• Andrew Davies, VP, Global Market Strategy, Financial Crime Risk Management at Fiserv


• 11.12.2018 11:15 am
undisclosed

Delivering an excellent customer experience and managing risk are among financial institutions’ top priorities. Notably, these priorities are solidly linked: as life moves faster and new technologies are introduced to help make our financial lives easier, the risks also grow. When money moves quickly there is less time to catch fraud, and proliferating channels mean there are more entry points to monitor.

Preventing fraud is an end goal of risk management, but false positives can be almost as disruptive for customers as fraud itself.  If a legitimate transaction is flagged as a potential instance of fraud and halted, the customer may at a minimum be annoyed, and at worst, be left in an extreme financial bind. Risk managers have little room for error when evaluating risks – and as a result, many are looking at ways they can improve processes.  

Below are recommended approaches financial institutions can adopt to manage risk in the era of customer experience:

Making risk management ‘frictionless’ 

The way many institutions are approaching risk management is changing.  Though the discipline has evolved as a standalone function, risk management is now being integrated into every area of an institution’s operations.  It is important to have an adaptable and pragmatic approach that takes customer impact into account when it comes to balancing risk and opportunity. Capabilities such as geolocation are allowing more risk monitoring to take place behind the scenes, while biometric authentication is enabling more streamlined customer interactions that are simultaneously more secure. As more and more financial interactions take place entirely online, financial institutions will benefit from automating more aspects of risk management with emerging functionalities such as robotic process automation, through which analysis is refined by learning over time. 

Drawing on a wider range of internal data

The increasing automation of risk management processes means financial institutions can apply a wide variety of risk models to transactions in real time. It’s not just the customer that benefits from this increased velocity: institutional risk managers can also assess how different models are performing and make adjustments accordingly.   

They can also draw on a wider range of seemingly disconnected information sources to inform and validate models – from device-level data to card usage trends.  This broader range of internal data sets can help validate rules and identify anomalies, helping risk managers continually sharpen their models.  This agile approach to risk management can facilitate the identification of shifts in fraud patterns, helping institutions stay ahead of criminals.

Getting ahead of the curve with machine learning

Even the best risk models cannot cater for everyeventuality.  Consider ‘money mules’, one of the fastest-growing fraud categories.  Money mule activity went up by 11% between 2016 and 2017, according to CIFAS.  The people co-opted by criminals to launder money through their accounts in this way may be otherwise legitimate customers, who elude ‘conventional’ risk models.  Yet more predictive techniques, such as machine learning and the previously mentioned robotic process automation, could well identify money mule activity: even though each individual transaction may appear genuine, the overall trend is suspicious.

Unleashing ‘bigger data’, or the power of consortia

The financial sector has a long history of data-sharing across institutions to help identify potential credit and fraud risks.  In the continuing mission to manage risks at the speed of life, institutions can benefit from deeper ties across the industry.  This is especially important for some faster growing types of fraud, such as asset conversion, in which a customer sells an asset (often a vehicle) for which they have not fully paid. This type of fraud is more readily identified when institutions share records.  A consortium of institutions can also pool their resources to build shared databases of ‘bad’ devices, suspicious activity and other factors for which shared intelligence is at a premium.

The ability to continuously build and refine industry-wide intelligence can decrease false positives, allowing financial institutions to strengthen their customer relationships while helping ensure the industry is abreast of the latest fraud trends.

Looking to the future

Risk management can never stand still – as the fraud landscape evolves, so too must risk models, business processes, and supporting technologies.  Institutions that leverage new technology trends can enhance the customer experience while also enhancing security, avoiding many of the traditional trade-offs between these areas as they venture boldly into the era of customer experience.