Status: Draft -- Not PublishedWill be live at 01/30/2023 08:00
Data Policy Impacts - Fraud Prevention
This case example explores the impact of restrictive data policy on AI/ML tools to combat the growing challenges of fraud in payments. The implications for the economy are significant in an era of digital transformation and they are broad-based—including on micro, small and medium enterprises (MSMEs).
- AI/Machine Learning solutions have become critical tools to keep pace and combat the growing challenge of fraud in payments with loss totaling $28.58bn in 2020 and is projected to grow to $49.32bn by 2030.
- The flow of data is integral for these AI/ML tools to perform at the level on which we rely. One global payments network’s advanced systems were able to prevent $26 billion in fraud during 2021 and screened 30% more transactions than in 2020.
National restrictions on the flow of data continue to proliferate around the globe. We are rapidly reaching an inflection point where data localization requirements and fragmented standards for data and privacy may begin to break the on-demand services and real-time systems that we have come to expect and rely on.
Fraud detection and prevention are an important example of where restrictive data frameworks impact services and systems. To fight this trend, and maintain accurate and effective fraud prevention systems, AI/ML solutions need to be strengthened and data sets need to be broad – the more data, the more effective the model is at detecting fraud.
Failure to keep pace with the criminals could have broad based costs for the economy including for micro, small and medium enterprises (MSMEs). Digital commerce enables many micro, small and medium enterprises (MSMEs) to reach larger audiences and offer customers greater choice; however, MSMEs frequently lack the specialized skills and resources to combat the increased exposure to fraudulent activity from their digital transformation. Reliance is placed on global payment networks which can deliver the benefit of advance fraud analysis using global data sets to combat fraudsters, other malicious actors, and even sophisticated nation states who do no respect sovereignty or borders.