Fighting
Fraud
Factually Determining Danger from Data
The Current State of Fraud
65
%
of organizations attributed the increase of payment fraud directly to the pandemic.
The COVID-19 pandemic had undeniably exacerbated the volume of fraud these past 2 years. The various social distancing mandates that governments globally imposed have caused people to move their daily activities online, including work, school, and shopping. This phenomenon opened up new avenues for fraudsters to exploit. According to AFP’s 2021 Payments Fraud and Control Survey, 65% of organizations attributed the increase of payment fraud directly to the pandemic.
Correspondingly, the cost, time, effort, and resources to manage fraud have increased, placing an enormous strain on fraud departments’ workloads. Financial institutions are most susceptible to the effects of fraud. Of the 74% of organizations that fell victim to payment frauds in 2020, 78% of those with revenues of at least $1 billion have experienced actual fraud attempts (successful and not).
This is not inconsequential, as organizations stand to lose about 5% of their annual revenues to fraud.
This shows that fraudsters now target large organizations, such as banks, more frequently. They are also incorporating methods such as social engineering, to increase their success rates. This is not inconsequential, as organizations stand to lose about 5% of their annual revenues to fraud.
Considering the intense scrutiny and regulatory environment that most financial organizations find themselves in, they may also be liable for negligence and may even need to reimburse fraud victims. Apart from direct financial losses, fraud can cause reputational damage and erosion of customer trust, hurting organizations further.
Expected Increase in Specific Fraud Risks
(April 2021 - April 2022)
References
2021 AFP Payments Fraud and control survey report
2020 Report to the Nations, Association of Certified Fraud Examiners
2021 The Next Normal: Preparing for a Post-Pandemic Fraud Landscape Report
CURRENT STATE
What are we working with
CONSTITUTING FACTORS
Existing solutions lack the depth of detection and scalability required to tackle the modern fraud landscape.
As fraudsters become increasingly sophisticated, the capabilities of fraud solutions will have to follow suit. It is critical to note that sophisticated fraud attacks typically come from fraud rings specializing in specific areas.
For example, credit card application fraud rings often target contact centers. While various fraud detection software solutions are available, many are resource-intensive from a personnel and operational cost perspective.
Furthermore, these solutions often struggle to scale with growing requirements, and scalability is crucial as organizations deal with larger attack volumes. In short, many existing solutions lack the depth of detection and scalability required to tackle the modern fraud landscape.
It's all in the data.
Data is one of the most crucial elements in insight generation, which is even more apparent in the context of fraud. Data helps identify fraud patterns by detecting transactional and behavioral anomalies and raising them as red flags. Moreover, interpreting and expressing data in multiple ways can help uncover oversights and anticipate problems before they occur
Despite its many uses, data comes with its own set of problems. Today, teams often deal with having too much data rather than too little. As the number of data sources available to fraud departments grows, so does the volume of data. It becomes impossible for any organization to manage all its data single-handedly.
Human vs Machine
Recently, machine learning emerged as the answer to consuming and adding value to data for various use cases in many industries. In fraud, machine learning can take over investigation tasks that usually take months or even years to perform, reducing them to days or minutes. Machine learning processes data using built-in algorithms and rulebooks to perform automatic detection and alerting. This helps organizations stop potential fraud while minimizing human intervention, saving fraud department valuable resources, and decreasing business costs.
It is in the interest of organizations to adopt a data-first approach to combating fraud.
Splunk Fraud Detection Case Study:
Insurance Company Adopts an Analytics-Driven Approach to Fighting Fraud
Aflac, one of the largest insurance providers in the United States, was facing an increase in the volume and velocity of security threats as it entered new markets and introduced new services. The company needed a new analytics-driven security approach to protect its customers, nearly 10,000 employees and brand reputation.
They selected Splunk Enterprise Security (ES) and User Behaviour Analytics (UBA). The entire implementation of the solution took only 2 weeks.
Aflac saved more than 40hours a month through automating report generation and blocked 2 million security threats, in a six-month period, that might have otherwise compromised the company. The UBA solution gave Aflac the ability to identify suspicious behaviour across its contractors and other business-related privileged access to its networks.
3 Essential Considerations
CONSIDERABLE SOLUTION
When Assessing Fraud Detection Solutions For Your Organization
Automation and Efficiency
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• What is the baseline skill to effectively operate the solution and understand the resulting reports?
• How many work hours do employees potentially waste doing automatable work?
• How much money does the organization spend on work hours contributing to the total cost of owning existing and potential anti-fraud solutions?
• How much data can the solution ingest, and what is the depth of the insights it can generate?
• Can the solution learn from the data it ingest?
• Does the solution address multiple use cases?
• Can the solution work across multiple domains?
• Can the solution work with existing deployments without extensive overhauling?
• Can new technologies be added to the stack without potentially conflicting with the solution??
ABOUT SPLUNK FRAUD DETECTION
Splunk delivers integrated enterprise fraud management software that quickly defines behavior patterns and protects enterprise information. In addition, Splunkbase enhances and extends the Splunk platform with a library of hundreds of apps and add-ons from Splunk, our partners, and our community. Customers can learn how Splunk Enterprise may be used to detect various forms of fraud using the example scenarios in Splunk Security Essentials for Fraud Detection.
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