Big Data analytics can help banking and financial organizations extract valuable data to speed up their digital processes and find opportunities.
For businesses today, Big Data is an imperative technology that provides data-based insights into business challenges that cause hindrances. Many financial services firms leverage big data to transform their processes and digitalized operations.
A report by IBM in Analytics: The Real-World Use of Big Data in Financial Services found that 71 percent of banking and financial firms use big data analytics for their data processes and create a competitive advantage. 36% of banking and financial companies reported gaining an advantage in efficiently structuring data.
At the same time, these firms deal with diverse data types through big data analytics and witness a demanding customer base. While the banking and finance industry’s customer data grows, leaders today necessitate implementing big data as an emerging tool and a more critical source of customer insight.
How Banks and Finance Organizations Use Big Data
Here are a few ways banks and finance organizations use Big Data and the benefits it delivers.
Hyper-segmentation of the Customer Base
Banks and finance organizations segregate their customers into several levels and segments based on certain levels and target types. The segregated data information helps to offer customers the right kind of services. Similarly, financial organizations apply segmentation using big data analytics to provide products and services better and attract potential customers with personalized offers. In addition, they both can also estimate customers’ expected expenditures and incomes in the future and draw a plan to ensure financial plans are offered in the right way and at the right time.
Banks and financial organizations leverage big data to effectively implement Robotic process automation (RPA) that results in better efficiency in operations, lessening errors and boosting the workforce productively. As the digital finance workforce grows, the need for robotic process automation is equally increasing. Gartner’s report Finance’s New Digital Workforce mentions that the digital finance workforce is growing by 50%. The growth in financial processes is after implementing robotic process automation (RPA). The boost in its implementation predicts to grow by 88% in 2025.
With RPA playing a critical role in the digital finance workforce, how it will serve in the big data space is a new consideration.
- Accounting – RPA reduces errors while saving manual data entry or gathering effort. Data errors are daunting but impossible to avoid. Here, RPA will help in collating data accurately and rapidly.
- Maintain Data Consistency – RPA software and tools update customers’ data details and constantly filter valuable data. The software helps data run continuously, resulting in continuous data filtration.
- Track and monitor big data – RPA helps financial organizations accurately track big data and monitor it to gain information about customer demands, market position, and valuation. Gaining big data through RPA also helps minimize the risks of breaches and boosts personalized financial services.
Security and Fraud Detection
Big data accumulates in banks and finance organizations due to increased privacy protection regulations under cloud computing technologies. With the continuous technology integration in finance, cyber-attacks have soared by taking advantage of new methods of financial fraud. That makes sense to increase the protection of data.
With the help of big data, finance organizations can speed up reports, analysis, tracking, and monitoring of potential threats, thus ensuring rapidly reducing risks.
In addition, Machine Learning, driven by big data, also helps in fraud detection and prevention of finance data. For instance, credit card security risks are the most common. With the help of big data analytics, banks and finance can interpret and mitigate risks or may freeze cards to avoid fraudulent transactions.
Financial Trend Forecasting
Big data is changing the way finance experts forecast trends. It is one of the primary advantages of big data today. Organizations can easily predict future trends. With specific financial or banking data, leaders can make appropriate decisions about future product productions, investments, and services to customers. And as a matter of fact, financial and banking data analytics makes leaders offer personalized offers and update business processes and strategies accordingly.
Strong Analytics Capabilities
Big data address fundamental business challenges. Advanced big data provides access to more varied data types with strong analytics capabilities that help businesses to detect challenges and teams rectify them in time.
Examining banking and financial companies engaged in extensive data activities reveals that they leverage a strong core of analytics capabilities that helps structure data, allowing optimization, simulations, and data visualization. In addition, Big data also supports the analysis of multiple data types at once, putting banking and financial firms ahead of their peers. Strong analytics also includes interpreting data intentions, bolstering efforts to understand customer behavior and preferences, and improving customer experience.
Today, Big Data analysis opens room for growth and new bank and finance prospects. Financial institutions implementing the technology can better understand customer needs and make accurate business decisions.