Big data can be described as huge volumes of structured or unstructured data whose analysis can enable businesses to make strategic decisions. A proper understanding of big data through reporting can help businesses make significant progress and make better and more successful decisions in areas such as finance, human resources, customer service, and more.
The effective use of big data can help businesses to overcome competition and move to the next level. The benefit of big data to businesses and decision makers is more dependent on the quality of data and analysis, and not on the volumes of raw data.
Top 10 Advantages of Big Data in Financial Sectors
The following are some uses or advantages of raw data in the financial sectors:
New product development
Financial institutions like banks and micro-lenders can gain deeper insights into customer needs and satisfaction through big data analytics. From this understanding, the institutions can develop products according to the wants of their customers, which will be more focused and will result in higher satisfaction and larger sales volumes.
Highly regulated sectors like banking and finance attract numerous fraud attempts. Big data can be of great importance to these sectors since the analysis of relevant data sets can make it possible to have improved fraud detection and prevention. The big steps that have been made in analytics and machine learning imply that fraud detection experts and teams can identify risk factors faster and even more precisely.
Monitor online reputation
Thanks to massive innovations, big data tools can carry out sentimental analysis. From the postings on various platforms such as Facebook, Twitter, and Instagram, players in the financial sector can analyze the feedback. They can analyze what various parties are saying about them, their products or services, and so on. Thus, big data tools can come in handy for those financial players who want to keep tabs on their online reputation as well as make it better.
Financial sector participants like banks, regulators, and others can improve their services by proper analysis, understanding, and implementation of the results of the analysis. For example, a bank can use its transaction records to identify the busiest hours of the day, days of the week, and even months, and arrange to have maximum staff deployed, more customer seats, and other needed services. They may even plan to operate longer hours, hire extra staff, or improve their systems for uninterrupted and fast services.
Appropriate understanding of risk is a major concern in financial sectors among players like banks and insurance companies. Insurance agencies must have the highest level of understanding of how much risk they are taking on as they insure customers. On the other hand, investment firms must also grasp the market risks when investing and the extent of risk their customers are willing to take. Lenders likewise need to know how much they are exposing themselves when giving loans or other credit products.
With big data applications, the risk analysis process is possible in a faster and more accurate way for excellent customer service, and for reduced adverse experiences with the financial sector players. As such, financial institutions should prioritize the application of big data, either by having in-house experts or outsourcing experts such as Active Wizards developers, who can work on a consultancy basis and offer them top-notch solutions.
A greater understanding of market conditions
Through big data analysis, the financial sector participants can get an in-depth understanding of the prevailing market conditions. For example, the banks or insurance companies can analyze customer purchasing behavior and get to know what products are in higher demand, thus enhancing their offering of the same for better turnover. They can even detect declining sales for other products and investigate the cause, thereby taking necessary actions to modify the products, be it in pricing or payment mode. Such knowledge of market conditions is crucial to withstand competition and make significant sales.
Better customer service
Financial sector players can use big data analytics on various areas such as customer traffic, preferred products, and feedback from various platforms to improve their customer service. They can increase the number of cashiers, vending machines, and sitting space, as well as improve on other areas that customers may have given feedback on. The idea is to have a more customer-oriented approach to retain many loyal customers and to win others through referrals from the satisfied ones.
Banks, insurance companies, and other financial service providers can rely on big data analysis to identify possible locations to expand. They may use location-based responses or feedback, as well as other data that may show a huge concentration of corporate or individual clients in a geographical location. With such information, they can decide where to open new branches, to have more agents, or to install automated machines.
Choosing the best marketing and promotional strategies
It makes no sense to use a given medium to do marketing and promotions while targeting a certain group of people if it is the wrong channel, as the results will not be achieved. Big data analysis on various things like the platforms that clients use to give feedback can show the best way to reach them. Where most customers use social media platforms like Facebook, the platforms would be better to provide them with marketing and promotional information.
With the application of big data tools, financial sector players are able to eliminate some inefficiencies, choose the best channels of marketing, make faster and more accurate decisions, and reduce idle labor hours, among many other cost-reduction measures. For example, banks are able to plan staff shifts and install ATMs or other services according to the findings from their big data analytics, thus reducing wastage of resources.
The advantages of big data in the financial sectors are numerous. They include improved customer service, better operations, a high level of fraud detection, proper risk analysis, good decisions on expansion, new product development, and cost reductions, just to mention a few.