Must Read: Big Data in Finance 2022 (detailed Guide)

by Administrator
17 minutes read

Digitalization in the financial sector has enabled technologies like advanced analytics, machine learning, big data AI, and cloud technology to penetrate and alter how financial institutions compete in the market.

Large companies are adopting these technologies to carry out digital transformation, satisfy customer demand and boost profits and losses. Although most businesses are storing valuable and new data, they’re not aware of the best way to utilize its potential because the data is not structured or isn’t a part of the organization.

As the financial industry is moving towards optimization based on data and data-driven optimization, businesses must adapt to these shifts in a planned and comprehensive way. Practical technology solutions that can meet the demands of advanced analytics in digital transformation will allow companies in the financial sector to take advantage of the potential of unstructured, large volumes of data, identify competitive advantages, and create fresh market possibilities.

However, first, companies must be aware of the advantages of technology solutions for big data in finance and what they can mean to their customers and the business process.

What Is Big Data in Finance Exactly?

The massive data and the growing technological complexity change how industries function and compete. In the last couple of years, 90%% of the data that exists in the world has come because of creating 2.5 trillion bytes every day on every day. Also known by the term “big data,” this massive expansion and storage open up opportunities for the collection, processing, and analysis of both structured and unstructured data.

The finance industry is a source of a large amount of information. Structured data is the information that an organization manages to provide crucial insights for decision-making. Unstructured data is available from multiple sources and offers significant analytical opportunities.

The world is flooded with billions of dollars that move through the world’s markets; everyday financial data analyst are accountable for monitoring the data with accuracy, security, speed, and precision to make predictions, discover patterns, and develop strategic strategies for predicting the future. Data’s value heavily depends on the method by which it is collected, processed, stored, and processed. Since traditional systems cannot handle unstructured and siloed information without a significant and complex IT involvement, Analysts are increasingly embracing cloud-based data solutions.

Cloud-based big-data solutions not only reduce the cost of hardware on-premise with a limited shelf life but can also increase capacity and flexibility, integrate security into all of the business applications and, perhaps most importantly, gain the most efficient approach to analytics and big data science in finance.

Through the analysis of different types of data, Financial companies can Make well-informed decisions regarding uses such as better customer service and fraud prevention, improved customer targeting, top channel performance, and risk exposure assessments.

What is Big Data In Finance Works

Following the four pillars that are the basis of data mining, businesses employ analytics and data to gain valuable insights for better business decision-making. Industries that have embraced the big data approach include technology, financial services marketing, financial services, and health care. The use of big data in finance is constantly changing the competitive landscape for industries. Around 84 percent of companies believe that those who do not have an analytics plan run the possibility of losing an advantage in the market.

Financial services, particularly, have embraced big data analytics and also to become a master in finance and data analytics to help make better investment decisions that yield regular returns. Together with the use of big data, algorithmic trading makes use of massive historical data along with sophisticated mathematical models to increase the returns on portfolios. The continuing adoption of big data is bound to change the nature of financial services. But, despite its apparent benefits, significant obstacles remain to be overcome with the capacity of big data to manage the growing amount of data.

How Big Data in Finance Revolutionized

The rapid increase in technology and data creation fundamentally alters how industries and firms operate. The financial services industry is often regarded with the highest data demands and offers a unique opportunity to analyze, process, and use the data to make it worthwhile.

Historically, the process of crunching numbers was carried out by humans. Decisions were based on the inferences made from the calculated risk and trend. In the present, the capability is being replaced by computers. Ultimately, this big data-based finance technology market has tremendous potential and is among the most exciting.

1. Risk Management and Fraud Detection

Financial companies use big data in finance to reduce risks to operations and fight fraud while significantly reducing information asymmetry issues and achieving the goals of compliance and regulation.

Banks have access to real-time information that could be beneficial in identifying fraudulent activity. For instance, when two transactions are conducted with one credit card during an hour in two different cities, the bank could immediately inform the cardholder about security issues and possibly stop these transactions.

Additionally, in the case of insurance, the company has access to data on social networks, previous claims or criminal records, telephone conversations, and more. Beyond the details of the suit while processing the claim. It may declare the claim suspiciously and request an examination if it discovers anything suspicious.

Alibaba built a fraud management and risk monitoring system that relies on real-time extensive data processing to stop fraud. It detects fraudulent transactions and signals by analyzing vast quantities of data on user behavior in real-time, using machine learning.

2. Big Data Analytics in Financial Models

Big data analytics offer the chance to enhance predictive modeling, allowing us to predict investment returns and results better. Access to massive data and a deeper understanding of algorithms will result in more precise forecasts and the ability to effectively limit the inherent risks associated with trading in financial instruments.

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3. Real-Time Stock Market Insights

Big data in finance is revolutionizing how stock markets worldwide operate, and investors make investment decisions. Machine learning, applying computer algorithms to discover patterns within massive quantities of data, allows computers to make precise predictions, make human-like choices when they are fed data, and execute trades at high speeds and frequencies.

The business archetype tracks the stock market’s trends in real-time. It incorporates the highest possible prices to allow analysts to make informed choices and minimize manual errors because of behavior-related influences and biases. With the help of significant information, algorithms result in highly optimized data for traders who want to maximize their returns from their portfolios.

4. Customer analytics

Customers are at the core of every business, based on operations, data technologies, systems, and technology. Therefore, the large-scale data initiatives implemented by financial and banking businesses focus on customer analytics to improve customer service.

Businesses are trying to learn about their customers’ needs and preferences to anticipate the future behaviors of customers to generate sales leads, use the latest channels and technologies, improve their offerings, and increase the satisfaction of their customers.

By establishing positive relationships with their clients and increasing their capacity to anticipate consumer preferences, Financial market organizations can quickly provide new products and services that capitalize on market opportunities.

For instance, the Oversea Chinese Bank (OCBC) analyzed massive amounts of customer information to identify the preferences of each customer to create an event-based strategy for marketing. The system was focused on a wide range of targeted, coordinated marketing communications that span various channels, such as texts, email ATMs, call centers, and more.

4 V’s of Big Data In Finance

The four V’s are the key to the big data world: volume, variety, veracity, and speed. With ever-growing competition, regulatory restrictions, and the demands of customers, Financial institutions are searching for innovative ways to harness technology to increase efficiency. Based on their industry, the company can use specific elements of data mining to achieve a competitive edge.

Velocity refers to how fast data needs to be stored and processed. It is estimated that the New York Stock Exchange captures one terabyte of data daily. In 2016 it was estimated that there were 18.9 billion connections to networks, roughly 2.5 connections per person around Earth. 3 Financial institutions can distinguish their businesses from others by focusing on efficient and speedily processing trades.

Big data can be classified as either structured or unstructured. Unstructured data refers to information that is not structured and is not a part of an established model. This includes information gathered from social media that aid institutions in collecting data on the needs of customers. Structured data comprises information that a company already manages in relational databases and spreadsheets. The different data types must be actively controlled to help better business decision-making.

The ever-growing volume of market data is an enormous challenge for banks and financial institutions. Alongside the essential historical data, banks and capital markets must proactively manage the ticker data. In the same way, investment banks and financial data management companies rely on a wealth of information to make an informed investment decision. Companies that offer insurance and retirement plans can access policies and information on claims for continuous risk monitoring.

Four Big Data Finance CHallenges

1. Compliance with regulatory requirements

Financial institutions must comply with the requirements of the Fundamental Review of the Trading Book (FRTB), strict regulations – formulated by the Basel Committee on Banking Supervision (BCBS), which govern access to crucial data and call for faster reporting.

2. Data silos

The inability to link data across organizational and departmental silos is now seen as one of the biggest business intelligence challenges, leading to complex analysis and standing behind significant data-driven initiatives.

3. Privacy of your data

Data privacy is a significant issue in adopting cloud computing technology. Businesses are concerned about the privacy of their confidential data in the cloud. Even though some have built secure cloud systems, these initiatives can be expensive.

Algorithmic Trading

Algorithmic trading has come to be associated with big data in finance thanks to the increasing capabilities of computers. Automated process allows computers to run financial transactions at rates and speeds that human traders can’t. Based on mathematical algorithms, algorithmic trading will enable trades to be executed at the highest prices possible and in a timely placing and minimizes manual errors for behavioral reasons.

Institutions can better manage algorithms to include massive amounts of data, using vast amounts of historical data to back-test strategies, resulting in more secure investments. This allows users to identify valuable data to keep and lower-value data to dispose of. Because algorithms can be developed using structured and unstructured data by incorporating news in real-time and social media as well as stock data into one engine can result in better trading choices. Contrary to decision-making influenced by various factors, including human emotions and bias, algorithmic trades are based solely on mathematical models of Finance and the data.

Robo advisors employ algorithms for investing and vast amounts of information on an online platform. Investments are considered in terms of Modern Portfolio Theory, which generally recommends long-term investments that provide steady returns and require the least amount of interactions with human, financial advisors.

How to Start with Big Data in Finance

Big Data in Finance

Large financial companies have helped pave the way for big data adoption and offered evidence that big data’s benefits are real. Every financial institution has its particular stage of big-data implementation and maturation; however, the primary motivation for full performance stems from the same fundamental question that is asked across the spectrum: “How can data solve the biggest business challenges we face?”

Suppose the primary concern is customer service, operational optimization, or improved processes for business. In that case, There are specific actions that financial institutions should follow to embrace the data-driven revolution that cloud and big data solutions can bring.

1. Define a data strategy

The definition of a data strategy must always begin with a business objective. A comprehensive strategy should extend across all departments and the entire networks of partners. The companies must consider their data’s direction and expansion rather than focusing on temporary, short-term solutions.

2. Select the right platform

The requirements of each company are unique to each company. The choice of a cloud system that’s versatile and adaptable will allow companies to gather the amount of data they require as well as process the data in real-time.

In addition, the financial sector must choose a platform specialized in security. Monitoring data at an excellent level and ensuring that crucial data is available to the most critical participants will determine the success or failure of the data strategy.

3. Start with one problem

Big data is a wealth of potential. Finding and solving a problem at a time and then expanding from one approach to another allows the user to use big data to be more unified and practical. Simple use cases can be developed and grown in time.

Big Data in FinanceApplications

Financial companies can now use big data in finance in instances such as creating new revenue streams using data-driven promotions, offering customized customer recommendations and generating more efficiency to increase competitive advantage, and delivering enhanced security and more efficient services to their customers. Numerous finance firms are appropriately using big data and seeing immediate results.

Increased revenue and customer satisfaction

Companies such as Slidetrade have used big data techniques to build analytics platforms that can predict customers’ behavior when it comes to paying. By gaining insights into the habits of their customers, companies can reduce the time for payment and increase cash while also improving the customer experience.

Speeding up manual processes

Data integration solutions can grow according to the changing needs of the business. The ability to view a complete record of all transactions each day allows credit card companies such as Qudos bank to automatize manual processes, reduce IT personnel time, and provide insight into the daily transactions of their customers.

A better path to purchase

Older tools don’t offer the solutions to handle large and disparate amounts of data and typically have only a limited number of servers they can install.

Cloud-based tools for managing data have assisted companies such as MoneySuperMarket in obtaining data from various websites in databases for use by different departments, including marketing, Finance, business intelligence, market intelligence, and reporting. Cloud-based solutions like these enhance the buying process for customers, allowing daily performance metrics and forecasts as well as ad-hoc analysis of data.

The Workflow Is Simplified and Has Secure System Processing.

Growing volumes of banking data have led to the modernization of bank data and applications by integrating them across platforms. Coupled with a more efficient workflow and a reliable platform to process data, organizations such as Landesbank Berlin have integrated applications to process up to 2TB of data daily, establish 1,000 interfaces and employ only one system for all interfacing and information logistics.

Review the performance of financials and control growth

With thousands of tasks per year and numerous business units, monitoring financial performance and controlling the growth of employees within a company can be challenging. Data integration techniques have allowed companies such as Syndex to automate their daily reports, aiding IT departments to increase efficiency and enabling business users to access and analyze critical information quickly.


Big data in finance continues to alter how different industries operate, including financial services. Numerous financial institutions are using big data in finance to stay competitive. With the help of structured and unstructured data, sophisticated algorithms can make trades with various data sources. Human bias and emotion can be reduced by automation. However, trading using extensive data analysis comes with a unique set of issues. Statistical analysis results haven’t been widely accepted due to their relative newness. However, as financial services trend toward extensive data and automation, The sophistication of statistical methods will improve the accuracy of financial services.

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