While AI is today touching virtually every aspect of life, what is of great concern is its misuse, especially in perpetrating frauds. It is very widely used in:
- Creating fake identities to evade security procedures
- Phishing and related crimes
- Fake voice identities and perpetrating frauds
- Deepfake videos for character assassination
Fraudsters appear to be one or more steps ahead of regulators in making use of new technologies and cause immense losses. One of the latest applications of AI in fraud activities is the creation of synthetic identities – fraudsters use real and fake data and generate fictitious identities. Such identities are used to defraud financial and government institutions and even individuals by opening fake accounts and making fraudulent purchases. It is very difficult to detect frauds committed using synthetic identities.
AI tools are increasingly being used in phishing campaigns of scale and in carrying out fraudulent transactions and activities like betting, especially in sports. There are also AI tools for cloning voice and launching scams.
Against this unprecedented onslaught, there are various technology-driven solutions available today for institutions can counter fraudsters. For example, AI tools help banks to do transaction monitoring, which is an automated process of screening purchases, money transfers and even business interactions.
AI is also now widely used in cybersecurity. One example is the AI-enabled financial fraud detection and prevention strategy platforms
Consolidated use of AI tools can detect account takeovers and fake account creation, identify and prevent card fraud, locate credential stuffing and use of betting bots.
One disadvantage that institutions face today in the use of AI is that there is no one solution that fits all. There has to be a localized approach and several fraud-fighting models have to be created using ML tools. These tools can be effectively used to evolve risk rules based on an institution’s specific transactions and fraud data. These tools learn the institution’s context, make suggestions and create rules thereby timely flagging a potential fraudulent activity.
The accepted methods of fraud detection today are data collection, feature engineering, model training, anomaly detection, continuous learning and alerting and reporting. Some of the tools that are available in the market are:
- Kount, a tool that scrutinizes transactions to mitigate digital payment fraud
- Featurespace, which is behavioral analytics
- Darktrace, which offers cyber-threat detection and response
- SAS Fraud Management, which again is based on advanced analytics to identify and thwart fraud in real-time
- Feedzai, which helps in analyzing big data with ML to prevent fraudulent activity
- DataVisor, a method of using unsupervised ML to uncover fraud.
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