The reactive approach of combating fraud using just rules and blacklists to build effective defences is no longer good enough.
Instead, organisations require a focused approach that incorporates data and analytics where predictive algorithms and machine learning can be leveraged to detect fraud.
For instance, banks now use algorithms and machine learning to profile how their customers are paying and moving money, asking a wide variety of questions.
If customers are banking through the same device and operating system as they usually do?
If the system language settings are not suspicious when cross-checking it with geolocation? Are customers sending a lot of transactions to high-risk countries?
Are small-value transactions popping up in places the customer has not been before?
While one of them might only suggest a blip, once a pattern starts emerging then the financial institution must start thinking of taking action to suspend activity and forward this for further investigation to protect the client.
Advanced algorithms and machine learning technologies enable anti-fraud systems to make these decisions in milliseconds, in time to approve or decline a payment or application.
Changing fraud environment
Of course, fraud risk is escalating for banks and other financial services providers.
Besides the proliferation of data breaches, raise of fabricated or compiled identities, considering the growing number of Internet and mobile payments, industry observe growing phishing and social engineering schemes often combined with malware or remote access trojans.
In that new digital fraud era, it is becoming extremely important to have the right information in place and to feed this into advanced technology giving the power to spot suspicious behaviour quickly.
Fraud detection in practice
In recent years, financial institutions have been working with technology firms such as SAS to gain a cross-channel view of behaviour and expand use of machine learning, often leveraging additional digital information about devices, geolocation, and behavioural biometrics.
At a fundamental level, addressing financial fraud in today’s digitally connected environment requires an expanded use of data, dynamic rules and adaptive models, and a multilevel approach to both detection and prevention.
Using a solution that can provide the technology and analytics to address all types of fraud becomes an appealing opportunity, allowing for more sophisticated detection methods, reduced costs, and increased efficiencies.
Here, SAS provides with its Detection and Investigation package for Banking enhanced fraud detection and improved operational efficiency while decreasing the total cost of ownership.
SAS gives ability to leverage multiple techniques such as supervised and unsupervised machine learning, text mining, anomaly detection and network analytics, and some more, to detect and prevent fraud at the individual transaction, account, customer, and network level.
Alerts are scored and prioritised based on severity, then can be routed to investigation units, where investigators perform more in-depth reviews to determine if the transaction or application is really fraudulent.
SAS gives financial institutions the flexibility to configure the system to meet their specific needs, as well as update models and adapt the system to address changes in fraud trends whenever necessary.
Combatting money laundering
Data and analytics can help to safeguard against more than just fraud. Algorithms and machine learning can also be used to effectively fight money laundering, which has become a growing threat given the availability of modern ways to store and transfer funds.
Moreover, analytics can help companies identify people and entities that are subject to international economic sanctions. In fact, leveraging automation and analytics is essential given the amount of textual data that must be addressed.
And because many new electronic payment methods are processed now faster than ever, it is mission-critical to do this kind analysis in real-time and at scale.
A complex web
But given the complexity of financial fraud, technology must identify cross-brand and product fraud by viewing customer accounts and transactions for all lines of business in one consolidated view.
This is where a visualisation interface is key for users to investigate linked entities and banking fraud crime rings that they would otherwise miss.
Sophisticated network analytics capabilities and social network diagrams must be integrated into a detection and investigation solution to provide financial institutions with a better understanding of new fraud threats, so they can prevent substantial losses early.
Ultimately, algorithms are only as good as input data and as good as they are trained. If not properly developed or outdated they can generate many false positives, causing an issue for organisations with limited resources and limited time.
It is therefore critical to embrace an integrated and effective detection and investigation solution that is cognisant of these and other issues.