave of automation and AI emerging in the next few years, in which machines will do up to 10 to 25 percent of work across bank functions, increasing capacity and freeing employees to focus on higher-value tasks and projects. To maximize the potential of this opportunity, banks first need to design new processes that support automated/AI work, and they will need a reliable supply of high-quality, integrated data to sustain them.

The compliance conundrum

One of the key drivers for effective data management in the banking sector is satisfying regulatory and compliance requirements. These regulations mean having accurate and up-to-date information with full audit trails and adequate data security protection is important. Historically, this has led to friction between the need to sufficiently protect and report on data and the desire to use it to streamline operations and customize the customer experience.

That has changed as advances in data management technologies have developed to include provisions for meeting data protection and privacy standards. Modern Master Data Management and Data Governance platforms combine the delivery of a trusted single view with the assurance of rigorous data governance capabilities that allow banks to achieve full compliance and use their data with confidence. This is accomplished

through a combination of features like Automated PII Detection, Automatic Data Masking, Data Sovereignty, Consent Management, and the setting of Retention Policies.

The time is now

Achieving fully governed, trusted data is no mean feat for a sector that accumulates a tremendous amount of data on a daily basis. It is however no longer a nice-to-have, as customers demand more from their financial providers and competitors are upping the ante in terms of convenience, flexibility, and experience. The longer a bank allows its technical data debt to grow, the harder it will be to remain competitive.

As margins shrink and new contenders enter the market, the pressure is on to find new ways of delighting customers and exceeding their expectations. For the vast majority of banks, the answers lie within their already extensive data reserves, and now is the time to tap into them.


By Tim Ward
Chief Executive Officer at CluedIn

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