Effective risk management and a seamless customer experience have not always gone hand in hand. Measures such as tokens, challenge questions and transaction holds are often regarded by consumers as an inconvenience, despite their necessity for security. Fortunately, advances in technology are eliminating the trade-off between user experience and risk management, delivering experiences that are both seamless and secure.

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Managing risk in real time

Consumers of financial services, both retail and commercial, expect a secure, seamless and convenient banking experience, with real-time transactions and access to solutions and services available anywhere and at any time. However, real-time interactions involve unique risks.

As the speed of money movement and settlement around the globe increases, so too does the speed with which payments can be hijacked or fraudulently initiated by sophisticated criminals, not to mention the speed with which money can be laundered through the financial system. Consequently, financial institutions must be able to manage the risk associated with each transaction in real time to protect their customers and their reputation – without disrupting the customer experience or transaction process.

Utilising data analytics and machine learning

Balancing effective risk management and a seamless customer experience is a constant challenge. Consumers are less likely to adopt new technologies if they are worried about security. On the other hand, they might consider moving to a different provider if they encounter what they believe are unnecessary hurdles 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 are a significant industry issue that can be mitigated with greater customer knowledge 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. Advanced analytics and machine learning, which enable processes to become “smarter” and more refined over time, are a few ways financial institutions can use technology to detect and identify instances of financial crime.

By leveraging these technologies, financial institutions can not only monitor individual transactions, but also factor in information on the customer relationship and contextual data pertaining to the transaction, such as geographic location and customer history. Greater data aggregation and analysis through machine learning can help financial institutions more quickly and accurately identify the patterns, behaviours and anomalies that can be a flag for fraud. This benefits both financial institutions and their customers, as financial institutions can focus on investigating true instances of fraud and money laundering, and legitimate customers can carry out their transactions without disruption.

Knowing the customer

Knowing the customer is paramount to successfully managing risk without sacrificing the customer experience.

Data is a powerful tool that can be used to better understand customers and get a clearer picture of typical (or atypical) behaviour. On its own, data can be useful. However, analysis against broader sets of information within the institution as well as other information from data sources across the industry can improve the ability to differentiate between normal activity and fraudulent transactions. For example, The Australian Transaction Reports and Analysis Centre (AUSTRAC) has recently released a range of new case studies detailing the indicators of money laundering and other financial crime in a bid to help fight against financial crime. Continuously building and refining such intelligence can decrease false positives – not to mention their potential to negatively impact or inconvenience the customer.

In a world that increasingly demands financial services move at the speed of life, machine learning and other advanced technologies can enable financial institutions to operate more efficiently without taking on more risk. The sophisticated, real-time analysis of transactional and customer data can provide financial institutions a more holistic view of each customer and a clearer understanding of unique behaviour patterns. As a result, financial institutions can deliver a customer experience that is both more seamless and more secure, a true win-win.

Andrew Davies, VP, global market strategy, financial crime risk management, Fiserv

 

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