Citizens need to be at the heart of governments’ and public sector agencies’ fraud risk strategies. There are increasing reports of citizens receiving disguised phone calls—for example, from ‘tax authorities’ regarding pending tax bills or ‘immigration officers’ threatening deportation. In many cases, fraudsters persuade the individuals to make financial transactions or share financial information, resulting in losses. Fraud impacts people mentally and emotionally. It creates embarrassment and in some cases, victims—particularly senior citizens—stop using digital services altogether for fear of going through it all again.
As professionals who manage fraud risk, we focus a lot on financial recovery and detection. However, equal attention needs to be paid on supporting those impacted by fraud. We need to ensure that fraud-related insights and foresights are used to educate citizens on the types of fraud being committed. As we shape our defence against fraud, keeping citizens at the centre of our strategy is key.
Fraud impacts all businesses and industries, everywhere in the world. Financial services is one of the most heavily impacted; every year, the industry loses over US$1 TN to fraud globally.
Common types of fraud across industries include:
When it comes to the public sector, the government has a difficult task ensuring that:
To ensure that we have robust controls against fraud, we need platforms that facilitate private/public sector collaboration, through which learnings and best practices from one industry are shared with others. And through which fraud-related data can be shared across organisations, institutes and agencies.
Based on a KPMG survey analysing 750 fraudsters across 81 countries, 69% of fraudsters are between 36-55 years-old. While 79% are male, the percentage of females committing fraud is on the rise. Many fraudsters say that they are women, as women are less likely to come under suspicion. Thus, we must ensure that fraud prevention and detection machine learning models detect and learn from such scenarios without compromising ethics. Approximately 65% of fraudsters are employed by the companies that they are committing fraud for, and 35% hold senior leadership/director positions within them. The research also revealed that 62% work in groups.
If we look at fraudsters’ motivations, 60% do it for personal gain, 36% are motivated by greed and 27% do it “because I can.” This underscores that a lot of work needs to be done to lay foundations to prevent fraud.
Other key stats include the fact that technology is an enabler of fraud in 24% of cases, and the fact that only 3% of fraudsters are detected through proactive data and analytics.
Across government and the commercial sector, including Amazon.com, we see the importance of balancing customer experience, fraud, regulatory requirements, workforce capacity and technology advancements. Fraudsters are constantly changing their patterns while businesses are required to make critical decisions quickly and fairly on a large number of applicants. Citizens, for their part, demand positive, fast and reliable experiences. This has led, in some cases, to payment transactions being executed more quickly, leaving agencies with less time to identify, counteract and recover underlying funds when necessary. At the centre of all this is the friction created by trying to enable a positive experience while trying to prevent and detect fraud—with false positives often the result. According to KPMG, approximately 3% of e-commerce transactions are subject to false positives, creating additional friction and reducing consumer confidence.
Given these challenges, agencies need to consider holistic solutions that address current requirements yet offer the flexibility and scalability to meet future requirements.
Let’s first map out the different types of fraud that citizens and businesses are exposed at various stages of their journey.
Here’s an example—a citizen’s shopping journey at an e-commerce site and how advanced detection and prevention techniques can reduce the likelihood of fraud:
The following foundational capabilities are required to create a robust defence against fraud. It’s important to have a flexible and scalable platform that will adapt to and tackle new types of fraud.
Effective detection, prevention, containment and remediation of fraud requires a cross-functional team from diverse backgrounds to work together. Modern platforms need to ensure that any framework and technologies used are intuitive and promote collaboration through smooth exchange and knowledge sharing.
Here, you need data engineers to manage internal and external data and to cleanse and prepare the data for building insights and foresights. Data scientists use this data to build machine learning, AI algorithms and analytics products that help detect and prevent fraud.
On the other hand, citizens get better experiences with advanced KYC and smart claims processing. The investigators pick up the outcomes of the machine learning models and scores in real-time to support decision making or investigate claims or fraudulent activities.
Built over 20 years from the experience of fighting fraud on amazon.com, the AWS Cloud incorporates end-to-end capabilities to support counter fraud journeys. AWS continuously invests in building new capabilities itself or via partners to drive better experiences for citizens and investigators.
AWS sponsors a variety of programmes for UK public sector customers. These include Data Driven Everything (D2E), which helps customers develop an end-to-end fraud orchestration journey by providing people, processes and technologies to support the transformation. Data scientist and engineers, meanwhile, can benefit from AWS Immersion Days, through which they can get hands-on experience with services such as Amazon Fraud Detector, Amazon Rekognition, Amazon Textract and Amazon Comprehend.