Data processing in banks and financial institutions refers to the collection and manipulation of customer data to generate meaningful business information. Data processing techniques helps banks and other financial institution to process essential banking and financial information of the customer. The various processes involved in data processing are sorting, validating, analysis, summarization, aggregation, classification, and reporting. The validation of data consists of ensuring that the data supplied are correct and relevant. Sorting refers to the arrangement of various items in the required sequence at banks and financial institutions. Customer information is classified into multiple categories in banks, and these pieces of information are combined to process the data.
The data processed in banks and other financial institutions need to be kept safe from the cyber-attackers. The cyber-criminals generally attack the data processing technologies with malware to gain access to processed data in banking sectors. The data processing is essential for FINTECH services because it helps FINTECH businesses to face the competitions and challenges in the financial areas. The data processing allows banks and financial institutions to focus on productive banking activities. Some of the data processing activities involved in banking sectors are form processing, check processing, insurance claims, and image processing.
Types of data processing used in banks and financial sectors:
Manual data processing
Some of the banking data processing services, such as posting transactions, producing reports and cash flow statements, and maintaining balance sheets involves manual data processing methods. This process does not include electronic machines. Manual data processing methods are slow and less reliable. The error rates are high while using manual data processing methods in banks and other financial institutions. It requires extensive use of human resources and is considered expensive.
Automated data processing
Automated data processing is carried out with the help of small electronic devices. Error rates in automatic data processing methods are low when compared with manual data processing methods. The output of mechanical data processing methods is relatively small but is more reliable and saves time.
Electronic data processing
Electronic data processing methods are more reliable and save more user time when compared with the other two techniques. Error rates of electronic data processing are much lower compared to the other two was compared to the other two ways. A large amount of data can be processed within a limited amount of times.
Impact of big data on banking sectors
Real-time analytics, customer analytics, or predictive analysis in the banking sector are examples of Big data analytics. It defines the analysis of large, diverse, unstructured, and structural data. Big data helps banks and financial industries to find a useful pattern that improves the banking business. The impact of big data on banking sectors cannot be ignored as most of the new banking solutions are designed with the information obtained through it. The banking and financial sectors contain a large amount of customer data such as data of withdrawal or deposits at ATM, purchase details of customers at the point of sale, online payments, and customer profiles. The customer data, if handled appropriately, will provide the details of customer requirements and expectations. The current rise in data availability and the need for deriving values in banking sectors has increased the growth of big data in financial institutions and banks.
Technology evolution in Banking sectors that is leading to the generation of a large amount of input data for data processing
The rise in technologies like the internet of things (IoT) can help the banking sectors to explode the customer data. The involvement of IoT in banking sectors results in continuous and new streams of information in banks and financial institutions.
The biometric authentication technologies and continuous authentication can help banks and other financial institutions to increase the amount of customer data that are processed in near real-time.
The open Application Program Interface (APIs) or open architectures can help banks and other financial institutions in collecting valuable customer data. The data stored at various other financial institutions are obtained with the help of open APIs.
Technologies used to conduct data processing:
Batch Processing
Batch processing is considered the most straightforward form of data processing technologies used. It can help banks and other financial institutions, that contains a large amount of data. The batch processing is fast enough to process and send essential information to the respective organizations. The batch processing technologies process the data at the end of the day or week, as required by the banking sectors.
Real-time processing
The real-time data processing technologies are faster than the batch processing technologies and can handle information that requires instant turn-around. The real-time processing technologies helps banks and financial institutions to provide immediate feedback to the customers. The real-time data processing works continuously in the banking sectors.
Data mining
Data mining helps banks and financial institutions by collecting information from multiple pools and sources. The data mining technologies combine the collected data and look for correlations. Correlations in banking sectors can help banking sectors to find out about customer preferences.
Measures to secure banking data from the cyber-criminals:
Adopt the data management standards
Banks and financial institutions can continuously establish the card-catalog of various data sources, with the proper standard and format. The financial institutions and banks need to extend their catalog services to security analytics to secure the sensitive banking data from cyber-criminals.
Empowering banking security systems with proper platform and tools
The network communication data are optimized and correlated with implementing an analytic and data management platform. It will generate a complete picture of what and where it has altered in banking data.
Establishing a cybersecurity vanguard
Security professionals or cybersecurity analysts in banking sectors can align with various other organizations and know about their data management expertise present in different domains. Cybersecurity vanguard needs to work closely with various departments within the banking sector to design, deploy, and enforce security guidelines required for data protection.
To ensure the availability, confidentiality, and integrity of the data being processed and available for processing by various assets and services, it is necessary to segment, tag, and label the data that is being accessed by multiple resources.
NetSentries, combining the experience gained in assessing diverse applications installed in operational specific designs and services, through its inbred AVDR (Assess, Declare, Validate and Respond) framework, ensures a thorough assessment of the information processing infrastructure in place to disburse services.
This assessment assures 360-degree security, starting with the posture assessment, covering all the elements including control validation and cyber SOC response strategies with an objective to provide a complete picture of the gaps, remediation assistance, and continuous security monitoring.
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