Master Data Management in Banking Sector

Introduction to Master Data Management

The Financial sector of today is drifting in a sea of data, information of customers, product, services and their buying history, financial transactions, marketing strategies, and more sourced from various smartphone applications and devices. This plenty of data produces valuable instances, but it can also create hurdles if this vital data is inconsistent over different systems and mishandled as a conclusion. 

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Also, the fact is that several financial and banking services companies are under regulatory severe, for example, banks, which are directed to the Beneficial Ownership Rule (BFO). Fulfilled and strengthened by the Financial Crimes Enforcement Network, BFO requires banks to identify all significant owners of legal entities and their accounts to prevent illegal financial activities such as money laundering, tax avoidance, and fraud, as well as other major crimes like terrorism. The outcomes of violating this and different rules and regulations are often critical. The risk of any of the problems mentioned above can be significantly reduced by master data management for financial services organizations.

Challenges in Banking Sector

The Challenges in Banking Sector are

A large quantity of data

It is predicted that 2.5 quintillion bytes of data are generated every day. For banks and financial services providers, the amount of data they produce, utilize, store and access will grow exponentially year over year. Mergers and acquisitions also bring in new data sources, including large data stores. Banking is a complex operation dealing with various data and applies complex business rules. This moves organizations continuing to face the challenge of aggregating and accessing vast volumes of historical data and many forms of structured and unstructured data. For credit scoring and marketing purposes, finance businesses also consume data from external data sources.

Complex Data Architecture

The absolute amount of data is managed by various stakeholders, leading to a lack of data ownership. Every business unit (Retail, Loans and Deposits, Credit, etc.) stores data in distinct silos and often in legacy systems. The combination of data between these systems, for cards, payment processing, etc., endures a challenge and only leads to weakened decision-making for the business. Data ownership is, however, predominantly fragmented and is handled by multiple stakeholders and usually measured at a departmental level, rather than at an organizational level.

Governing Agreement

Most of the Banking Services Companies are bound by strict compliance regulations like FATCA, BASEL, etc. Being unable to comply with these data standards, financial organizations usually end up paying huge fines. Banks are mandated by laws like EU Data Protection Act, etc., and measures like PCI DSS (Payment Card Industry Data Security Standard) to defend and safeguard customer data, and the rules vary depending on different geographies. Unless data is digitally maintained in a single repository, it is slow to keep up with these regulations.

Clients Centricity

Improvements in technology and communication have enabled a change in customer dynamics, making the finance area to become a customer-centric industry and focus on developing an enhanced customer engagement plan. About 70% of officials from the finance division maintain the importance of client-centricity. But, here come the questions:

• Do banks “know” their clients?

• Do clients “trust” bankers?

• Are banks giving a “multi-channel” experience?

• Are financial products “related” to clients?

MDM Solutions in Banking sector

Client intellect built

Master Data Management helps banks build a primary central repository of customer data by combining data across different source systems. This supports getting a whole picture of customers’ activities, purchases, etc. with the bank, hence improving client officials nourish customer relationships. MDM merges and de-duplicates client and product data to have a single source of authenticity, therefore allowing reliable and quality data.

Early Fraud Detection

The domain of digital, despite all its pros, does come with the immense hurdle for banks to handle frauds and scams. MDM helps banks understand client spending patterns, client irregularities, etc., to be able to identify fraud at an early stage, by cross-verifying changes. Banks can get better clarity in understanding client behaviour through MDM, thus preventing fraud and developing regulatory compliance.

Compliance Risks Abolished

MDM enables companies to learn and decrease compliance risks by helping organizations maintain data quality centrally. Each MDM solution in the market comes with features, which allow organizations to recognize and remove any data quality issues. This efficiently ensures clean and precise data is consistently sent to inspection teams to reduce regulatory fines.

Increase Business Profitability

Being a central repository of all data, an MDM solution addresses the goal of improving revenue and margin. MDM helps in recognizing the specific needs of clients to provide better to those clients, and customized services to make new customer requests. MDM allows marketing teams to optimize cross-sell, up-sell, and product bundling offers, hence helping banks improve customer acquisition, raise the revenue of customer, decrease costs to acquire and hold, reduce customer attrition, and improve product sales.

The Importance of Master Data Management in Banking Sectors

Methods and practices that collectively provide a company’s key data elements enabling the whole organization to perform effectively. Well implemented, an MDM solution synchronizes master data secured within applications across the enterprise, distinguishing the critical data that accurately represents business entities such as clients, employees, products, and facilities.

For banks looking to improve their (KYC) Know Your Customer abilities to handle risk as well as to improve customer service. MDM provides a single, reliable repository for managing and sharing accurate customer data for all lines of business and different systems. Over the past two decades, companies have become more reliant on data to streamline processes and compete more effectively. Considering the kind of business intelligence (BI), analytics and AI results depend on the quality of data; master data management can help by:

• Removing redundant data

• Eliminating incorrect data

• Combining data from various data sources

• Normalizing different data so the data can be used more effectively

• Allowing a single source of reference (single repository)

Architecture of Master Data Management

• Evaluate - Start by examining key business methods and conducting stakeholder meetings and interviews to understand the organization’s goals, functions, and performance. This leads to identifying the beginning and long-term master data demands of the business.
• Specify - The input from the Evaluate stage will be used to set the business use cases, tools and roadmap for MDM ability enablement, concentrated on delivering rapid Return on Investment.
• Design - The Design stage develops an implementation plan, including MDM process parts; the data model, data control; specifications of technology, and takes care of stakeholder expectations.
• Execute - This helps you to realize value through the execution of the implementation strategy, leveraging lessons learned from earlier implementations.