The risk management in banks has largely been affected by the development of information technology and the globalization of financial markets. Regulatory aspects also play a vital role in addition to these factors. Banks have to manage their risk to assure their financial soundness and profitability. Regulators are equally liable to safeguard the soundness of the entire financial system. With the advent of attractive financial products and the latest trading technology, the business of banking has developed tremendously over the past few years. As compared to the instant modifications in the financial market, the management of risk in banks is considered to be not so strong.
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Regulations shall continue to broaden and deepen as the speed and degree of regulatory modifications are not likely to be consistent across nations. The behavior of banks toward their valued customers has been under inspection. In several jurisdictions, the conditions and terms of the contracts, sales practices, branding and marketing are being regulated. The rules to safeguard bank customers are more likely to constrict.
Technology is an integral part of every banking unit. It is a must for delivering a high-quality customer experience. Banks cannot deploy the same technology for many years. They tend to change it in response to the constantly changing anticipations and needs of bank customers. The technology deployed by banks has to be completely customized. The point here is, the customization of technology in banks is expensive. Thus, the degree of the risk management in banks is likely to be higher in the near future.
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The banking sector has witnessed many advanced analytics in the recent past. The most popular advanced analytics encompass big data, machine learning (m-learning) and crowdsourcing. Big data is an inexpensive and faster computing power. It is more useful to banks. The question now is whether or not banking institutions may obtain customer and regulatory approvals for the models, which make use of social data and online activities. Machine learning enhances the accuracy of risk models through recognizing nonlinear and complex patterns in big-scale sets of data. These technology innovations may reduce costs and fines associated with the risk management in banks.
New risks have been emerging in the banking sector. They include model risk, contagion risk and cybersecurity risk. The dependency of banks on business models has been increasing. Therefore, risk managers should manage and understand the risks associated with business models. Banks are expected to face cybersecurity risk in the future as they are storing a rising quantum of data about their valued customers. They also need to track and evaluate their exposure to corruption. The risk associated with the exposure to contagion or corruption is called contagion risk.
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For bank risk-management functions, biases have been highly relevant since banks take risk. Each and every risk decision is prone to biases. Many techniques have been developed by renowned academicians and practitioners in order to overcome the biases existing in the system. Several industries have started to apply them. A few energy utilities are endeavoring to eradicate biases through redesigning the process they chase.
Industry veterans have predicted that the pressure for cost savings shall continue among banks. The operating overheads of banks shall perhaps be lower than what they are today. The ways to cut the costs include digitalization, standardization and simplification. The risk function in banks has to find ways to reduce costs this way. For instance, a powerful and automated framework for control may reduce the interventions of human beings in the banking system.
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