Data Analytics in Financial Services
Digital transformation in the finance industry has introduced many new technologies such as machine learning, AI and advanced analytics. How does the finance industry capitalize on the functionalities of data analytics? This article helps you find the answer with an overview of the use cases of analytics in finance.
The financial sector is constantly expanding with a solid emphasis on leveraging the power of data and analytics for benefits. New, innovative developments in technologies such as cloud computing, AI, machine learning and analytics have been infused into the strategic approaches of many financial institutions towards competition in the market. Many large companies have recognized the potential of big data technologies to support them in addressing consumer demands effectively. At the same time, the advantages of analytics also help financial institutions in boosting their profits.
How does data analytics deliver these valuable benefits for the financial services industry? What are the reasons for adopting data analytics in finance? What are the different use cases of big data analytics in financial institutions? What will data analytics look like in the financial services industry in the future? You must have so many questions like these for estimating the potential impact of data analytics in the world of finance. Let us find the answers to these questions with a clear impression of the different applications of data analytics in finance.
Why Is Data Analytics Relevant for Financial Services?
The first thing you need to note about the domain of finance is the humongous consumer base. As consumers interact with multiple financial services every day in huge numbers, they are also generating massive volumes of data. Financial institutions maintain structured data within the organization for extracting crucial decision-making insights.
On the other hand, financial institutions also have access to unstructured data present in multiple sources. The interesting fact about unstructured data is that it is constantly increasing alongside offering many plausible opportunities for data analytics. The basic tenets of business analytics rely on structured and unstructured data for extracting viable insights to guide their decision-making purposes. How does data analytics become relevant for the financial sector?
The continuously growing volume of data from structured and unstructured sources is an opportunity in data science, albeit with few problems. For example, legacy data systems cannot deal with the volume, variety, and velocity of data generated today. Here are some of the most notable issues in data management for financial institutions today.
- Data Silos
The foremost trouble in leveraging data analytics in healthcare industry or finance relates directly to the existence of data silos. Companies can source financial data from various sources such as emails, employee documents and enterprise applications. Therefore, an aggregated view of all the data with the help of data integration tools can help in extracting the true potential of structured and unstructured data.
- Data Security
The continuously increasing volumes of data generated by financial institutions also open up many vulnerabilities in data security. With the growth in advanced and persistent threats alongside the adaptability of hackers, financial institutions have to worry about data security. On the other hand, the benefits of big data analytics could support the security of data alongside proactive identification of suspicious activity. Learn more about the implications of analytics for data security with a professional data analytics solution provider like Visvero right now.
- Data Quality
Another prominent reason for the implementation of big data analytics in healthcare, finance and many other sectors is data quality. Companies in the domain of finance don’t want to store their data in massive silos without any intention of using it. However, collecting data from different sources presents formidable trouble in using it. Data analytics can ensure that businesses have access to secure, usable and accurate information.
The different benefits of business data analytics are clearly evident in the case of data analytics for financial institutions. Data analytics could help companies in the financial services industry use the data they have at their disposal. Apart from ensuring secure storage of data and improving data quality, analytics could also help in extracting insights from an integrated view of available data.
What Are the Benefits of Data Analytics in Financial Services?
Data analytics has emerged as a formidable instrument for transforming the conventional precedents in the financial services industry. The benefits of data analytics in healthcare and finance, alongside other sectors, have been responsible for reducing the possibilities of human error in daily financial transactions. In the financial services industry, data analytics can offer promising value advantages such as,
- Supporting finance executives in the transformation of structured or unstructured data into different insights for promoting improved decision-making.
- Finance teams can collect the information required for obtaining a clear impression of the Key Performance Indicators (KPIs) such as net income, payroll costs, generated revenue and others.
- Finance teams can utilize analytics big data technologies for a detailed understanding and review of the essential metrics pertaining to security.
- Data analytics is also an essential tool for discovering new business opportunities, such as the identification of future markets and data-driven marketing.
- Financial service institutions can also use data analytics for drawing predictions on the effect of economic, regulatory and market forces on business results and strategy.
Considering the broad scope of value advantages of data analytics in the financial services industry, it is reasonable to wonder how you can use analytics in finance. You can understand the significance of data analytics in financial services only after identifying the practical applications. The two most prominent areas in financial services include banking and insurance. Dive deeper into some of the most promising use cases of analytics in other industries with Visvero right now. Let us find how data analytics drives different use cases in banking and insurance companies.
What Are The Uses of Data Analytics in the Banking Industry?
The banking industry accounts for the lion’s share of the global financial services industry. You can discover the following crucial use cases of big data and analytics in the banking industry with prolific advantages.
- Targeted Customer Experiences
The banking industry can use data analytics to develop to better understand clients by analyzing their transactions and different trading activities. As a result, banking institutions can identify their customer’s requirements and achieve desired customer acquisition and retention levels.
- Improved Risk Assessment
The effective and precise assessment of risk profiles of different credit applicants could provide a vital boost for credit assessments. Data analytics could help in improving early-warning systems alongside enhancing the scope for data collection. At the same time, effective risk assessment helps in lowering risk costs alongside ensuring quicker awareness of fraud.
- Faster Responses to Regulatory Requests
The use of advanced analytics helps banks in ensuring faster and more accurate responses to different regulatory requests. Data can serve as a vital aspect for determining decisions in activities such as placement of ATMs and the amount of cash required in each ATM.
- Development of New Business Models
The benefits of big data analytics in banking would also focus on the development of new business models alongside new sources of income. Data collected from customers can provide ideas for new business models. For example, they are sharing customer data with other companies when the customer has agreed to the condition.
- Rise of Digital Banks
Data analytics seem more relevant than ever for the financial services industry as the majority of banking transactions have shifted online. Many people conduct their banking transactions online through computers or their smartphones. As a result, banks have the opportunity to capitalize on an expanding base of big data to develop an in-depth understanding of their customers. Identify the best practices for implementing data analytics in the banking sector with a reliable guide like Visvero right now.
What Are The Uses of Data Analytics in Insurance Companies?
One of the prominent use cases of data analytics refers to processing healthcare claims in data analytics solutions. With a solid backing of credible data, data analytics could introduce many value improvements in the field of insurance. Here are some of the notable use cases of data analytics for insurance companies.
- Product Design and Marketing
Access to new sources of data helps insurance companies target their customers with specific and highly relevant products. Insurance companies can come up with offerings on the basis of expectations regarding people’s needs in the future. At the same time, data analytics can also help in tailoring new offerings for working effectively alongside improvements in regulation and technology.
- Accuracy in Insurance Operations
The next promising value advantage of data analytics in insurance refers to the accuracy of estimates. The insights of a proficient data analyst can support the careful investigation of the risk profiles of different applicants. As a result, it can lead to better-informed underwriting decisions alongside precise premium calculations according to the relevant risk levels.
- Efficiency in Claims Management
The concerns regarding the significance of “what is data analytics” in the insurance sector find a perfect answer to inefficient claims management. Data analytics supports effective prioritization of claims based on their complexity. Therefore, analytics can help the faster settlement of claims with a stronger emphasis on improving customer experiences.
The technological advancements around us are evolving into many new and innovative forms. As the whole world witnessed radical growth in data analytics solutions, companies in the financial services industry are seeking improved solutions for making the most of the data they have. You can get in touch with reliable data analytics solution providers like Visvero to evaluate the possibilities of using data analytics in financial services.