Stay ahead of fraud with financial crime risk management
Chris Oakley | Fraud subject matter expert (SME) at Featurespace
September 26, 2022
Like many things in today’s economy, the cost of fraud is increasing. Financial services and lending firms in the US are incurring fraud-related expenses that are significantly higher than before the pandemic. Additionally, the 2022 Australian Payment Fraud Report shows that fraud on lost and stolen cards increased by 9.3% to $28.9 million and card-not-present (CNP) fraud increased by 7.6% to $452 million.
Costs are rising in part due to the complexity of detecting and preventing fraud across an ever-evolving payments space. Traditional manually programmed rule sets and static fraud models no longer provide sufficient risk protection. With a broader array of converging data sets to manage in fraud detection, financial institutions, acquirers, issuers and payment processors must explore new financial crime risk management (FCRM) strategies that utilize more versatile tools and technology.
The rise of financial crime
Financial crimes threaten the security of economic systems around the world and range from simple operations involving individuals to complex undertakings led by large criminal enterprises. Better controls and government regulations have improved security for financial systems but do not prevent crimes from occurring altogether. When faced with obstacles to their usual targets, criminals simply set their sights on other vulnerabilities in the payments process.
For example, an emphasis on strong customer authentication brought about by PSD2 within the European Union has been successful in lowering incidents of card not present fraud; however, this has led to an increase in other types of payments fraud. The advancement of real-time payment schemes such as Faster Payments in the UK have increased consumer convenience but made authorized push payment (APP) fraud more common.
Financial crime detection and prevention is challenging for financial institutions. Data analysis and implementation of measures to meet mandates like PSD2 require more time and resources than many financial institutions can afford. Coupled with the need to address increased competition, inflation, a potential recession and general market instability, institutions need a way to achieve greater fraud risk protection with less effort and cost.
Real-time fraud protection is crucial
As fraud continues to evolve, financial institutions, vendors, software and systems must adapt to changing circumstances and new threats. The growth in real-time payments demands real-time fraud management for banks. While payment card companies are getting smarter about detecting fraud, intercepting fraud during the fraud journey remains challenging.
With financial crime risk higher than ever, institutions need to move beyond a traditional reactive approach to fraud to a more proactive one. Fraud management systems must be able to identify true fraud from false positives and customer impact, which can be as or more damaging and costly than actual fraud.
Using adaptive behavioral analytics and machine learning to stop scammers
General fraud-detection criteria aren’t effective against new and emerging scams. Instead, knowing when a customer’s spending behavior is out of character provides a better path forward. Rather than using blanket fraud-detection criteria, adaptive behavioral analytics creates customer profiles based on payment activity and frequency. These profiles are then used to flag fraudulent activity before it occurs.
Adaptive behavioral analytics enhance the capabilities of machine learning through automated, self-learning algorithms. By reviewing customer account activities, anomalies can be identified in real time. Adaptive behavioral analytics and machine learning combined can help organizations predict, prevent and minimize the cost of fighting financial crime by increasing the speed of fraud detection while simultaneously minimizing friction in the payments process.
Adaptive behavioral analytics more accurately identifies real fraud from activity that appears suspicious but is legitimate. This reduces false positives and helps FCRM teams be more efficient.
Choosing the right partner to help manage fraud risk
Fraud management for banks is no simple affair, and engaging a third party can ease the burden of compliance and costs. But working with a vendor focused solely on identifying fraudulent transactions isn’t enough. The complexities involved in financial crime require a partner with the expertise, technology and reach to adapt to a shifting landscape of risk.
The new partnership from Featurespace and FIS® helps financial institutions, acquirers, issuers and payment processors stop scammers and fraudsters by utilizing adaptive real-time individual behavioral analytics and machine learning that adapts to changing behavior. Using real-time transaction monitoring, Featurespace’s ARIC™ Risk Hub blocks on average 75% of fraud attacks as they occur with a significant reduction in false positive rate against incumbent systems.
ARIC Risk Hub learns and adapts to the slightest changes in behavior, comparing each event to the profile in milli-seconds to determine any anomalies. By developing profiles of “normal” customer behavior, the solution more accurately identifies scams or out-of-normal behavior in real time and prevents payments friction.
Through initial profiling of the risk of the transaction, ARIC Risk Hub can initiate third-party providers’ API calls to enrich the data that supports decisioning of the transaction such as one time password and two factor authentication. This strategic approach to third-party data enrichment reduces costs and helps institutions improve their commercial position.
Another advantage of ARIC Risk Hub is that it integrates with a wide range of third-party enterprise systems to provide insights based on a 360-degree view of the money movement within customers’ accounts. A unified user dashboard and workflow provides multiple levels of reporting to help increase operational efficiency.
To learn more about how adaptive behavioral analytics and machine learning can enhance your organization’s financial crime risk management strategy, visit Featurespace.