In the banking industry, trust is the largest bankable asset for any institution and its risk profiling happens to be the most volatile, complex, and regressional one among the non-systematic risks. We will explore the role of Big Data Analytics in catering a support mechanism to the industry. I will also share hypothetical scenarios for the audience and the potential of this advanced technology in harnessing the best practices.
Current Challenges
As a civilization, the commercial pursuit has been around since the very beginning but the connectivity witnessed in contemporary times has been a phenomenal change. Adding to it, the information is stored and shared in an unprecedented volume. No event goes unnoticed or vanishes in thin air due to these developments. The banking sector is one of the largest sets of accessible institutions of finance across the globe.
In the recent past, the great recession of 2008 is the finest example of malpractices in this sector that triggered huge dents on the global economy and the reputation at the same time. Once considered among the finance’s top players in the US, Bear Sterns was acquired by JP Morgan Chase in March 2008. Beneath the subprime mortgage crisis, the industry faced loss of trust’ which demotivated investors and all other stakeholders in general including the retail clients.
Having the employees talking ill of the company or dissatisfied clients bashing the brand on the online social media platforms can fetch heavy attention in some instances including national media and government authorities. Controlling this risk strategically will require acquisition, segregation, structuralization, and analysis for the optimal configuration of all business processes. This may sound easy going but this is a gigantic process since the volume of information for all the transactions, customers, historical data, market developments, socio-political changes, and international trade treaties are too large to be evaluated by normal technological tools.
Scope and Application of Big Data Analytics (BDA) in the Risk Management Process in Terms of Reputation Risk
Grievance redressal is one of the major areas for the application of BDA since the agitated customers are most likely to escalate the issues on social media and in their personal conversations. The banks need to keep a tab on the internal mechanism of the organization for assessing customer service quality. The data regarding every query and counteractions shall be stored in the company ERP with a detailed transaction entry. Having them will ensure quick response rates and problem resolutions owing to a quick investigation.
For instance, any internal security lapse causing a data breach can be detected by clubbing the data acquired from multiple sources like attendance reports, staff movement, internal communications, exchange of crucial data over official channels, and receipt of vulnerable data beyond the premises or beyond working hours. Running analysis will not only safeguard against such shortcomings, but it will also improve compliance management and lower the costs.
As a bank, it is necessary to monitor the online safety of the brand and visibility of the brand and continuously analyze the unpleasant reviews, feedbacks, and forum discussions. Even Google is now reviewing the mentionless (No-Follow) links policy and considering them as analytical parameters (source). Detecting such issues on time and approaching the situation systematically derails brewing contempt by the whole community based on a single incident.
BDA will also find great traction in monitoring the transactions made and pick out the suspicious activities of staff by interfacing the data accessed from payroll software solutions, various investment tools, debts, transaction history, lending track record, demographics, repayment frequency, and law-defying activities records. BDA enables coverage on all these spheres and builds a collective consensus backed with facts and figures. Operational risks harm the reputation of the banks as the procedures mostly don’t pass under scrutiny on a regular basis.
One of the major benefits of Big Data Analytics is the modelling and sensitivity analysis done as the scale of deflection is too small to be traced by normal analysis. The results developed by the BDA approach will also lay down the foundation of evolutionarily enhanced risk profiling parameters. The high-quality computational simulations also provide insights regarding the preventive measures well in advance.
Contextual structures of process parameters such as unfriendly staff members on a particular location, rude management practices, disdain from a particular employee cadre base, and frequent client complaints can be tracked down efficiently as these external expressions have links with internal practices. BDA tools allow banking companies to exercise control over the internal working methods to curb the threats related frauds and also ensure industry best practices.
What we can expect from Big Data Analytics in the Days to Come
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BDA will potentially provide a road map to deescalating complaints by tracing the problems from multifaceted scrutinies.
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All the factors that pose a threat to the bank’s reputation be it internal or external, will be highlighted promptly without any influence of involved parties.
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Client servicing teams will be able to detect the behavioral repercussions from the client end whether deliberate or unintentional for query solving purposes.
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BDA will also provide a road map for operations and sales teams in case of new products by using similarities with the past data.
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It will also index threats to the organization as a part of Reputation Risk profiling based on intensity, urgency, frequency, probability, and regulatory provisions.
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The credit policy and internal management policies can be integrated with external effects to contain any negative impacts in their initial stages such as employee stress issues causing staffing backlashes due to bad employer reputation. Moreover, as per factoHR.com, due to continuous investment in employer branding, the turnover rate can reduce by 28%, which helps in keeping a good identity in the marketplace.
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The banking sector may also experience early detection of some implicit factors causing unwanted dents on reputation like misuse of office and the personal misconducts in the organization by the higher authorities leading to whistleblowing.
The Ending Notes
It takes 20 years to build a reputation and five minutes to ruin it. – Warren Buffett
Market reputation is the most vulnerable yet effective attribute in the banking world and Big Data Analytics will act as a compass to safely navigate through Reputation Risks. Hence, we can expect the financial institutions to radically embrace advanced technologies like BDA and AI algorithms to be on the safer side due to their contributions in improving reputation risk management. Having said that, I also find the growing amount of information and its dispersion as grave concerns for the already pistanthrophobia facing the sector. Well, Big Data Analytics seems like a knight in shining armour and it will enjoy higher acceptance in the days to come.