MDM in banking
Illia PinchukIllia PinchukCEO
Technology·Banking·

Master data management (MDM) in the banking sector: Challenges and benefits

The advent of Industry 4.0 has introduced an information-centered economy in which organizations and individuals rely on data as the cornerstone of their decision-making. Among the ocean of data, businesses leverage in their workflow; some core portion is crucial for the company’s effective work. Master data presents accurate, consistent, and subject-to-little modification descriptions of all shop floor objects and processes, serving as a reference source for other data types (transactional, inventory, etc.). 

While master data is essential for enterprises in all industries, in the banking sector, it is mission-critical. So, banks go to all lengths to have efficient master data management software and policy. What is master data, and what does make Master Data Management (MDM) in banking so important? 

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What is master data in banks?

Master data in banks refers to the core data elements shared across various systems and applications within the bank. It represents the key data about customers, accounts, products, and other entities that are fundamental to the bank’s operations and decision-making processes. Master data is typically stored in centralized repositories or databases and serves as a single source of truth for accurate and consistent information. 

Some examples of master data are as follows: 

Customer master data — detailed information about individual and corporate customers, such as their names, addresses, contact details, identification numbers, and other relevant data. 

Account master data comprises details about different types of accounts offered by the bank, such as savings accounts, checking accounts, loan accounts, credit card accounts, and so on. It includes account numbers and types, ownership details, and account status. 

Product master data encompasses information about the bank’s financial products and services, such as loan products, investment products, insurance policies, and other offerings. It includes product names, descriptions, terms and conditions, pricing, etc. 

Vendor/Supplier master data includes data about external vendors or suppliers with whom the bank engages in business relationships. It contains details like the vendor’s name, contact information, payment terms, contracts, etc. 

MDM in banking

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MDM in banking

Appropriate Master Data Management is crucial for banks to ensure smooth and efficient operations, compliance with regulations, and effective customer relationship management. It serves as a foundation for various banking activities, including account opening, customer onboarding, transaction processing, risk assessment, reporting, and analytics.   

Related article: “Data governance for banks: Top 5 benefits of implementing”

Why should banks use MDM?

Financial flows are the blood of the global economic organism, and the banking system is the heart that helps this blood reach all parts of it. As the organism grows, banks must increase efforts to keep their systems operating flawlessly. However, that’s impossible unless banks streamline their data management routine, which is often a tough row to hoe. Why?  

Banks generate vast amounts of data (and master data is a significant part) that can be stored, processed, and analyzed only by implementing an efficient data management strategy and employing high-end banking software. Otherwise, they will be flooded by big data and unable to understand it or leverage it as an efficiency-driving instrument.  

Another reason for the necessity of master data management in banking is the industry’s very nature. When you deal with money, any error in personal or business data can cost you a lot—and such accidents are unavoidable if the accuracy, completeness, or security of master data isn’t up to the mark.  

Finally, a powerful incentive behind introducing robust master data management in banking sector is the existing competition in the niche. Banking and financial services are pretty limited in scope, so you can stand out among the rivals only by improving the quality, reducing the red tape, or increasing the speed of service. Adequate master data management is a second-to-none means of obtaining a competitive edge, allowing your organization to provide services and expand efficiently. 

When starting to improve your organization’s master data management, you should keep in mind potential pitfalls symptomatic of the industry. 

Challenges banks face in data management

As a seasoned IT vendor with solid expertise in the banking realm, we at DICEUS are well aware of the challenges related to data management implementation in banking. 

The scope and diversity of data  

For enterprises in some industries, the sheer volume of data banks deal with would be enough to throw them into chaos. For banks, the amount is not the main problem. First, the data they handle relates to various objects and processes – customers (with their buying histories), employees, partners, products, services, transactions, marketing strategies, etc. Plus, there is meta-data. It comes from multiple sources (enterprise apps, ERPs, CRMs, smartphones, and numerous third-party systems). Thirdly, it is often organized in different formats or completely unstructured. All this bulk information must be aggregated, integrated, standardized, and analyzed for any use as a collection of data-driven insights. 

Doubtful data ownership 

Business data is often fragmented between departments and stakeholders, resulting in data silos and intermittent, if not crippled, data exchange. Consequently, uniform data storage is frequently absent at the organizational level, and data flow is sluggish.  

Endangered data security 

In finance, compromising business or personal data can lead to substantial monetary losses and severe reputational damage for clients and banks. Knowing this, cybercriminals are becoming ever more inventive in trying to breach the defenses of data depots, software, and infrastructure banks employ. 

Legacy systems and software 

Nothing restricts data management more than reliance on old-school solutions and manual data entry practices. In this case, it is not only the obsolete software that hamstrings performance but also the human factor responsible for errors, redundancies, oversights, inconsistencies, and other mishandlings that dramatically worsen data quality.  

Following compliance rules 

The banking sphere is one of the most heavily regulated domains, and master data management efforts should consider this. Banks must comply with FATCHA, BASEL, and other standards; otherwise, they will be subject to huge fines and other penalties. Under PCI DSS, BFO, GDPR, etc., banks are mandated to enforce strict anti-money laundering and data security measures within the framework of their KYC (Know Your Customer) policies. They must identify owners of accounts to prevent illegal practices such as tax evasion and fraud which malefactors can exploit to finance terrorism and organized crime.  

Enabling in-depth data analysis 

Raw data hoarded in a safe place is of little value. It becomes an asset when you can leverage it for business intelligence and analytics. That is why master data management should focus on distinguishing this data from the rest, eliminating redundant or incorrect items, and collecting all data under a unified data warehouse (DWH) roof. It must serve as secure data storage and the single source of truth where you can apply BI tools to pinpoint patterns and trends and analyze the company’s performance. 

Providing adequate data architecture 

When data collection is in disarray, processing becomes a significant problem. That is why you should prioritize building transparent master data architecture for your organization.  

Given the utmost importance of this aspect of master data management, let’s have a closer look at it.  

MDM architecture: Models and the algorithm for creating

Having been in the data science niche for over a dozen years, we know the pivotal role of data architecture as the bedrock of efficient data management. While giving thought to its organization, the first thing you should do is to decide on the architecture model. You can choose among three data arrangement schemes. 

Master data management architecture example

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Master data management architecture example

Scheme 1 – Registry architecture 

It enables read-only access to master data for stakeholders who can view but not modify the information in the system. This model guarantees a consistent entry gateway to master data and eliminate duplicates. It is swift to deploy and will cost less than other MDM architectural patterns. Besides, its read-only nature rules out any intrusion into the core systems.  

As for registry architecture limitations, they are pretty severe. In this architecture, all data attributes are disharmonized, which turns master data into a collection of low-quality items that lack completeness and consistency. 

Scheme 2 – Hybrid architecture 

This model is more advanced than the previous one since it enables the completeness and consistency of master data. The absence of total consistency is explained by a delay in master data synchronization updates between subsystems and the MDM database. However, this architecture allows for a better quality of data (which is harmonized and cleansed before entering the system), quicker access due to the absence of federation, easier deployment of workflows for master data collaborative authoring, and more dynamic reporting thanks to the centralized nature of master data attributes. 

Of course, a more sophisticated arrangement and a broader scope of functionalities spell more deployment and data integration expenditures, but this is the price you have to pay to get a superior product. 

Scheme 3 – Repository architecture 

This is the best architecture model in which read and write operations are performed via the MDM system but not in applications (as with hybrid architecture with synchronization delays). It results in absolute consistency, accuracy, and completeness of master data 24/7. The first-rate quality of this architecture type comes at a price, too: it will require not only a thicker wallet to set up but also a deep intrusion into application systems and even suspension of transactions while it is being implemented. 

4-step strategy to implement an MDM architecture model of your choice

Whatever MDM architecture model you will eventually opt for, you should know how to accomplish it. We recommend a four-step strategy for carrying it out, which we employ in our data management projects.  

Strategy to implement MDM

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Strategy to implement MDM

When you develop an adequate MDM architecture and take care of other challenges we discussed, you can enjoy the benefits master data management can bring to your organization. 

Benefits of MDM in banking

The advantages banks can obtain thanks to efficient master data management are as follows:

Data handling benefits 

This is the immediate asset of setting up robust master data management. It will enable your company to optimize data collection and disposal, improve data quality, enhance data integration and governance, monitor data compliance and security, and provide efficient storage. 

Organizational level benefits 

Such perks relate to improving the shop floor routine of the enterprise. Stepping up its master data management, your bank will boost its efficiency and competitive edge by reducing unnecessary and redundant workload, increasing the agility of its workflow, eliminating data silos, and creating a single source of truth and reference point for all employees. 

Business operations and activities benefits 

MDM solutions allow banks to generate accurate reports, improve risk modeling and management, forestall threats, manage privacy, make knowledgeable strategic decisions, optimize company rules, accelerate innovation, etc., eventually resulting in augmented productivity and profitability. 

Customer level benefits 

Customer satisfaction is an overarching business goal, and banks are no exception. Master data management has a clientele-centric perspective, producing a win-win outcome for all parties. Banks get a 360-view of their clients with the ability to adopt a personalized approach to customer experience. Customers receive a general view of relevant information and can obtain the necessary services on short notice without excessive red tape. As a result, their satisfaction is high, turning one-time clients into loyal customers.  

If you want to reap these benefits by the dozen, you should adhere to the best practices of master data management in banking.  

Related article: Customer experience in insurance: Key steps to optimize

The best practices when planning MDM

To make the most of your master data management, we recommend following the tips from our MDM experts.  

These recommendations sound sensible, but did you follow your advice in real-life use cases, you may ask? Let’s find out how DICEUS tackled a master data management project in banking.  

DICEUS MDM expertise in banking showcased

Our customer, a bank from the Middle East, offers various services, including mobile banking and online payments. Their daily activities generate a vast amount of data, so they needed a solution to handle data governance on a large scale. We offered to develop an MDM system that would provide a shared understanding and improved quality of data, a complete view of each client, robust data management, a comprehensive data map, easy access, and consistent compliance.  

Our first move was to get a complete picture of the task we had to fulfill, including establishing data source systems, obtaining a list of entities for each data asset, identifying the list of properties, and gathering data profiles. This discovery phase allowed us to shape a data merge strategy, design a system prioritization mechanism, and develop data quality rules.  

We passed on to the data quality and data cleansing stage after all preliminary steps. Here, our data experts established the data catalog via auto-profiling for all source data and implemented data quality rules. Then, they designed a merge strategy architecture, implemented deduplication logic, and integrated the MDM mechanism into ETL processes. Finally, they propagated historical changes, thus automating data pipelines and enabling access to employees responsible for data handling. 

The primary project deliverable was an easy-to-operate but powerful MDM-based solution with significant scalability potential, whose architecture allowed seamless integration with the data warehouse the client was already using.  

Explore our case studies

Drawing a bottom line

The concept of master data covers the entire scope of information about objects, people, and processes that are vital for an organization’s functioning. When adequately organized, master data management can usher in multiple benefits to companies, including efficient data handling, facilitated workflow, improved risk management, and augmented customer experience.  

In the banking industry, MDM faces various challenges related to the volume and diversity of data, its precarious security, numerous compliance regulations banks must observe, and obsolete approaches and tools for data processing and analytics. 

You can make the most of master data management by relying on best practices in the niche and hiring a qualified IT vendor to develop and implement a state-of-the-art bespoke MDM solution. Contact DICEUS to obtain a top-notch MDM product tailored to your company’s needs and requirements. 

FAQ

Why is master data management important in the banking sector? 

MDM in banking industry is crucial for data integrity and accuracy, complying with regulatory requirements such as know your customer (KYC) and anti-money laundering (AML), better and more personalized customer experience, effective risk management, and efficient operations.  

What are the key components of master data management in banking? 

The key components of MDM usually include data governance, data quality management, data integration, master data repository, metadata management, data stewardship, and master data lifecycle management. 

What types of data are managed through master data management in banking industry? 

Master data management for banks involves managing the following types of data: financial information, customer data, account data, product data, employee data, reference data, risk data, and more.  
 

What challenges do banks face in implementing master data management? 

Data management implementation for the financial sector can pose the following challenges: data complexity (vast amounts of data from various sources), legacy systems (data can be stored in different formats), data quality issues, regulatory compliance, resource constraints (MDM requires significant investment in skilled staff and infrastructure), and change management (MDM implementation requires changes in organization, rethinking roles and responsibilities, establishing new processes).  

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