Reasons Why Big Data are Relevant to Risk Management

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Reasons Why Big Data are Relevant to Risk Management

In the Islamic financial world where there are many questions regarding the Islamic risk management which there are numerous issues such as the limited data. the illiquid instruments to use and many other risks. A lot of studies have focused on risks that are particularly aimed at being controlled risks and how to manage them in a way of that reduces bad effect in decision making wherefore the issue of how the risk originally exists and why it is perceived as a bad effect instead of a good one should first be answered.

Big data technologies can help Risk groups acquire exact risk intelligence, drawn from an assortment of data sources, in nearly Real-time. Inside the financial services industry, they can permit asset manager, banks and insurance agencies to proactively recognize potential dangers, respond quicker and all the more adequately, and settle on strong choices educated by a great many risk variable.

At the point when applied to risk management inside the financial services related industry, we would include ‘high-veracity’ and ‘high-value’ to this list- compelling examination of this data can possibly drive expanded accuracy and dependability, and offers conceivably huge cost reserve funds by combatting the potential significant cost saving by combatting risk that can cost financial institutions fortunes’. Big data technologies are set to change the world of risk management big data technologies given the chances to address these challenges.

Risk Management faces new requests and difficulties. Because of the upheaval, regulators are requiring progressively point by point data and in-depth reports. Later, distinctly published ‘rogue trader’ and money laundering scandal where the unhealthy activity of having illegal tax avoidance outrages have provoked further industry calls for enhanced risk checking and risk modelling. As the advent of financial crisis in diverse part of the world, and a correlated emphasis on systematic financial risk, there have been many developments of the international financial regulation which designed to mitigating such a risk, therefore the created first systematic risk model based on big data.

Big data technologies given the chances to address these challenges. Based on the applied viewpoint in the Cerchiello and Giudici journal, it shows that the risk model can go far on the interrelationship between financial institution industry. As the big data have a tremendous, far fetching technologies which will permit the improvement of models that will bolster regular risk officer on the decision-making. Ready to process immeasurable amount of data in quick time-frame, the technologies can likewise suit new stipulation for situation stress tests at the trade exchange, counterparty and portfolio levels. By using big data, Incidents are identified quickly and a broader perspective of the situation is offered, which allows for reactions in almost real time.

These days, everyone is talking about the estimation value of big data, yet finding down useful approaches to put this data to use remains a test for risk managers. One of the best ways is by utilizing big data to enhance chance administration and decide an organization’s total cost of risk. One other thing that big data incorporate is that it is crucial when picking at a strategy for how you will address, limit and manage with these costs, you’re taking a gander at everything in a progressively all-encompassing route in light of the fact that there is a great deal of interrelation. The things that you can do to address one viewpoint or aspect in total cost of risk may affect another. Thus, it will manage to broaden the risk management choices.

Principally, the data management challenges. According to Shukla, big data management and analytics has turned into an exceptionally vital apparatus in risk management in the banking sector and yet, with such a large number of various kinds of information accessible overseeing sheer volume of data is one of the greatest difficulties for keeping banking industry. Once more, the challenge makes itself discernible when attempting to deal with data that is helpful and data which are unfavourable. Financial institutions currently need to channel through considerably more information to distinguish fraud. Breaking down traditional client data is not sufficient as most client communications currently happen through the Internet, portable applications and online life. To pick up a driven edge, financial institution service needs to use enormous information to all the more likely consent to controls, recognize and anticipate extortion, decide client conduct, increase of sales, create data-driven items and significantly more. Significant issue identified with data analytics in developing nations concern over the importance of the data in, its representativeness, its unwavering reliability and also the larger privacy issues of using personal data.

Next, Big Data significantly help with the financial risk. As the Big Data allows Fintech industries to identify risk and opportunities of emergent technologies in order to provide efficient and sustainable financial services. Hence, the amount of data available online increases exponentially by the second whereas for the reason that their immediate availability, financial sectors has the opportunity in predicting the risk having quick reaction time to counter and increase the effectiveness of the sector.

Big data has made lots of significant role towards the risk management, primarily is credit risk. In Bank Islam, the application of using scorecard such as Statistical and Behavioural scorecards have help the bank evaluate client circumstances and manage to take preventive action (Thijs, n.d.). According to a survey by The Economist Intelligence Unit, businesses and entities reports that the most successful use of Big Data tools on the risk management activity is prevention of credit card fraud at the top and following by, the evaluation of credit repayment risk, analysing the liquidity requirements, regulatory compliance and reporting, also the least benefits is prediction on market trends. Big Data Analytics in banking sector helps early detection cases of high-risk accounts thereby helping in risk management thereby by reducing cases of frauds and defaults (Shukla, 2018). Following are operational risk. As it comes down to human error mostly the risk is high. Not every system provider can cater to complexity of Islamic Banking Products, therefore it needs efficient data integration and treat data integration very seriously as to cleanse and enrich the data. Big Datas potential power lies in its ability to integrate a variety of platforms into a single solution, which provides more control and information about client interactions, while improving security and confidentiality. As for market risk, there are plenty method to use such as Monte Carlo simulation which presents many possible developments as it requires huge amount of calculation, the big data plays role in this by covering all bases. Big Data allows for better business and market simulations and predictions, such as interest rates, exchange rates, liquidity, and raw material prices. Not only that, big data too has redefined risk and create more business models and opportunities.

Nonetheless, the issues related to risk management in Islamic financial institutions requires significant attention. This is because some risks arise due to the nature of Islamic financial institutions themselves. Big Data applications have great potential, but they are still in the initial phases of development and implementation. The availability of those data cannot solve every single problem, and in fact, big data poses as many technical challenges as well as opportunities for organizations and regulators. The talk about using Big Data models for risk management was more at an academic level and less in practice. Various institutions need to keep up with smaller, more agile companies and peer to peer models.

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