Customer Data Management for Banks

With 'Know your Customer', banks recognize the life moments of their customers based on data.

Banks have had a tumultuous few years. Currently, high inflation, geopolitical tensions and disrupted supply chains are creating a toxic mix. Potential must therefore be activated, costs and risks reduced, and returns increased. Data quality is an important lever.

After a long lean period, banks were able to get back on track and recently increase their return on equity again. However, the current crises and economic challenges threaten to undo this success.

Banks benefited above all from a profitable securities business, the increased marketing of third-party services and the move away from free banking services in the retail business. Value was also created by streamlining branch networks and by consolidation. Mergers in the area of savings banks and cooperative banks in particular reached a temporary peak last year.

Challenges of the industry

Despite all the joy and enthusiasm about these successes, the value contribution of high data quality often seems to be ignored and pushed into the background. Yet high-quality customer data in particular can activate additional return potential from inside, in addition to the external return factors. For example, efficient customer data processes based on reliable customer data promise enormous opportunities. They enable banks to better meet their customers' expectations for highly individualized support along the customer journey.

"Digitizing, knowing your customers, and making smart use of unstructured data are key learning areas for banks on their way to becoming data-driven financial services providers."

Here's how data quality supports:

Address validation:
The basic prerequisite for all data quality initiatives is up-to-date, complete and correct customer data. Combined with geo-coordinates and auto-completion, this creates a high value proposition.
Identity Resolution:
Who's Who. Error-tolerant precision identification can end this confusion and resolve and assign identities. This makes data unambiguous and free of overlap where desired. Sometimes, however, an intentional duplicate is needed, such as for billing and delivery.
360-degree view:
Only a holistic view makes it possible to address customers and business partners in a personalized manner or to implement regulatory requirements. The introduction of a unique ID or business partner matching for risk prevention in particular require valid data.
Household formation:
If you know, based on data, who your customers are, you can start to cluster, to summarize, to form households. Gaining information, but also working with that information intelligently. Who belongs together? Who do I write to on what topic?
Crises notwithstanding:
Data quality makes an important contribution to mastering crises resiliently. If you are well positioned in terms of data, you can take the catastrophic aspect out of crises. But it is also important to stay on the ball. Data is constantly in motion and changing. That's why data quality is a closed loop.

Fintechs in particular, whose business model is based on digital, customer data-based processes, have enormous strategic advantages here that established commercial banks will have to gradually catch up with. Transformation and further development are therefore the order of the day in order to stay on the path to profitability. In addition to other factors such as the use of Web3 technologies, progress on the cost side is crucial. Initiatives to increase efficiency are particularly important here. They require broader automation, digitization and optimized customer data processes. High-quality customer data is indispensable for this.

"Know Your Customer requires the legitimation check of certain new customers to prevent money laundering. With our DQ solutions, banks create security here and additionally add aspects of life support."

Axel Schmale, Senior Account Manager at Uniserv

Key challenges for banks and financial service providers

In order to tap new sources of revenue beyond their traditional business, banks and financial service providers are called upon to create a sustainable ecosystem in a data-driven manner and to place the customer at the center of this without compromise. These three tasks are particularly important for this Beyond Banking:

Digitization as an enabler for new services

Processes and workflows are becoming increasingly digital. Customers are demanding this from their bank. What fintechs have already achieved, traditional commercial banks often still have to realize, sometimes with a great deal of effort.

In any case, high-quality data is necessary, for example, for checking creditworthiness and credit standing, such as for e-commerce and online trading. In addition, in digital reality, there are also data-driven business models such as marketing automation, customer experience management, personalized services or even decisions on the next best action, based on artificial intelligence, which in turn requires recourse to high-quality data as a training basis. In short, to digitize cleanly, you need up-to-date, complete, correct and unambiguous data:

Address Validation: postal valid addresses.

Identity resolution: track down duplicate and multiple data records.

E-mail validation: communicate digitally at any time.

Our Customer Data Hub also provides a holistic view of your customers: It breaks down data silos and consolidates existing data into a single, centralized dataset with a 360-degree view of each customer as complete data truth. This enables you to offer your customers personalized and fully digital new services.

Know Your Customer. More than just fulfilling legal requirements.

"Know your customer" - what could generally be understood as a call for companies to seek closer contact with their customers has a very special meaning in the financial services industry. In the industry, "Know Your Customer" stands for a legally required legitimation check of certain new customers to prevent money laundering.

But this is actually only one side of the coin. Because the qualitatively robust data from these regulatory requirements can be used more widely in the sense of expanding Know Your Customer. Not only can customers be identified with legal certainty. No, the data can also be used by banks and financial service providers to identify important moments in the lives of their customers at an early stage, such as marriages, house construction, and apartment purchases.

These two central challenges of Know Your Customer can be solved sustainably by means of high-quality data. After all, customer loyalty and financial matters are always a matter of trust. The more individualized and forward-looking the approach, the more likely customers are to remain loyal to their institution instead of switching, which in the digital age can be done with just a few clicks of the mouse.

The ideal solution is a 360-degree view of each individual customer. This can be achieved with the Customer Data Hub. Dissolve data silos, merge and consolidate data, including back-synchronization into the source systems. And if you are not sure who is who or who belongs to whom, or who lives with whom in the same household, then the error-tolerant precision identification of Identity Resolution can help.

Unstructured data - well manageable with intelligent tools

Banks often have to deal with large volumes of unstructured data made available as part of Big Data, such as e-mails, documents, presentation slides or audio recordings. So on the data side, a lot of potential remains unused. This is because all of these pieces of data contain important information that can supplement a data set on a customer and thus make it more valuable. But it requires the appropriate know-how and also intelligent tools. Data quality functions help to bring the data 'into shape' and link it with customer data, i.e. structured information.

Ultimately, the key is to get the data into a manageable form. Manageable in the sense of usable. Data preparation involves collecting, combining, structuring and organizing data with the aim of making it usable. Data is valuable if you know how to read it. This is why data is often enriched, i.e. additional information is added to a contact or company data record (data enhancement). This includes data such as telephone numbers, geographical data, data via social media and other external data from various sources.