Big data has now become a staple for multiple industries and one of the biggest reasons for their successful growth. Among the many ways that companies use big data, the most important is for harvesting crucial information that is vital for security, customisation, and decision making.
However, to successfully use big data, you must rely on its quality and not on its large volume. It is all about how well you can categorise the available data so that it can be analysed for patterns that provide you with meaningful insight.
Quality data is a necessity in all industries, including the financial one. Just like in other sectors, big data serves the same role of providing understanding that leads to better customer experiences, help to outshine the competition and make strategic decisions that help the company grow. In this article, we discuss how big data helps financial companies to grow.
What is Big data?
Extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions. It is essential to all businesses who can exploit the insights it provides so they can make better decisions regarding which strategic moves will successfully facilitate business growth.
Top 7 Advantages of Big Data Technologies in the Financial Sector
Here are seven different ways that big data technologies benefit the financial sector:
1. Facilitates Product Diversity and Development
Big Data is a powerful medium for identifying patterns that allow companies to understand their customer’s psyche. As a result, businesses can develop and offer new and improved financial services that better meet their customer's needs. Using this information, they can refine their product offering and increase their customer satisfaction which in turn will increase customer loyalty and sales volumes.
2. Enhanced Market Analytics
The finance sector can use big data analysis to get an in-depth grasp of current market conditions. For example, banks and insurance companies can study their customer’s purchase history to identify their best and worst-selling products.
This insight allows them to improve their selling strategy - they can put more emphasis on their top-sellers and therefore improve their chances of a successful sale whilst coming up with an action plan to address their worst-selling products. This type of knowledge is vital to withstand the competition and grow sales volume.
3. Superior Security
Within the financial industry, there are services which are more susceptible to security failures and fraud.
Big data is a way to solve this problem. Through the use of machine learning and big data, financial institutions can automate their security by providing their security program with a list of suspicious behaviours or warning signs that will allow it to identify and protect against potential attacks automatically.
For example, with the use of these technologies security systems can identify a client’s patterns - where they usually use their financial services, what they use it on, and how often. This information makes it easier for big data to monitor and highlight inconsistencies from a client's typical behaviour; enhancing security through faster and more accurate risk identification.
Finally, through big data technologies and artificial intelligence companies can prevent criminals from taking harmful actions by exploiting security loopholes.
4. Fewer Manual Processes
The necessity of big data has also created advancements in AI and machine learning technology. Because of these advancements, companies are able to improve many manual processes (ex. processing documents) through automation.
Automation creates a customer-centric approach, its efficiency and accuracy allow companies to serve their customers more quickly and accurately, which improves customer satisfaction and company loyalty. Automation also saves businesses time and money as employees can refocus their attention on completing more essential tasks.
5. Reduces Costs
As we mentioned above, big data is a great way to cut down on business expenses. It allows financial companies to eliminate inefficiencies and idle work hours and to improve the accuracy and speed of decision making.
For example, banks can utilise big data to pinpoint which hours their stores are not busy and use that knowledge to cut costs by reducing the number of staff during slow hours. Alternatively, they can use big data to identify locations where they can add additional ATMs or services that will allow them to reduce wasting their resources.
6. Customer-Centric Perks
As previously stated through big data mining, financial companies can learn all their customer’s spending habits. This information permits sales agents to offer personalised recommendations to clients and to upsell services with suggestions that fit the customer’s past patterns and behaviour. Additionally, companies can create customised services that provide additional value to customers by better meeting their clients needs.
With this approach, banks can retain and develop customer loyalty as well as get referral traffic from their current satisfied customers.
7. Accurate Risk Analysis
Businesses from the financial sector must always ensure that they have a clear picture of the risk they face. For example, insurance agencies must understand what risks they are taking on by providing insurance to specific individuals. Investment firms need to understand the market risks when making investment choices, and banks and lenders need to know that they will be paid back for the loans they give out.
Big data applications are an excellent tool for these businesses to analyse the potential risk of the situations from above. For example, when receiving loan applications banks and lenders traditionally relied on very few factors like a credit score to determine if that person was credible enough to get their loan approved. But with the help of big data technologies, datasets have been diversified and expanded to offer faster and more accurate risk assessments.