By Katie Joll
The banking sector has seen dramatic transformations over the last decade, impacting most aspects of key operations.
Banks have made progress with their data capabilities between new regulatory requirements and the prioritization of digital transformation, yet there’s still considerable room for improvement. A McKinsey report reveals that many institutions are not fully compliant with data management principles such as BCBS 239, and have challenges with poor-quality data.
Challenges for data quality in banking
The banking industry has considerable room to improve data management practices and enjoys the benefits of doing so. Banks are swimming in a sea of data, but it’s often distributed among disparate systems, blurring visibility and impacting data quality.
There’s a growing reliance on data resources to inform decision-making, reporting, forecasting, risk management, and customer experience delivery. Consider how lending decisions are made, with a heavy weight placed on data the bank has on the customer. This is one example of where data – good or bad – can impact both the bank’s risk profile and the customer’s experience.
Poor data quality can result in a range of heavy consequences for banks. Anything from regulatory penalties due to mishandling of data, operational inefficiencies, loss of trust, and poor customer reputation can result.
How can banks enhance data quality and improve their overall operations? That’s where Master Data Management (MDM) comes in…
What is Master Data Management (MDM)?
Master Data Management (MDM) is an information management discipline that helps banks build a centralized data repository. One of the major challenges for banks is that legacy systems keep data separated, inhibiting users from getting a clear, trustworthy picture. Further, data is often inconsistent across different channels and systems, and mishandling can be an issue.
MDM brings information together from across different applications and systems, countering data silos and providing a “single source of truth.” It helps data users in banks to make better decisions based on consistently categorized and cleansed data. For example, some typical Master Data categories include customers, products, transactions, and accounts.
Some of the primary functions of MDM include:
- Data governance – The collection of processes, standards, metrics, policies, and roles that ensure compliance and effective data management.
- Data quality – The data’s accuracy, completeness, reliability, and consistency. “High quality” generally means that the data is fit for the purpose that the bank needs it for.
- Data integration – Pulling together data from the various systems and applications in the bank.
- Data stewardship – The roles responsible for oversight and managing data in the organization, ensuring that it remains high-quality and fit for purpose.
Benefits of MDM in enhancing data quality in banking
There are several benefits to MDM’s single source of truth and data governance practices for the banking industry. MDM enhances data accuracy, completeness, and consistency across systems and processes, resulting in the overall improvement in data quality.
Banks implementing MDM can expect reduced data errors and duplications and a more streamlined approach to data integration. Instead of cumbersome, legacy methods of integration that often involve manual work with IT in the middle, MDM allows for smooth, automated integration making high-quality data more accessible to users.
These are just a few examples that enhanced data quality can improve for banks:
- Fraud detection. Real-time data helps to build a clear picture of “normal” customer behaviors and to flag any inconsistencies.
- Client visibility and customer experience. A centralized view of client data helps to give client-facing staff a complete picture, allowing them to make informed decisions and offer better service.
- Risk management. MDM’s central storage architecture helps banks ensure regulatory compliance and high data quality for decision-making.
- Business growth. It’s clear that banks that are more advanced in their data management practices are seeing better returns. They’re able to use that data to focus their efforts on the right areas, targeting the most lucrative parts of their value chains.
Implementing MDM in banks
Any Master Data Management Program is a large undertaking and needs widespread support across executives and stakeholders in the bank. From the outset, these critical sponsors should be involved with planning and generating widespread buy-in for the project.
The best place to start is how implementing MDM will help solve key problems for the institution. What are the use cases? Define those problems and communicate them as goals. For example, “to reduce risk in our lending books,” “to improve data access compliance and security,” and “to have a complete view of every client.” Getting buy-in is much easier when people understand “what’s in it for me?”
It’s also important to establish a clear data governance and stewardship framework from the beginning. Your governance framework outlines the “rules” for how data is treated in your organization, with a particular focus on maintaining compliance and quality. Your data stewardship practices lay out the roles and responsibilities for overseeing data practices. Take a look at our article here for some best practices for implementing MDM in banking.
One key facet is choosing the right MDM technology. Your chosen finance data management software should support scalability and flexibility over time, growing with your bank and its needs. Ideally, it should be something that can be rolled out quickly rather than have a long lag time. Your bank doesn’t want to be stuck with the masses of financial institutions in the “proof of concept” phase!
It’s also important to remember that any MDM program requires a commitment to continuous improvement and monitoring. The banking world often undergoes changes, and your MDM system needs to keep up and maintain data quality for long-term success.
Case studies and success stories
MDM is the way of the future for successful data management in the banking and finance sectors. There are already multiple examples of institutions that have successfully implemented MDM and seen positive results for their KPIs. Here are some of those:
- APRIL – This multinational group of insurance services spans 16 countries and has thousands of staff members and brokers. APRIL had a pressing need for an automated system for prospect-customer data cross-referencing and reconciliation, having been using a system of manual inputs. With the implementation of Semarchy xDM, they were able to efficiently aggregate data and increase revenue by identifying cross-sell and upsell opportunities.
- AAIS – With millions of data records, the American Association of Insurance Services needed a solution to sit atop its data lake and serve product and associated reference data. With different legacy systems among their challenges, they needed an MDM program to improve data integration and consistency, comply with regulatory requirements, and improve the consistency of their product delivery. AAIS deployed Semarchy xDM, enabling data lake automation and a 360-degree view of customer data for improved intelligence and communications.
Your bank can gain a competitive edge, along with improved risk management, through the implementation of Master Data Management with Semarchy xDM. Rollout of your MDM program generally takes weeks rather than months so that you can realize ROI more quickly. Get started by contacting us here.