Data Analytics and Digital Security: Using Insights to Uncover Key Details

According to a recent global study, 49% of international organizations report that they have experienced some level of economic crime within the past two years. At first glance, that statistic offers hope that 51% of organizations have managed to escape falling victim to the detrimental effects that financial fraud can cause. Yet, the reality is that many more companies might have been under a similar attack, although without the means to monitor or even realize that it was occurring.

To that end, data analytics is emerging as a key way for companies to keep a closer eye on not only their operational information, as it enters and leaves their doors, but also their financial details as well. One of the earliest adopters of this technique has understandably been the banking sector, which transacts vast amounts of confidential and time-sensitive financial data every day.

Recently, forward-thinking banking institutions have adopted technologies including Artificial Intelligence (AI) and blockchain to make their data architecture more robust. As they continue to look for ways to ramp up their internal networks, these institutions will require smart data analytics to organize and assess the information they’re now able to acquire. One way these systems are already being harnessed? To help banks better identify and respond to customers that may be of concern in areas regarding financial fraud or other forms of economic crime.

While most jurisdictions maintain a lock-tight hold on client data, especially when it comes to financial affairs, banking institutions are realizing that new conversations must be formed around when this data is able to be shared with individual banks or with law enforcement agencies when required. The amount of Big Data gathered by the minute is vast, and there could be critical details buried within those transactions that could make or break a particular case.

The Path to Shared, Regulated Data

For this new application of data analytics to be effective, there has to be international agreement among all key stakeholders involved, including banks, regulators, law enforcement personnel and policymakers. To date, there are already such partnerships set up within the United States, the United Kingdom, Hong Kong, Singapore, Canada and Australia. Within each agreement, the impetus is for all leaders within these sectors to remain abreast not only on new criminal methods used to carry out financial crimes, but also on new technology being created and implemented to prevent and fight it. By melding public and private interests, the partnerships bring every necessary and important person to the table, helping to ensure that no detail goes unnoticed.

It’s also an effective way to catch criminals at their own game, as many of them patronize various institutions as they launder and move money around illegally. Bringing everyone together allows for a more thorough investigation and more targeted efforts. When team building is the targeted initiative, there is a greater likelihood that all key players involved will be active in achieving the targeted outcome.

Global Considerations to Keep in Mind

The idea of a public/private coalition to curb economic crime with stronger data analytics is an ideal one in theory. It’s also proven successful in real-world applications, as is the case with the U.K.-based Joint Money Laundering Intelligence Taskforce (JMLIT), which has already led to the arrest of 63 individuals and the reclaiming of millions of British pounds.

Yet, the notion is not without its challenges, primarily its financial hurdles. To ensure that data technology is put to the best use and not squandered, many institutions follow standards and regulations for combating financial crime set forth by the Financial Action Task Force (FATF), an inter-governmental body that has developed a series of recommendations to help organizations protect their money and its use. Moreover, for these global partnerships to be successful, there has to be an agreement on how customer data can be used, an act that can prove difficult considering that many countries have their own laws and permits around this issue.

Policymakers must also remember that customers are naturally wary of the way their financial data is captured and shared. In fact, a recent survey reveals that two-thirds of consumers are wary of shopping online for fear that their personal data may be compromised or intercepted. In the digital era, where you can shop online for anything from groceries to kitchen remodeling supplies, it’s imperative that organizations understand how to mitigate these concerns. Not only is taking proactive measures good for business, but it also helps to ensure that the backbone of these companies is one of integrity and trust. These two measures can go a long way in boosting the credibility of any institution, financial or otherwise.

For this movement to be successful, everyone must be on board with the process, including those who stand to be the most impacted by it -- the clients themselves.

The Case for a Global Data Analytics Platform in the Financial Sphere

Moving forward, though some countries have already engaged in partnerships similar to those discussed herein, there remains to be an overarching combined-unit entity that’s emerged as a leader in this space. To create a successful and sustainable one, companies will need to be as transparent about their aim as possible, reminding customers, stakeholders and regulators alike that their goal in adjusting how client data is shared is solely to improve the security of that data.

As they bring the audience along for the journey and remain open about its challenges and setbacks, financial leaders can lean on evidence acquired that these types of partnerships can indeed be effective. For years, compliance with pre-determined data regulations was the most common way that institutions maintained control of their sensitive information. Now, we understand that to prevent an increasingly tech-savvy criminal base from infiltrating legacy systems and compromising existing software, a new path must be taken. The financial services sector has seen major growth and innovation in the past year alone and a more robust and bigger-picture data analysis operation is the natural next step.

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