Banking | Financial Services

Data is the new interest rate

The financial industry is facing a massive transformation. Products are virtual, substitutable, unattractive and offer little room for differentiation. The decisive factor for sales success is the long-term staffing of the customer interface. This is where quality, customer service, trust, security and individuality come into play. In addition to the proximity of consultants and branches, digital competence and presence in everyday customer life is an increasingly important success factor. Many financial institutions run the risk of losing significant market share to digital challengers. Alongside the FinTech companies pushing their way onto the market, changing customer behavior is a major challenge for the traditional all-purpose banks. Customers of all age groups are getting used to being addressed in a personalized, situation- and demand-oriented manner. This requires extensive information and intelligent data about these customers.

Traditional financial institutions have a great advantage over emerging FinTech companies - they have a deep understanding of the industry, a well-established customer base and access to a long and diverse data history. Using this still existing advantage, they should use artificial intelligence (AI) and machine learning (ML) to better understand their clients, optimize their consulting and product offerings, reduce costs through intelligent processes and thus gain a long-term competitive advantage.

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Sebastian Conrady

Our cooperation partner

Data analytics in banking

Data Analytics has a promising application for banks and other financial service providers. Huge amounts of data are available which offer great potential to provide the bank with new or previously unused information about its customers and thus support their strategic operations.

In contrast to many other companies, banks tend to be fairly cautious with data analysis due to high sensitivity and regulatory requirements, and therefore do not yet exploit all possibilities for responsible data analysis. The WDL knows how to deal with these requirements: In addition to the requirements of the General Data Protection Regulation (GDPR) and the concretization by the Federal Data Protection Act (BDSG), which we dealt with at an early stage, we offer our customers trust, protection and security as a key component of our services. All data is transmitted in encrypted form or reduced to the minimum required for our analysis. We help you to identify and realize potential use cases for Data Analytics for your corporate management as well as to increase customer satisfaction, sales success and long-term customer loyalty.

A data analytics project with us usually involves three agile steps:

1. Proof of Concept (PoC) - 4 weeks
We identify a suitable use case together with our customers, which is then validated on the basis of the customer's data within a few weeks. The added value becomes visible and serves as the basis for the decision for ongoing use.

2. Implementation - 4 weeks
We create an individual and user-oriented dashboard with which the Use Case can be permanently established within the company.

3. Automation - Ongoing
When transferring to automated regular operation, all relevant core systems are connected to the system and the results of your analyses are available to you at all times.

Build a data-driven bank today!

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Our customers in banking

Further information on the application of AI in banking

Dr. Alexander Henk
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Westphalia DataLab GmbH

Regina-Protmann-Str. 16
48159 Münster
Phone: +49 (0) 251 20751120

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