BigDataScalability.com – Big data is an ever-increasing volume of data generated in the world. This vast information has transformed how industries compete and operate. Big data in financial services industry, for example, changes the way banks win the favor of customers.
Financial service companies mine the data from various sources, such as search inputs, social media, data collected by users’ devices, media publishing outlets, and corporate assets. These companies analyze the vast data and use the analysis to create better competitive and operating strategies.
What is Big Data in Finance and How does it Work
Digitization in the financial service industry has allowed big data and other advanced technology to transform the method financial service companies work. However, what does big data in finance mean?
What is Big Data in Financial Services Industry?
Big data in finance is the sheer volume of information or data financial companies use for creating strategies and anticipating customer behaviors. The financial industry generates quintillion bytes of data every day. The generated data consists of two types of data, i.e., structured and unstructured.
Structured and Unstructured Data
The structured data is information the companies manage within themselves to get essential decision-making insights. Meanwhile, unstructured data comes from different sources that provide essential analytical opportunities. The volume of unstructured data is vastly increasing every day.
How Big Data Works in the Financial Service Industry
This structured and unstructured data is valuable. However, its value depends on how analysts collect, process, store, and analyze it. With the right approach to big data analytics, financial companies can make the right decision for different aspects of their industry.
For example, some financial service companies adopt big data analytics to gain profitable investment decisions that will give them consistent investment returns. Meanwhile, other financial service companies adopt big data analytics to improve efficiency and gain competitiveness.
The Four Vs of Big Data
Big data has four fundamental aspects. They are volume (size), velocity (speed), variety (different types of data), and veracity (the certainty of the data). Financial companies can use all these aspects or only some aspects of big data to achieve their business goals.
Benefits of Big Data in Financial Services
The adoption of big data analytics in the financial service industry has brought many benefits to the industry. Below are several benefits of big data adoption in financial services.
1. Analyzing Risks
Financial service companies use machine learning algorithms to analyze the risk of giving a loan to a person or a company. The tool will utilize particular data, such as financial transactions and credit history, to predict whether or not a customer is a risky investment.
Based on the prediction, financial service companies can deny a loan to a risky customer because of his bad credit history or give a loan to a customer who they can trust with it.
2. Forecasting Financial Trends
Big data analytics enable financial service companies to predict future financial trends. Knowing the future trends can help them to minimize the negative impacts posed by challenging trends. It also helps them to assist customers in choosing the right financial service or investment.
Moreover, the analysis allows them to forecast demand, supply, and other essential financial indicators. In the end, the forecasts will assist them in making the most profitable decisions regarding their investments, services, and products.
3. Fostering Inclusivity
Financial service companies that use algorithms will treat their customers equally. They will not discriminate against a particular race, ethnicity, or gender because the algorithms they use help them see customers objectively.
Moreover, the algorithms allow the companies to focus more on the customers’ financial activities. As a result, the companies will have objective knowledge of the customers’ qualifications in getting a loan or other financial services.
4. Automating Tasks
Big data in financial services promote an efficient operation that can improve companies’ productivity and increase customers’ satisfaction. For example, when customers fill in an online form, they can autofill the form and finish the task quickly.
In addition, companies can collect and process the form quickly. Automation also allows companies to quickly and easily decide who is qualified to get their loan or other financial services.
5. Analyzing Customer Behavior
Financial service companies use big data related to the customers to understand them better. Big data helps them understand the customers’ needs and preferences. Consequently, financial service companies can enhance or create services based on the customers’ needs.
Big data analytics also helps them generate sales leads, anticipate customers’ future behavior, improve customers’ satisfaction, and take advantage of new channels.
6. Risk Management and Fraud Prevention
The adoption of big data in financial services helps the industry prevent fraud and mitigate operational risks. Unstructured data and real-time data can help the bank identify fraudulent activities, bad transactions, and anything suspicious.
7. Real-Time Stock Market Insights
Big data and trading algorithms assist computers in making accurate predictions and executing trades rapidly. They minimize human errors and manual errors. Therefore, financial service companies can make better and more profitable investments.
Big Data in Financial Services: Its Impacts on the Banking Industry
The banking institutions have adopted advanced technology and big data in their operation for the last decade or more. As a result, they can provide a better customer experience. Now, customers don’t need to visit the bank to save money, send money, and apply for a loan.
Meanwhile, the technology and big data in financial services enable banking institutions to operate more efficiently. Thanks to the adoptions, they can personalize their service offerings, track the customers’ spending patterns, and gather, interpret, and respond to customer feedback efficiently.
Use Cases of Big Data Adoption in Financial Services
Below are several use cases of big data in financial services.
1. Real-Time Analytics and Marketing
Big data and data science enable financial service companies to perform real-time analytics. Some examples of real-time analytics are beaconing, geotargeting, and geofencing. Real-time analytics allows those companies to make unbiased decisions quickly.
Moreover, it enables them to respond to new trends immediately. Real-time analytics also lets financial service companies understand and respond to customer behavior properly. Consequently, they can develop better-targeted marketing campaigns.
2. Customer Segmentation and Targeted Marketing
Financial service companies must segment their customers to understand and effectively reach the customers. They can segment them based on their income, age, gender, preferences, needs, etc.
Manually segmenting the customers is, of course, inefficient and ineffective. However, they can do this task easily thanks to the adoption of big data in financial services. Therefore, they can efficiently design financial products and services based on the segments.
3. Consumer Analytics and Understandings for Insurance Companies
Data science models help insurance companies to understand the insurance risk of each potential client. This technology offers more accurate insights and analysis. Therefore, insurance companies can make the best decision on whom they will accept as clients.
4. Financial Markets and Investment Analysis
Data science can expedite and automate the process of financial market analysis, decision-making, and execution. Therefore, financial service companies will not have problems choosing the most profitable stock to invest in and the best investment strategy.
5. Predictive Analytics and Future Planning
Date science offers an instant analysis of various data sets. This means that the adoption of big data in financial services simplifies the decision-making process for companies. It allows them to predict investment profits, set loan rates, and offer the right products.
Challenges of Leveraging Big Data in Financial Services
The adoption of big data analytics indeed offers many advantages and benefits for financial service companies. However, there are some significant big data challenges in financial services.
1. Data Privacy
Data privacy is one of the biggest challenges in big data adoption. The use of cloud computing and increased big data usage increase the risk of data breaches.
Companies must have excellent security systems and a private cloud network to minimize the risk. However, having both of them can be costly.
2. Data Silos
Another major challenge of big data in financial services is the inability to connect data across organizational silos. This inability will cause complicated big data analytics and hinder big data initiatives.
Big data analytics requires a large upfront investment. This cost may not be affordable for small to medium financial service companies. Consequently, only big companies can adopt big data analytics and get its advantages.
4. Skill Gap
Despite the growing need for big data analytics, there are not enough professionals present to perform this job. The main cause of this skill gap is that big data analytics requires a set of particular skills.
5. Legacy Infrastructure
Some financial service companies innovate and adopt new technology too quickly. Consequently, the existing systems will have difficulties in gathering, storing, and analyzing big data. This obstacle will harm the analytics tools and system stability.
How to Get Started with the Big Data Adoption
Different financial companies have different goals to achieve and issues to solve. However, big data adoption can help them get what they need. No matter what their business goals and issues are, they must do the three steps below to start the adoption of big data in financial services.
- Define the Data Strategy: Financial service companies must define their business goals before big data adoption. Once they define the goal, they can create a comprehensive strategy. Moreover, they must ensure that their data is growing.
- Select the Platform: The second step is choosing a cloud data platform. They need to ensure that the platform they choose is secure, scalable, and flexible. Selecting this type of platform will enable them to gain a large amount of valuable data.
- Start with a Problem: Next, companies must start solving their problems. However, they must solve one basic problem first. After that, they can move to the next solution and make the big data adoption realistic and cohesive.
Big Data in Financial Services: Examples of its Applications
Many financial service companies have adopted big data analytics for more than a decade. Below are some examples of big data adoption in financial service companies.
- Visa adopted Grid computing and the IMC platform to analyze big data. The purpose of utilizing the platforms is for detecting and monitoring credit card fraud.
- Citigroup used Hadoop big data technology for web analytics, fraud detection, and customer service.
- Bank of America adopts big data for enhancing multi-channel customer relationships.
- Garanti Bank (Turkey) gained performance improvement and reduced operation costs after adopting big data analytics with IMC’s complex real-time data.
- BBVA (Spain) adopted the Urban Discovery big data interaction tool for improving customer and employee satisfaction and detecting potential reputational risks.
The adoption of big data in financial services industry has been proven to be a beneficial strategy. Financial service companies will get some benefits from big data analytics. Some of those benefits are improved competitiveness, fraud prevention, and more profitable investment.
In addition, big data adoption also enables banks and other financial service institutions to offer a better customer experience. Consequently, they can maintain customers’ loyalty and attract new customers at the same time.
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