• Richard Cooke, UK Business Lead at Keepler Data Tech


• 19.06.2023 12:00 pm
#data #fraud

Data is now the lifeblood of businesses operating in the world of financial services.

Long gone are the days when bricks and mortar were king, with all activities and interactions between service provider and customers being carried out in branch. Now, face-to-face interactions are a rarity, with products and their associated support services provided almost entirely digitally.

All of this, of course, creates an enormous digital footprint that represents a treasure trove of data for companies to gain insight into how their customers behave, offering huge opportunities to create value.   

Such is the amount of data created, captured, copied, and consumed in the world, we now talk about the size of the ‘datasphere’ in zettabytes, with one zettabyte equating to a trillion gigabytes. According to the IDC, by 2025, the datasphere will expand to 163 zettabytes.

What is clear is that those financial services companies that are able to extract insights from this ever-growing pool of information will be supremely well-placed to offer superior customer experiences, drive better business decisions, and operate with greater agility and resilience. 

Opening doors for fraudsters  

At the same time, however, organisations also need to be aware of and respond proportionately to the threat posed by cybercriminals and fraudsters.

As the use of data within financial services organisations intensifies, so too does the opportunity for criminals to exploit the situation for malevolent gain, especially when that data is spread across operations hosted in multiple countries.

This is partly owed to increased complexity. Operating across multiple countries introduces variations in regulatory frameworks, financial systems, and legal jurisdictions, making it harder to detect and prevent fraudulent activities.

Adding to this, global businesses often engage in transactions that involve different currencies and jurisdictions, creating opportunities for fraudulent activities to exploit loopholes and inconsistencies.

Meanwhile, such transactions are reflective of a diverse customer base. Serving customers from various countries and cultural backgrounds can make it challenging to establish consistent fraud detection measures and identify patterns across disparate regions.

Failure to detect fraudulent activities can lead to serious consequences. In addition to significant monetary losses and the associated impact on shareholder value and overall financial stability, successful fraud incidents tarnish brand reputation and can erode customer trust and loyalty.

Regulatory bodies may also impose fines and penalties on financial service businesses that fail to adequately prevent and address fraudulent activities, while legal actions and lawsuits can further compound the financial and reputational impact.

This makes the need to manage data across borders even more important. However, once again, data teams in businesses with a multi-country presence face a plethora of challenges.

Data silos, for example, can result in data inconsistencies, leaving decision-makers with an incomplete picture on which to base important strategic calls. Furthermore, as well as discovering silos, data teams also need to develop an understanding of why they appear. For example, it could be a “spaghetti code” problem, a people or culture matter, or some form of operational or process-related flaw.  

Further complexity can arise from the variety of data sources and applications leveraged by operations teams in different countries, as well as the formats such data are presented in. And when it comes to measuring data quality, there will often be different measurements used in different places.

How should data teams respond?

All of these factors contribute to a dynamic where managing data in multi-country businesses is a complex task that is prone to error, and can thus present opportunities for fraudsters to take advantage.  

While eliminating the risk of fraud is a near-impossible feat, there are several actions data teams can take to reduce the likelihood of fraudsters succeeding.

One step is to develop advanced analytics models using machine learning to detect patterns and anomalies indicative of fraudulent activities – these models can analyse transactional data, customer behaviour and external data sources to identify suspicious patterns in real-time. This should sit within a proactive monitoring and risk assessment regime, which will enable firms to continuously monitor transactions and customer activities for potential fraud indicators. Data teams should also consider performing regular risk assessments to identify vulnerabilities in existing processes and systems and implement necessary controls.

Data security measures, including encryption, access controls and regular audits, should be prioritised, as protecting sensitive customer information reduces the likelihood of data breaches and unauthorised access, thus limiting opportunities for fraud.

Communication and collaboration are another critical factor behind reducing fraud-related risks. Data teams should collaborate with other departments, such as fraud prevention, compliance and legal, to share insights and intelligence – by pooling knowledge and resources, organisations can proactively identify and mitigate fraud risks more effectively.

At a broader scale, organisations need to educate their employees about fraud risks, prevention techniques and the importance of reporting suspicious activities. Doing so will help to build a culture of awareness and vigilance that will help to empower employees to identify and report potential incidents of fraud.