The Banking and Financial Services Industry(BFSI) has undergone many changes over the past few years, leading to increased use of Big Data technologies. Banking and Financial Service companies are now adopting big data technologies to improve their services and customer satisfaction.
Using information generated from the large volume of structured and unstructured data, companies can learn more about their consumer’s needs and use it to deliver them to the customer and succeed over other companies in that space. Big Data management can help Banking and Financial services to improve risk management through real-time insights into customer behavior.
Big Data in Banking and Finance Services consists of a large collection of structured, unstructured, and semi-structured data used to analyze customer behavior and create new strategies according to customer requirements in the banking and financial sectors.
The Banking and Finance industry generates structured, unstructured, and semi-structured data.
It is organized data stored in a proper format consisting of key insights of any organization or business.
It exists in multiple sources in large volumes and is randomly stored without any clear format. It offers significant analytical opportunities.
It may appear as unstructured but consists of many keywords that are helpful for processing and analysis.
Impact of Big Data on Banking and Financial Service Sectors
Big data has very vastly impacted on banking and financial services sectors. Its impact is major and everlasting, promising the growth of the banking and financial sectors. All the banking and financial industries work on processing data and generating information for the benefit of their organization. Studies have shown that the companies that use Big Data technologies more often have the upper hand in the market than those that rarely use them.
The primary banking and financial services that include big data are loan finance and credit-debit card companies. Big data affects how various providers and marketers collect information about the customers’ evolving demands and need to be based on their requests, usage, requirements, and behavior to develop new products to meet them. It helps the providers to optimize their time, money, energy, and efforts on the customer’s appropriate requirements.
Below are a few ways to manage Big Data in Banking and Financial Services.
Big data technologies in banking and financial service sectors assess risks when trading stocks or checking a customer’s worthiness. Knowing the usual patterns of people’s financial behavior can help the sector to know when something goes wrong.
Analyzing the types of transactions beforehand also significantly reduces the likelihood of fraud.
Today, customer needs and make accurate decisions. So, they can be more efficient and prompt in responding to market demands. People leave feedback and opinion on various websites and social networks. Specialists can analyze these available mentions with the help of Big Data technologies.
It will help in knowing customer demands and requirements. And to fulfill them, which in return, makes the customer a loyal member.
The banking and Finance industries can use Big Data to understand a customer’s whereabouts, like where customers live, where they shop, and how much they spend in their day-to-day life.
Regulation breaches help in reducing the risk of sanctions and fines.
These Big Data use cases in banking and financial services will give you an insight into how big data can impact the banking and financial sector.
It is the process of categorizing customers based on their behavior and requirements. It helps in meeting the needs of the customers in a better way. Studies have proven that many Banking and Financial sectors are moving from product-centric to customer-centric work environments, so targeting individual customers is most beneficial and necessary nowadays.
Big data analysis helps businesses know about the customer’s demographic details, transaction details, personal behavior, etc. Based on these data, banks can recognize and note separate lists for such customers and approach them based on their interests and behavior.
The next step to segment-based marketing is personalized marketing. It is the process in which customers are divided into segments. Segments are based on the parameters derived from big data analytics, and then those customers are pursued according to their demand and sales.
In personalized marketing, an individual customer is pursued based on their buying habits. Many Industries take the help of various AI technologies to find out about a customer’s browsing and buying status and history. The data provided by these AI technologies are a bit unstructured, so the use of Big Data technologies comes into play. It is further converted into structured and analyzed data.
To know customers’ habits and interests, companies use to analyze their social media platforms.
Risk management analysis is one of the key areas where the banking and financial service sector can save itself from fraud and risks. For this, many companies use Big data technologies to gather records about their customers, like their background details buying history, etc. After this, companies analyze whether they can pay for the kind of service that particular customer is looking for or not.
Banking and financial services need regular compliance in their financial business. A few rules state that every business and company needs data privacy and security. Here Big data technologies come to the rescue. Big data analysis can help analyze the data and find the situation where a financial crisis or security issues can occur. It will help the banking and financial sectors be safe from such security issues.
The banking and Finance Sectors are very prone to fraud activities. Many Big data and machine learning techniques can help reduce and solve fraud activities in the banking and financial service sectors. Based on the machine learning analysis, banking and financial service sectors can learn about their customers’ normal activities. And if any unusual behavior is seen, the bank can take appropriate action beforehand. They can immediately blocklist the customer’s card or account and inform the customer.
Data privacy is a major concern in Data science and cloud computing. Companies are worried about the privacy and security of their data. Putting such critical information in the cloud can be a highly insecure process. Hence, many companies have turned to private clouds, which is financially burdensome.
Banking and Financial organizations must fulfill the Fundamental Review of the Trading Book (FRTB) requirements. That is to regulate critical data and stop mischievous internal activities that can cause harm to the customer.
The inability to connect data across the department and organizational warehouse is considered one of the major business challenges. Connecting data and transferring it without traffic or interference is a very difficult task.
Though Banking and Finance companies provide various sources and technologies to collect and analyze data, the quality of data gets breached in all this process. Because data is sourced from so many different systems, it poses an obstacle to data governance. Real-time analytics tools sometimes provide breached and not-so-accurate data.
Bank and Finance Sectors have already dived deep into the usage of Big data technologies and AI technologies. It has made the analysis easier than before. All they need to know about their customer is their system data beforehand. All of this has been possible with the help of Big data in the banking and finance sector.
With the help of Big Data technologies, the time, effort, and money from the banking and financial sectors have been optimized and incited. The banking and finance service sectors can now practice more focused and personalized marketing and customer servicing with the help of big data management.
Big data is also helping these sectors in risk management with the implication of all the possible security assessments and risk management technologies. Trading has become the easiest between both the financial sector and its customers.
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