Where customers see convenience in banking online and with their mobile devices, fraudsters see new opportunities to launch more sophisticated attacks on financial services and insurance (FSI) organizations. The stakes are high, too. It’s estimated that global money laundering transactions comprise 2 to 5 percent of global GDP, equivalent to $1-2 trillion.1 Detecting and preventing these transactions requires an innovative approach to data analytics. However, a recent report by Cloudera revealed 60 percent of IT and cyber security organizations said a key cyber security challenge they face is the limitations set by their technologies.2
As a financial services business leader, driving your organization’s response to fraud and cybercrime is one of your top priorities. To counter such sophisticated attacks, banks must be able to deploy innovative, analytics-driven technologies that can both detect existing threats and help to anticipate new ones as they emerge.
Detect and help prevent fraud with a 360-degree overview of your data
The rigid, rules and silos-based approach to data management of days past simply won’t be enough to prevent your organization becoming a victim of fraud. That’s because this traditional method denies you the holistic insights you need to make connections between customers or events that, at first glance, may seem unrelated. When integrated however, evidence may emerge of collusion between claimants, or a pattern of fraudulent activity that had previously gone unnoticed.
This can be achieved using a data lake, a centralized data platform that – dependent on security restrictions – can enable you to view and access all data, from all sources, and in any format. Once your data lake is populated, artificial intelligence (AI) and machine learning-based technologies can be used to quickly channel your data into meaningful, real-time fraud detection insights. In contrast to a rules-based approach, which uses a fixed set of criteria to decide if a transaction is fraudulent, AI systems are more flexible, considering as much data as possible. By analyzing data from multiple sources, AI systems can ‘join the dots’ across your organization and flag suspicious behaviors your staff may not be aware of.
After just 10 weeks of data processing using an AI platform, one financial institution in the US located three potential gangs of fraudsters. On closer inspection, it became clear these rings were part of a larger ring that included 38 claims and 42 participants.3 On these 38 claims alone, the company has paid out a whopping $400,000.3
You can find about more about the steps required to deploy a data lake in this recent white paper from Intel.
You can’t afford to waste time
Fraud detection is also incredibly time-sensitive. The longer a breach goes undetected, the more your organization risks losing money – and credibility.
Deploying high-performance analytics - such as those offered by SAS and Cloudera - means you can monitor more risks in less time, and put more time into emerging threats and detect fraud faster.4 Not only that, but simplifying data preparation saves your organization valuable time and money by reducing the demands on IT staff, and ensuring data quality functions are handled by those with the necessary expertise.
Protect your customers with biometric security
With passwords, swipes and signatures simply not up to the job of protecting today’s always-on consumers, consider ways you can offer innovative protection methods that keep customer data safe without causing inconvenience. As 60 percent of mobile malware is designed to capture financial information on your device, it’s clear fraudsters have adapted to the shift in consumer preferences.5
Intel, as part of the FIDO Alliance, is working with EMVCo – the consortium of major credit card companies - on Intel® Online Connect, a product that allows two-factor authentication on devices such as laptops.6 For enterprise PC users, Intel® Authenticate offers a range of multi-factor authentication options.7 This enables your business to use multiple tailored options to verify a user’s identity, including Bluetooth proximity and location detection.
Many financial organizations are also looking to fingerprint, facial and voice authentication systems. Citibank has already collected and registered the voiceprints of around 250,000 of its customers.8
While not foolproof, these technologies are being refined continuously. It may not be long, for example, until you’re withdrawing cash at an ATM using an iris scanner rather than a debit card.
In the fight to prevent fraud, innovation is an organization’s greatest weapon. Staying ahead of cyber criminals requires creativity, and a comprehensive understanding of your data to ensure you’re leaving no stone unturned.
To find out more about how you can use the cloud to bring together your data to help fight fraud and money laundering, take a look at this recent white paper from Intel.9 10 11
Product and Performance Information
Benchmark results were obtained prior to implementation of recent software patches and firmware updates intended to address exploits referred to as "Spectre" and "Meltdown". Implementation of these updates may make these results inapplicable to your device or system.
Software and workloads used in performance tests may have been optimized for performance only on Intel® microprocessors. Performance tests, such as SYSmark* and MobileMark*, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit https://www.intel.com/benchmarks.
All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest Intel product specifications and roadmaps.