Big Data Analytics in Finance Industry

Financial big data refers to structured and unstructured data that can be used to predict customer behavior to formulate strategies for banks and financial institutions.

Structured data is information managed within an organization that can provide key insights for decision-making. While unstructured data is available in increasing quantities from multiple sources and provides considerable analysis possibilities.

Cloud-based big data solutions not only reduce the cost of on-premises hardware with limited durability but also improve scalability and flexibility and integrate security into all business applications. 

Most importantly, this is a kind of More effective big data and analysis method. With the ability to analyze different data sets, financial companies can make informed decisions for improving customer service, preventing fraud, better positioning customers, world-class channel performance, and risk assessment.

Applications of Big Data in Finance

Financial companies now have the ability to use big data for generating new sources of revenue through data-driven offers, provide customers with personalized recommendations, create higher efficiency to gain a competitive advantage, and provide customers with more security and better service.

1. Improve sales and customer satisfaction

Companies like Slide trade have been able to use big data solutions to develop analytics platforms that predict customer payment behavior. By understanding customer behavior, companies can reduce payment delays and generate more revenue, while increasing customer satisfaction. 

2. Speed up manual processes

Data integration solutions can be expanded as business needs change. It accesses daily the complete picture of all transactions allows credit card companies such as Qudos Bank to automate manual processes, save IT staff time and gain insight into customers’ daily transactions. 

3. Improved purchase path

Cloud-based data management tools have helped companies such as Money Super Market transfer data from multiple Web services to data warehouses for use by various departments such as finance, marketing, business intelligence, market intelligence, and reporting. Cloud strategies like this improve the customer's purchase path, support daily metrics and performance forecasts, and temporary data analysis.

4. Optimized workflow and reliable system processing

The amount of banking data continues to increase, and the core database data and application systems are modernized through a unified integrated platform. Coupled with lean workflows and reliable processing systems, companies such as Landesbank Berlin use application integration to process 2 TB of data every day, implement 1,000 interfaces, and use only one process to complete the entire information logistics and connection.

5. Analyze financial performance and control growth

There are thousands of orders and dozens of business units each year. Analyzing financial performance and controlling the growth of company personnel can be complicated. It enables companies by data integration processes such as Syndex to automate daily reports, help IT departments increase productivity, and enable business users to easily access and analyze key insights.