Banking is data - Top ten requirements for effective data management

Data management in the financial industry faces many challenges. These include:

• Complex IT landscapes consisting of many different data silos
• Lack of uniform data transfer standards for both internal and external data consumers
• Increasing regulation of the financial sector with intensive impact on data management (e.g. Basel III, MiFID II, DSGVO)
• Need to store unstructured data
• High data flood
• Growing demand for data security & data protection with increasing threat at the same time

To meet these challenges, the financial industry needs to organize many aspects of data management optimally at the same time. CURENTIS has compiled the top ten data management requirements in the financial industry:

1. Data capture and storage: Banks and insurance companies collect and store a large amount of unstructured data such as customer data, including personal information, financial transactions, account information and more. It is important to have robust systems and technologies in place to securely capture, structure and store this data. The origin of the data (internal vs. external), the frequency of provisioning, and the location of the data within its own architecture also play a role.
2. Data quality and data integrity: To ensure accurate and reliable information, it is critical to ensure that data is of high quality and does not contain errors or inconsistencies. Banks and insurance companies use various methods and techniques to ensure data quality, such as data validation, duplicate checking, and error correction. For example, a database of customer names, addresses, and other information may be subject to the above checks and validations to ensure data integrity, that is, maintaining the accuracy and consistency of data throughout its lifetime.
3. Data privacy and security: Since banks manage sensitive information about their customers, data protection is of utmost importance. This includes measures such as access controls, encryption, firewalls, intrusion detection systems and other security measures to prevent unauthorized access, data leaks or fraud. The General Data Protection Regulation (GDPR) has been in effect since May 25, 2018. For the first time, this provides uniform regulations across Europe on how banks and other companies may handle personal data. The GDPR is of great importance for all of the bank's customers.
4. Data analytics and business intelligence: Banks and insurance companies use data analytics techniques to gain insights into customer behavior, risk management, fraud detection, marketing strategies and other business decisions. Advanced analytics such as machine learning and artificial intelligence are used to identify patterns and trends and develop predictive models.
5. Compliance and regulatory requirements: Financial firms must adhere to compliance and regulatory requirements that also intensively affect data management. These include data protection laws, anti-money laundering regulations, know-your-customer regulations and other industry-specific regulations. Data management must aim to meet these requirements and ensure that data is managed in accordance with regulatory requirements.
6. Data archiving and retention: Banks are required to retain certain data for a specified period of time, whether for legal, compliance or business reasons. Effective data management involves archiving and retaining data in accordance with regulations and enabling efficient recovery and use when needed.
7. Risk management: Data management plays an essential role in banks' risk management. Through thorough collection and analysis of data, potential risks can be identified and assessed, whether in terms of credit risk, fraud risk, operational risk, or other types of risk. Good data management enables banks to proactively address risks and take appropriate measures to minimize them.
8. Business analytics and decision making: Data is a valuable asset for banks as it provides insights into customer behavior, market trends, product performance and other business-related aspects. Effective data management enables banks to efficiently analyze and derive insights from data that help with business planning, product development, marketing strategy, and decision making.
9. Efficiency and customer service: Good data management enables banks to retrieve information quickly and accurately. This enables them to provide better customer service to customers, for example, through fast processing of inquiries, personalized offers, and tailored solutions. Efficient data management also helps to optimize internal operations and processes, resulting in improved efficiency and cost reduction.
10. Competitiveness: In today's data-driven economy, effective use of data is a key factor for banks' competitiveness. Through strategic data management, banks can deliver value to their customers, develop innovative products, provide personalized services, and optimize their business strategies. Poor data management, on the other hand, can lead to inaccurate information, inefficient processes and a competitive disadvantage.

Data management is an essential success factor in the financial industry. CURENTIS actively supports the financial industry in this complex area.