Role of Big Data in Banking
The emergence of social media, messaging apps, and IoT devices has lead to a significant increase in data, and now the challenge does not lie with collecting the information, but to clean and structure it to derive actionable insights. In this age of information overload, big data is especially advantageous in the banking industry in terms of risk management, fraud detection and regulating compliance.
• Real-Time Risk Analysis: Basil III framework in banks aims to protect it from capital-related, credit or financial risks. With real-time analysis, banks are notified when a certain limit has been reached so that can take the appropriate action. Due to the high frequency of trading data, big data is apt to assess various potential risks.
• Fraud Detection: Big data enables detection of patterns, which becomes an integral part of monitoring activity and separating normal from abnormal user behavior. By doing so, big data avert threats like identity theft, mortgage application frauds, and credit card frauds by raising early awareness and allowing faster resolution.
• Compliance and Regulatory: Banking industry is expected to adhere strictly to compliance regulations. It's a constant struggle to tiptoe around guidelines while enhancing their profits. Big data is instrumental in offering a competitive advantage when analyzing investment portfolios or creating hedged versions while still complying with the necessary regulations.
• Panoramic Perspective: Big data enables banks to acquire a 360 degree perspective of their customer. This includes transaction history, debt ratios, spending habits, which assists the bank in determining individual personalities, and preferences that enables to enhance their customer experience.
• Customer Segmentation: Big data has refined the process of customer segmentation which was earlier based on demographics, geographic location, and common stereotypes. Now, segmentation is done on individual customer behavior, like in the instance of credit card usage.
• Multichannel: Big data enables the bank to detect a customer’s preferred interaction channel. The bank can enrich each user’s profile with the preferred medium and utilize that one to interact with them.
• Chatbots: Chat-bots can prove really useful in resolving simple issues and answering basic queries.
Cost-effective: The application of big data proves to be a cost-effective strategy for banks in terms of analyzing performance, optimizing discounts and eradicating unproductive departments.
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