User experience and security frequently find themselves at odds these days. For many financial institutions, a simple user experience means sacrificing security while better security creates inconveniences: multiple passwords, one-time codes, lockouts and so forth.
But in our experience-focused world, this tense trade-off borders on the unacceptable.
As institutions ramp up their demand for and use of digital services, they must mind both ends of the see-saw—bolstering security mechanisms while providing that coveted “seamless customer experience.” Fortunately, advances in tech have led to a deeper understanding of individual customer behavior. And that means superior security and experiences can finally go hand in hand.
Satisfying the need for speed—securely
Consumers want every action to accelerate as they manage their money, from making real-time payments to immediately accessing deposited checks. However, real-time transactions come with risks.
As the speed of settlement and money movement quickens, both here and abroad, so too does the velocity with which money can be stolen. As digital channels proliferate, we must monitor them all for the fraudulent transactions that sophisticated criminals launch. Consequently, financial institutions must manage the risk associated with each transaction as it happens. That protects customers without disrupting their experience or transaction process.
A decade back, transactions typically moved in batches. Payments took hours if not days to process, clear and settle. But faster money movement restricts the window to detect and stop bad transactions. Financial crime prevention takes on an increased urgency. All the processing and network steps demand a one-second completion time—at most. That includes, by the way, validation, accounting and fraud detection.
Consumers are less likely to adopt new technologies — particularly around payments—if they worry about security or perceive so-called unnecessary hurdles as they transact with their financial services provider. Studies show this can even impact consumer loyalty.
For example, if a bank flags and halts a legitimate transaction as a potential instance of financial crime, fraud or sanctions, the customer may at a minimum be annoyed and at worst land in a financial bind. False positives such as this represent a significant industry issue that better technology and a more thorough knowledge of the customer can mitigate.
Smarter crime detection
Emerging technologies such as advanced analytics and machine learning can help financial institutions manage and monitor fraud. These result in “smarter” processes that refine themselves over time. Machine learning can identify patterns of behavior from large data sources such as transaction-level data. Thus its role in crime detection and prevention shifts the process. What was once fraud and money laundering discovered via selected data sources and limited rules becomes comprehensive, continuous monitoring of data and behavior that pins down pattern deviations.
Critically, machine learning identifies predictive variables faster and converts them into detection models. The models can even take behavioral analysis to the individual customer level, which improves detection and false positive rates even as it enables regulatory compliance. Institutions not only get to monitor individual transactions, but also factor in customer relationship and contextual data that pertain to the given transaction. Greater data aggregation and analysis through machine learning also help financial institutions more accurately detect potential fraud and money-laundering activity.
The shift proves invaluable: Financial institutions can focus on true instances of fraud and money laundering while legitimate customers carry out their business without disruption.
Data’s dominant role
Of course, emerging solutions such as machine learning and advanced analytics depend on the accuracy, consistency and completeness of data across the financial institution. Many face a knowledge gap because they only see bits and pieces of each customer’s behavior patterns and activity.
Fortunately, data collected over the customer life cycle forges a complete customer profile. The components can include data from:
» within an organization
» industry groups through “know your customer” questionnaires during onboarding
» customer due diligence and transaction monitoring.
With this information, institutions can grasp the full picture and better spot unusual behavior that typically indicates fraud, tax evasion, human trafficking and money laundering.
Additionally, this data allows for more precise and valuable alerts, as well as better decision-making. Financial institutions can refine alerts based on predetermined detection scenarios, peer group activity and historical profiles. Such fine tuning will lead to more accurate alerts, upgraded operational efficiency and superior regulatory compliance.
Putting it all together: Smooth and secure at the same time
Only a robust fraud and anti–money laundering solution, powered by machine learning and advanced analytics, can pave the way for advanced data collection and analysis—particularly as regulations require institutions to capture more detailed ownership and “controlling person” information.
Sophisticated, real-time analysis of transactional, channel and customer data can provide financial institutions with a more holistic view of each customer— and a clearer understanding of unique behavior patterns. Financial institutions so empowered can deliver a better, more secure customer experience while managing risk.
Ultimately, this means the days of required trade-offs are over: allowing banks to better ply their trade and customers to trade in aggravation for gratification.
Andrew Davies, is vice president, global market strategy, Financial Crime Risk Management, Fiserv.
Jeannette Kescenovitz, who leads development of banking-as-a-service at Finastra, joins us on the BAI Banking Strategies podcast to share her views on how BaaS might grow its presence at U.S. banks and credit unions this year.
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