Big Data has inevitably transformed the landscape of financial services. With economic sectors generating vast data, efficient data integration will allow businesses to provide enhanced services with reduced operational costs.
Big data in finance has led to crucial technological innovations that offer convenient, personalized, and secure solutions. Here are a few efficiencies, pitfalls, and challenges of big data in financial services.
Efficiencies of Big Data in Financial Services
Banks can effectively use customers’ transactional information with big data. These real-time insights boost financial services with solid profitability. Here are a few efficiencies of big data in the banking industry.
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Offers Better Banking Solutions and Segmentation of Customers
Integration of big data with practical tools and technologies provides better opportunities for the financial industries to understand individual customers based on the insights like investment habits and personal or economic backgrounds. Additionally, big data in banking creates products and services per existing customer profiles tailored to their specific needs.
Furthermore, robust customer segmentation allows banks to appropriately target clients with suitable marketing campaigns to meet their expectations in a meaningful way.
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Allows Businesses to Analyze Customer Feedback
Big Data tools provide banks with customer queries and comments by utilizing the insights procured from feedback. This feedback assists businesses in responding to customer needs promptly. Customers will retain loyalty to the company if they believe the banks value the feedback with quick interactions.
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Fraud Prevention and Detection
Fraud detection and prevention of questionable transactions are critical challenges in the banking industry. Big Data ensures that no unofficial or suspicious transactions are processed and provides safety and security. Simultaneously, banks can utilize big data to make customers feel secure by analyzing customer spending patterns and monitoring unusual behavior.
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Enhances Customer Relations and Compliance with Regulations
Big data analytics has changed the way financial organizations interact with customers. It plays a significant role in collecting and analyzing customer data to provide beneficial offers with secure transactions and better relationships.
With big data analytics and BI tools maintaining records and complying with regulations becomes much more efficient. These tools can effectively manage and track all regulatory procedures. Furthermore, when devices are configured appropriately, they can efficiently manage compliance and reduce the errors caused by human intervention.
Pitfalls of Big Data in Financial Services
The financial services industry can better meet customer needs by aligning with big data. However, some crucial roadblocks need to be addressed when implementing big data.
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Outdated Systems and Minimal Level of Data Maturity
A majority of banks rely on traditional mainframes for their operations. Legacy and the obsolete system often need help to cope with the increasing data. The entire system’s stability is at risk when a vast amount of data is collected and stored in an outdated infrastructure.
Data maturity is a standard to which banks and financial organizations adhere to get the most valuable insights.
These necessities a sophisticated technique to analyze the data that decides the data maturity of the financial organization.
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Big Data is Getting Vast
With vast data volume, the financial organization often needs help to separate valuable data. While the amount of potentially useful data is maximized, irrelevant data also need sorting. Businesses need to prepare and bolster effective methods and robust applications for analyzing more volume of data. Big data easily justify risks by providing vital insights with minimal resources.
Challenges with Big Data in Financial Services
A growing number of unstructured and structured sources actively generate big data. Legacy and outdated data systems cannot tackle the vast volume, velocity, and variety that the data offers. While management relies on building appropriate processes, enabling robust technologies to extract insights from the data is necessary.
With technology already stationed in place to resolve these challenges, businesses need to understand the process of managing big data by aligning process modern technology initiatives to overcome general organizational resistance. Here are a few challenges of big data in finance.
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Regulatory Requirements
The finance industry is bound to the Fundamental Review of the Trading Book (FRTB), which has secured access to critical data. However, innovations in big data technology enable financial organizations to scale up cost-effective risk management while enhanced metrics transform data for analytic processing to gain vital insights.
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Data Quality and Data Silos
Data is sourced from numerous systems; therefore, it barely fulfills the data governance factors. Thus, data management solutions ensure that information is accurate and secure.
Furthermore, employee documents, emails, and enterprise applications form the basis of crucial financial data. Data integration tools combine and reconcile big data to simplify the storage process.
Big data solutions and the cloud together can tackle and resolve these challenges. As more financial organizations utilize cloud solutions, they indicate to the financial market that big data solutions benefit financial business applications.
Also Read: ThetaRay and Piie Join Forces to Monitor AML in Insurance Payments
Influence of Big Data Analytics in the Financial Sector
The data curated by big data influences the production and success factors of financial services. Financial analysts can make informed investment decisions and monitor spending patterns using data-based predictive models. Additionally, it has enabled businesses to overcome roadblocks and improve the banking experience while meeting customers’ expectations. Furthermore, big data has created transparency by significantly impacting economic analysis. Financial organizations have adopted big data analytics to maintain a solid competitive edge. As financial services move towards big data and automation, statistical techniques will increase accuracy.
Data boosts financial organizations. Therefore, they need appropriate tools to monetize the data. As per a recent report by Research and Markets, “Big Data: Global Market Size, Share and Growth,” the global big data market is anticipated to grow to USD 278.5 Billion at a CAGR of 10.7% by 2027. Businesses continue to move towards adopting big data solutions due to their untapped potential giving companies access to innovate to match the sharp competitive edge
