How big data can assist banks in risk management
Posted: Mar 13, 2020
Big Data is increasingly going to help companies make decisions.
Every day, banks deal with large amounts of customer data and financial transactions carried out by them. Therefore, leaders of the banks' technology departments have been looking for new solutions to handle this data.
An alternative that serves this purpose is Big Data, which can help banks reduce dropout rates, improve customer service and satisfaction, in addition to helping to combat fraud and improve risk management.
Now, the focus is precisely on talking about this last point: credit concessions. Especially those that can present a very high risk for financial institutions - as examples, we can mention mortgages, real estate financing and credit cards. And when the possibility of human failure is considered in the risk analysis, the danger is even greater.
In this sense, Big Data provides, in real time, data of great relevance in a much more precise way and generates reliable information from the crossing of data - this information can include the customer's buying behaviour pattern, his current and potential assets, default trend and public social media data. All to ensure a greater chance of success in the concession of high-risk products and prioritize the economic and financial balance of the banks.
Risk control self assessment for banks involves combating fraud, one of the most used solutions by banks to analyse data. The permanent monitoring of data centres, data network and systems bank can and should be done through data mining, since managing threats via traditional systems does not have more risks. In practice, this process crosses all access information on an unimaginable scale to human understanding and in real time, generating usage patterns for each client. Under any sign of deviation, access is blocked. In addition, customers and banks receive alerts, which inform the suspected attempted invasion.
Among these frauds is money laundering, which can be mitigated with diagnoses based on pre-established scenarios that relate to information stored in legacy systems, without including algorithms or predictive analysis, so that detecting deviations between so many variables requires effort considerable manual. This also occurs with fraud detection, which depends on reconciling a significant amount of data from customers, operations and employees.
By helping banks do this, Big Data revolutionizes traditional processes and can save costs by reducing the potential for risk. The technology not only monitors past events, but their analysis accesses multiple sources and combines them to identify a pattern of behaviour, resulting in predictions that allow for a much faster reaction.
In risk management, the bank can also make use of a Security Operations Centre (CyberSOC), available 24 hours a day, seven days a week. It is made up of a group of security experts who show the institution which data is most important and should be prioritized, as well as ways to reduce the risk of attack. In addition, it controls traffic flows, identifies exceptions and acts decisively when an attack occurs, with policies based on geolocation and blacklists, in order to reduce the risk of attacks via botnets, malware and spam hosting sites. Their robots detect attacks and divert traffic to centralized filtering servers where they can be blocked.
In other words, Big Data analysis enhances the visualization of threats in real time and streamlines the response to incidents and post-events, which allows discovering and identifying malicious actions with behaviour analysis.
Thus, through a better understanding of information, it is possible to identify areas where duplicate efforts are made and suggest improvements in processes, in addition to reducing capital requirements, understanding employee behaviours and identifying opportunities for improvement in internal communication or defining hiring profiles.
Given this, it is possible to conclude that, by having control of information, banks can map their own future with more security. Those who invest in it and can extract a manageable value from their data will have a broad competitive advantage. Data has become an important commodity for many different industries because it can reveal insights that would otherwise be inaccessible. Big data allows banks to see how their risk management will work in the near future and thus allows them to mitigate risks proactively. It is easy to see why so much investment is being done by banks in big data.
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