How to manage risk in the era of customer experience
August 28, 2018
Vendor View: Andrew Davies, VP, Global Market Strategy, Financial Crime Risk Management at Fiserv
Delivering an excellent customer experience and managing risk are among financial institutions’ top priorities. Unfortunately, customer experience sometimes suffers for the sake of security, a trade off that is increasingly difficult for financial institutions to balance in today’s experience-focused world.
The key to tackling—and eliminating—this trade-off is the development of a deep understanding of individual customer behaviour and learning from this behaviour over time.
Financial institutions and other consumer-facing organisations can intelligently use data and technology at their disposal to strike a balance between accurate and effective fraud management, and convenient, frictionless consumer experiences. Doing this effectively, financial institutions can ensure they are providing customers with a seamless banking experience, helping to retain existing and acquire new customers, and ultimately keeping competitors at bay.
People expect a secure, seamless and convenient banking experience, including the ability to complete transactions in real time. However, real-time comes with some risks.
As the speed of money movement around the globe increases, so too does the speed with which payments can be hijacked or fraudulently initiated by sophisticated criminals. Consequently, financial institutions must be able to manage the risk associated with each transaction in real time to protect their customers—without disrupting the customer experience or transaction process.
Advanced analytics, machine learning, and the customer relationship
Balancing a seamless customer experience with effective risk management is an ongoing challenge. Consumers are less likely to adopt new financial technologies if they are worried about security. On the flip side, they might consider moving to a different provider if they encounter what they believe are undue hurdles or hassles in their banking interactions.
For example, 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. False positives such as this one are a significant industry issue that can be mitigated with greater knowledge of customers and their transactions tendencies, and better technology to effectively apply that knowledge.
There are emerging technologies that can help financial institutions manage and monitor fraudulent behaviour while potentially benefitting the customer experience. The incorporation of advanced analytics and machine learning, which enable processes to become “smarter” and more refined over time, into technology solutions is enabling financial institutions to better detect and identify instances of financial crime.
Biometric capabilities are another example of how customer-facing interactions can simultaneously be made more seamless and more secure. Biometric authentication can enable access to financial services in seconds with a higher level of security than can be achieved with passwords. Location-based transaction monitoring can deliver similar results, running in the background to help identify suspicious transactions without impacting the user experience.
By leveraging these technologies, financial institutions can not only authenticate and monitor individual transactions, but also factor in information on the customer relationship and contextual data pertaining to the transaction. Greater data aggregation and analysis through machine learning helps financial institutions more accurately detect potential fraud and money laundering activity. This creates a win-win, as financial institutions can focus on investigating true instances of fraud and money laundering, and legitimate customers can carry out their transactions without disruption.
Data can be used to better understand customers and gain a clearer picture what is typical (or atypical) for them. Financial institutions can leverage the data they have access to and use analytics and predictive techniques to facilitate real-time detection of fraudulent behaviour.
Collecting data enables financial institutions to better identify fraud by providing the ability to create an in-depth analysis of what it looks like and how fraudsters target customers and employees across organisations. This enables financial institutions to better recognise unusual behaviour and increase the likelihood of pre-empting and preventing fraudulent behaviour.
The value of data lies in its application. Analysis against broader sets of information, such as customer behaviour and other information from data sources across the industry, can reveal patterns that improve the ability to differentiate between normal activity and fraudulent transactions. Because of this intelligence, financial institutions can reduce false positives and strengthen their customer relationships while staying one step ahead of the competition and the fraudsters.
Knowing the customer is paramount to successfully managing risk—without sacrificing the customer experience. In a world that increasingly demands financial services to move at the speed of life, data analytics, machine learning, biometrics and other advanced technologies are more valuable today than ever before; sophisticated, real time analysis of customer interactions and transactional data provides financial institutions a more holistic, view of each customer and a better ability to detect and prevent fraud without sacrificing the customer experience.