Management Of Financial Services

Management Of Financial Services – Financial institutions are adrift in a sea of ​​data (product and service histories, customer information, financial transactions, marketing campaigns, regulatory data, external benchmarking data and more) from various mobile apps, online activities and interactions within the branches.

The promise of every single data point created by a first or third party is that they inform financial services data management solutions to develop a better, more accurate and deeper understanding of customers’ needs and wants. A Forrester study of European banks found that institutions recognized as leaders in customer service have equipped their service representatives with the knowledge and ability to respond quickly to individual customer needs and concerns.

Management Of Financial Services

However, financial institutions face particular challenges when trying to personalize their customer experience. In addition to creating a comprehensive view of their customers and a seamless omnichannel customer experience, financial institutions need to manage regulatory requirements, generate ideas for more cross-sell and up-sell opportunities, and consolidate M&A data. While data can provide valuable options for solving all of these problems, it can also create its own problems if data is inconsistent across systems and as a result is not handled properly.

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The danger of erroneous or mismanaged data is compounded by intense regulatory pressure on financial institutions, which can have serious consequences if breached. For example, the Beneficial Ownership Rule (BFO), implemented and enforced by the Financial Crimes Enforcement Network, requires banks to identify all significant owners of legal entities and their accounts. A comprehensive view of the customer can quickly identify any illegal financial activity such as money laundering, tax evasion or fraud, as well as other serious crimes such as terrorist activity, enabling the financial institution to take timely action.

The risks and benefits of data collection are substantial, but fortunately both can be balanced with master data management for financial services organizations.

“Bad data is a problem” isn’t exactly a groundbreaking statement, but companies across all industries are guilty of underestimating just how big a problem with bad data is. Gartner reports that poor-quality data costs organizations an average of $12.9 million annually.

And the consequences of poor data quality aren’t just financial; for financial service providers, this poses additional risks of bad decisions, customer dissatisfaction and non-compliance.

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The good news is that inaccurate data doesn’t have to be a constant problem. Its root causes – human error, departmental silos, data duplication, etc. – are easy to identify, allowing you to plan them. To develop a robust data strategy, you must first ensure that your existing data and any new data entering your systems meet the following criteria:

Setting standards and controlling data is just the beginning. To truly leverage this valuable resource, financial institutions must also take steps to unify customer data, break down data silos, and break down data isolation.

Not so long ago, customer data was limited to superficial information like name, address, and transaction history. Today, a huge amount of data comes instantly from many different sources, from mobile applications and website portals to branch transactions and ATM interactions. Effective data management enables financial institutions to build a realistic, actionable and up-to-date view of their customers, which can lead to a more personalized experience for the customer and better understanding for the institution.

Unfortunately, a larger set of input data doesn’t automatically mean better understanding of customers. Many departments maintain data warehouses that store data even though it may be useful to other departments and teams. This lack of connectivity also means that much of this available data is duplicated and replicated within different institutions across institutions, leading to inefficiencies and increasing the risk of errors.

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The companies that were able to effectively analyze their data at scale, what the McKinsey study termed as breakaways, were the ones that excelled at enabling decision makers to drive better business outcomes. These companies are reportedly 2.5 times more likely to have a clear data strategy and twice as likely to have robust data governance, enabled by master data management.

Master data management (MDM) refers to the collaboration between business units and departments in an organization regarding the orchestration, inclusion, and workflow of a particular data region. In the financial services industry, data domains typically include customers, products, and assets. Mastery of these domains provides a complete understanding of all data stored in these domains. Having a single consolidated database allows business users to:

The key issue for MDM is security. Managing who has access to business-critical information, who can make changes to that information, what changes can be made, and when those changes take effect is complex. MDM provides a single place to manage these changes and gives you a clear view of how they affect all business units.

MDM is designed as an “active data” solution that keeps business systems in sync. For example, you can use a data service to automatically collect, validate, store, and distribute/sync customer information, categorize people based on where they are in the sales pipeline, and so on. This ensures that the right data is collected at the right time and that the data is accurate, consistent and up to date. This can be tricky given that financial institutions often obtain data from multiple sources, some of which provide “ugly” or inconsistent data. This data needs to be cleansed and made actionable so that business users can use it to make better decisions about target markets, fraud prevention, upsell and cross-sell to customers, and so on. Therefore, a key element of ‘MDM is the validation of data previously entered into the system to ensure that the bank’s data quality standards are met.

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As with any successful data strategy, successful financial services data management requires careful consideration and planning. First, you need to establish the scope of your MDM project – that is, determine the key areas of the business and the data that needs to be managed. To do the latter, you need to determine which domains are most important to master, how those domains affect your organization, and what the potential risks are associated with not managing this data.

From there, you need to take an inventory of all your existing data sources and figure out which ones you can afford to exclude and which ones you can consolidate. In most cases, it is possible to replace several smaller systems with one more robust solution. The fewer systems you have to monitor, the easier it is to create a central repository and maintain proper data hygiene.

In the scaling stage, you also need to choose an implementation style. There are four common styles for implementing MDM:

Note that choosing an implementation style can be difficult for financial institutions that have acquired multiple companies over time as they need to understand how to manage those companies.

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Once you’ve decided on your project scope, you’re ready to create an organizational structure similar to the one shown in the diagram below:

Each person included in this framework plays a key role in the overall success of your financial services data management project. For example, the MDM board should ideally consist of a small group of business leaders responsible for sponsoring the program, setting its direction, and issuing final approval of its scope, structure, and processes. Your core MDM team should build and continually update supporting MDM structures and provide guidance to other team members, and your supporting IT resources should include data scientists responsible for data profiling, cleansing, enrichment, and auditing .

Now that you have created your organizational structure, you need to understand what processes you will need to manage it and what technologies you will use to support it.

In conclusion, it is important to note that master data management for financial services is a combination of people, process and technology. Data can be processed without technology, but without people and processes no technology can give you what you really need.

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There are a few simple rules that every financial institution should follow to ensure the success of their MDM project:

Go beyond MDM – turn it into a business asset through revenue opportunities, efficiencies and new lines of business.

Take the guesswork out of financial services data management by partnering with a dedicated team that has faced these issues before. At Hitachi Solutions, our team uniquely combines consulting services, technological innovation, superior delivery quality, and enhanced security and support to eliminate the friction associated with digital transformation. We take a technology-agnostic approach to banking data management, which allows us to serve customers from any source and in any system.

Best of all, we not only walk you through the MDM process, we partner with you every step of the way so you can take ownership of your results and develop the confidence to take on any challenges that come your way. . Contact the Hitachi Solutions team today to get started! The financial services industry is undergoing such rapid change and innovation as it exists

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