Abstract
Like in other industries, financial institutions are facing a new era of big data, which plays the leading role in transformation of financial and economic environment. This article dives into why big data is important in the financial field, and how the information it provides is useful and helpful to corporations and companies. Applying big data analytics provides financial firms with insight into how they can detect fraud, conduct real-time analytics, deal with risk, and direct customer portfolio. Specific personalized services can be offered for their customers via customer portfolio management, customer data management, and analytics, even with real-time analytics. Corporations can better forecast financial markets, trends, patters, and irregularities. Therefore, financial analytics has become a fundamental instrument for finance firms, professional, and individual investors, as well as financial researchers.
TopBackground
There has been an extremely fast growth in the amount of data that is made and collected on a daily basis and because of this data collection, analysis, and processing have created opportunities for new technology, jobs and industries. According to Forrester (2019), big data is considered as a vital dominant driver of competitive advantage that refers the ability to outperform the rivals for businesses. Today, there is a dramatic increase in the amount of generated, mined and stored data, reaching a market size of $50 billion to reach $104.3 billion by 2026 (Basdas & Esen, 2021). Companies produce large amounts of raw data in daily basis via IoT, smart devices and cloud platforms. Technology is constantly evolving and changing the way that we do business. The implementation of Big Data in the business world has proved to be very beneficial and effective especially in the financial sector. The addition of this new research will allow companies to combat fraud, offer more personalized experiences to their customers, and make smarter investment decisions.
Key Terms in this Chapter
Streaming Analytics: The processing and analyzing of data records continuously rather than in batches. Generally, streaming analytics is useful for the types of data sources that send data in small sizes (often in kilobytes) in a continuous flow as the data is generated.
Personalized Services: Making a measurable impression on consumers that is tailored to each individual customer, based on their specific wants and needs.
Real-Time Processing: It requires a continual input, constant processing, and steady output of data.
Risk Management: The process of identifying, assessing, and controlling threats of unfortunate events to an organization's capital and earnings.
Financial Analytics: Financial analysis is the process of evaluating businesses, projects, budgets, and other finance-related transactions to determine their performance and suitability.
Fraud Detection: A set of processes and analyses that allow businesses to identify and prevent unauthorized financial activity.
Social Media Analytics: The ability to gather and find meaning in data gathered from social channels to support business decisions.
Big Data Analytics: A process used to extract meaningful insights, such as hidden patterns, unknown correlations, market trends, etc.