Balancing risk management with a seamless customer experience

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

 

For quite some time, measures to mitigate financial risk have been taken at the expense of a seamless customer experience. Policies such as challenge questions or transaction holds, are often viewed by consumers as an irritation.

But with fraud tactics becoming increasingly sophisticated and evolving at great speed, the onus is on financial institutions to take necessary action. However, as criminal activities develop, so do anti-fraud solutions – meaning risk management and customer experience aren’t mutually exclusive. The result is a safe and user-friendly process for customers.

 

Risk in Real Time

Driven by an increased customer expectation for speed and convenience, banks are having to quickly manage the risks associated with real-time payments. With money moving faster than ever before, the speed with which payments can be hijacked or fraudulently initiated by sophisticated criminals also increases.

Criminals are becoming more sophisticated and therefore able to exploit any weakness. In order to satisfy the fast-pace needs of customers while ensuring criminal behaviour is kept at bay, financial institutions must employ advanced machine learning technology to manage the risk associated with each transaction in real time. And they must do so without disrupting the consumer experience or transaction process.

 

Smarter technology solutions

Providing a user-friendly banking experience while concurrently ensuring that risks are mitigated is a delicate balancing act. Although most consumers are keen to adopt new technologies to streamline their banking experience, this is far less likely if there is the faintest security concern. On the flip side, some consumers may choose to switch to an alternative bank if the customer experience is too complicated.

Taking as an example, instances where a legitimate transaction is flagged as a potential instance of fraud and therefore halted. The impact this has on a customer can vary from them feeling slightly annoyed, to them being in a serious financial predicament. False positives like this are a real issue in the industry, but one that can be mitigated with greater customer knowledge and better technology to effectively apply that knowledge.

One of the answers to this conundrum is the development of advanced analytics and machine learning anti-fraud capabilities. Such technologies can act as extremely effective prevention strategies through monitoring and managing fraudulent behaviour. In addition, these technologies don’t serve to lengthen nor complicate the user experience.

Advanced analytics and machine learning solutions become ‘smarter’ and more sophisticated over time, helping financial institutions identify criminal activities at great speed. Banks are also able to obtain information about the customer relationship and contextual data relating to the transaction, such as location and customer history. This data, coupled with machine learning capabilities, means that financial institutions can accurately identify anomalies in behaviour that may well be fraud. The heightened speed and precision of these solutions mean that customers are less likely to be flagged for false positives while banks can focus on real instances of financial crime.

 

Customer knowledge is key

The key to successfully managing risk while also providing a seamless customer experience is through understanding your customer. The monitoring and analysis of data enables institutions to formulate a picture of their typical customer. As more data is gathered, it becomes easier to spot atypical behaviour through anomalies in the data.

In addition to using data captured from within your own financial institution, banks can also benefit from working alongside fellow institutions by sharing data with peers. This can take the form of a data consortium, improving data integrity and fraud detection accuracy. This type of collaboration between financial institutions can improve the ability to differentiate typical behaviour from criminal behaviour. As banks continue to develop such intelligence building capabilities, fraudulent behaviours will be better spotted and customers will benefit from a more seamless user experience.

 

A seamless and secure future

In a world that increasingly requires banks to move at the speed of life, machine learning technologies can enable financial institutions to operate more effectively to manage risk in real time. Real-time monitoring and detection of transactions and user data can allow for financial institutions to have a more comprehensive understanding of their customers and their typical behaviours. Consequently, financial institutions are able to provide an excellent customer experience that is both seamless and secure.

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