Things are changing now, and they will continue to change dramatically over the next decade due to the disruption generated by COVID-19 and as novel technologies like blockchain and Distributed Ledger Technology (DLT) mature.
Fremont, CA: In banking, risk management refers to developing a strategy for detecting, avoiding, mitigating, or responding to prospective losses. Previously, the risks associated with highly leveraged financial institutions such as banks or hedge funds were emphasized. Since the 2008 financial crisis, this has taken the form of regulating against the creation of unlawful and unethical financial products, with countries increasingly demanding compliance from domestic and international financial institutions and measures to protect depositors' interests. Banks relied on internal governance mechanisms for identifying and controlling their two key risk points – cybersecurity and legal compliance – while governments focused on the above. Time ahead risk management in banking is a hot issue of debate as the field becomes increasingly crucial for financial services.
The future of banking risk management will be focused on Technological disruptions such as Distributed Ledger Technology (DLT) and the emergence of fintech, which banks have yet to embrace completely. Other risk factors in the industry include shifting client expectations and an ever-changing regulatory framework. These issues go beyond the usual focus of corporate risk management programs and public policy on prudent loan portfolio management and the need to protect depositor balances.
Banks are, by definition, financial intermediaries. However, DLT is a whole new form of finance, unlike anything ever existed before. DLT allows users to transact safely on a shared ledger without an intermediary. The ledger is distributed and owned by everyone and no one simultaneously. In a DLT value chain, banks may need to rethink themselves fundamentally. As DLT becomes more ubiquitous, some of the risk considerations in traditional banking business models will reduce, if not vanish entirely. To remain ahead of developing risks and disruptions, risk management in banking will need to harness new technologies in the future. For example, machine learning and data science are used by tools like AMPLYFI's DeepInsight to analyze unstructured deep-web data sources, assisting in discovering signals that might otherwise go undetected.
