MANAGING FINANCIAL RISKS IN THE ERA OF INNOVATIONS

INTRODUCTION:

Financial Risk Management is the process of identification, analysis and mitigation of uncertainty in investment decisions. Risk management is a much-needed core competency that helps organizations deliver and increase stakeholder value over time. A good risk management requires better data and information, so organizations can take action on an ever-evolving inventory of risks. Predictive risk information can give management a leg-up in making better-informed decisions and help them take actions that produce outcomes that are more reliable. Evolution in computing and risk technology, and related developments in new technologies that exploit Big Data, Analytics, Mobile Applications, and Governance, Risk, and Compliance systems (GRC), are also important for risk management. These technical advancements help risk managers in improving existing risk management programs for enhancing risk management effectiveness.

MANAGING RISK USING TECHNOLOGIES:

Big data provides an alternative approach to building scalable solutions because the time required to manage the data is reduced. Using big data, calculations are moved to the data instead of moving the data to the calculations, greatly reducing overall run times. Big data also provides resiliency to mission-critical applications because redundancy and fault tolerance are built into the software framework. By design, multiple copies of all file blocks are stored on the file system so, if a disk drive crashes or a node dies, jobs can be simply rerun using another copy of the data. AI is the face of the future and is changing the way organizations conduct business. In the financial environment, cognitive capabilities can help you act faster and with greater confidence. Natural language processing (NLP) helps you understand patterns and connections in the news and blogs—it even identifies changing sentiments—before they hit the news. Cognitive technology can be used to uncover patterns in identifying new stress-testing scenarios—and faster than ever before. The potential for AI in financial applications is limitless. When looking specifically at the field of risk management, there are several potential applications for AI that stand out. First of all, ML can be applied to labor intensive jobs, such as cleaning data to improve data quality. A second potential application is processing unstructured data in the form of written articles and live feeds for interesting news that’s related to a portfolio. Natural language processes, combined with sentiment analysis, can help firms take unstructured data and create scenarios that are relevant to one or more investments. Block chain is being viewed as the foundational technology for the future of risk management. The block chain peer-to-peer framework offers the potential to transform current business processes by disintermediating central entities or processes, improving efficiencies, and creating an immutable audit trail of transactions. This will help to lower costs, decrease interaction or settlement times, and improve transparency for all parties. Block chain technology will transform business models from a human-based trust model to an algorithm-based trust model, which might expose firms to risks that they have not encountered before. In order to avoid such risks, firms should consider establishing a robust risk management strategy, governance, and controls framework.

For example, in case of block chain technologies, there are private block chains where there are regulators present on the network. That regulator could have its chain permissions set so that it is only able to view transactions that are in a specific state. In contrast, a business participant in the block chain will only be able to transact with non-regulator parties and not be able to view or action transactions that is not party to. So, private block chains may create less risky environment.

CONCLUSION:

Although the business requirements facing firms related to financial risk management are increasing, so are the ways to address them. The technology that can be applied to the field of financial risk management is changing extremely rapidly. Attempting to keep abreast of the constantly evolving technologies can be overwhelming, and frankly, intimidating. Big data can dramatically reduce run times by reducing data moves while building in redundancy and fault tolerance at the same time. AI can help incorporate unstructured data into the risk processes, as well as aid in data cleaning. So, as the future of finance will have more use of technology, managing financial risks would be simpler.