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New technologies are transforming customer expectations. The young, tech-savvy generation will be the dominant revenue contributor to banks within a few years, while older customers will adopt technology at a much higher rate.
Fremont, CA: The banking system has changed significantly over the last decade. A sector that had demonstrated its shortcomings during the 2008 market collapse was given new regulations due to the global financial crisis. As part of efforts to mitigate the excesses of finance, more detailed and demanding capital, leverage, liquidity, and funding requirements have been introduced. In this article, we will examine how this process affects credit risk management in retail.
Expanding the range and depth of regulation
Diverse factors will continue to influence the regulation. For example, since the global financial crisis, the public and government have been less tolerant of bank failures. Currently, the public sector is policing unethical behavior much more heavily than before 2008. Additionally, governments increasingly require domestic and global compliance with their regulatory standards. As a result, extraterritorial laws and regulations are becoming more prevalent.
The changing expectations of customers
New technologies are transforming customer expectations. The young, tech-savvy generation will be the dominant revenue contributor to banks within a few years, while older customers will adopt technology at a much higher rate. Increasing innovations and investments in fintech startups have brought a range of highly competitive credit risk management offerings. Accordingly, the customers will increasingly Expect intuitive experiences, access to services at any time and on any device, and instant decisions. As a result of these growing customer demands, the bank will need to rethink the entire organization from a digitalized customer experience perspective.
Technology and analytics
New technologies will change customers' expectations as well as risk management techniques. For starters, banks have access to an enormous amount of data. Banks can use faster and cheaper computing power to process data, make better credit-risk decisions, and monitor portfolios for detecting financial crimes and predicting operational losses. Thanks to machine learning, data elaboration is becoming more and more sophisticated. Over time, they become better at predicting events based on new information they receive. Some banks are exploring the use of machine-based collection and fraud detection processes. Additionally, companies can crowdfund ideas on the internet to improve their effectiveness in certain areas.