Businesses and cities around the world are experimenting with using computerized detection and prevention in diverse areas, from cybercrime to healthcare and law enforcement.
The idea behind many of these artificial intelligence (AI) projects is that negative phenomena like cyberattacks or crime are relatively predictable. But to generate reasonable predictions, one needs to sort through huge data volumes to discover patterns and potential issues.
Analyzing massive data volumes is exactly what AI does. Although this kind of analysis was impossible less than a decade ago, things have changed now. How exactly do you apply AI-powered algorithms for the detection and prevention of negative phenomena?
In this post:
- AI detection and prevention: cybersecurity
- AI detection and prevention: law enforcement
- AI detection and prevention: finance and banking.
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AI Detection and Prevention: Cybersecurity
Let’s start with the area that most people imagine AI making a difference first. Indeed, the technology already has a track record of successful use in battling:
- Online fraud
- Cyber attacks.
Online fraud
To protect online transactions against fraud, companies are using predictive machine learning models. These are algorithms that analyze data to learn about normal patterns and standards in online transactions to estimate the probability of fraudulent transactions and detect them as abnormalities.
Cyberattacks
AI algorithms’ capability to examine data traffic and find abnormalities also helps with detecting security breaches. The technology can quickly process massive data volumes and find abnormalities in patterns based on historical analysis. An abnormality could be as small as a harmless piece of code placed by hackers.
Other AI capabilities that are being added to the existing cybersecurity tools:
- Identifying cyber threats and activities using predictive analytics algorithms
- Designing more accurate, secure, and biometric-based login techniques
- Making conditional authentication and access more secure.
The effectiveness of AI in battling cyberattacks results in more interest from companies and governments. According to the latest reports, the AI in the cybersecurity market is expected to reach $46.3 billion in seven years and grow by 23 percent annually.
Related content: Artificial Intelligence: Fundamental Principles
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AI Detection and Prevention: Law Enforcement
The role of AI in law enforcement is getting more important each day. Currently, governments and cities and using the technology to both identify and prevent crime.
AI security cameras
AI chips placed in security cameras have advanced capabilities of detecting license plates, performing facial recognition to search for criminals and missing persons, and detecting unattended objects in crowded places.
Although cameras have had this feature before AI, the technology has eliminated the need to process video surveillance data on a centralized data hub or cloud. In other words, security cameras now can make decisions by themselves.
Predicting future crime spots
Machine learning is now used to analyze historical data on crimes to predict where and when they are likely to occur in the future. The algorithm is based on the observation that crime tends to cluster in space and time, so the analysis of crime occurrence data might show the possible hotspots.
Pre-trial suspect release and parole
AI is powering algorithms that evaluate and predict a suspect’s potential to offend and re-offend. The police departments around the world are already using them.
For example, one UK police department has been using the Harm Assessment Risk Tool (HART) to help custody sergeants to make decisions regarding the release of suspects. Sheena Urwin, a member of Durham PD who worked on HART, has recently been awarded Police Staff Member of the Year by the British Association for Women in Policing.
Related content: Artificial Intelligence: Advanced Principles
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AI Detection and Prevention: Finance & Banking
In the financial industry, AI-powered algorithms are already found many uses–and are projected to save banks $447 billion by 2023. Financial institutions apply them to combat money laundering and personal loan fraud, understand and manage financial risk, and make credit decisions.
Make credit decisions
Many banks use AI to make more informed decisions regarding credits. Instead of evaluating the borrower by doing checks manually, they use data models to access and evaluate all available data on clients. The models can identify high-risk lenders and credit-worthy individuals.
Players in the industry have welcomed AI in making credit application decisions. Aaron Klein, the director of the Center of Regulation and Markets, wrote that the technology can take more data into account and solve the problem of many credit decisions being made on the basis of personal relationships.
Prevent fake insurance claims
Fake and fraudulent insurance claims make up to 10 percent of all insurance payouts and translate to $80 billion annual losses for U.S. companies alone. That’s why many of them are investing in AI prevention and detection of fraud.
Decision-makers in the industry say the best prevention is being aggressive–which means using the latest tech like AI to seek out red flags. Now, AI fraud prevention models analyze tons of data in each claim to define a likelihood of fraud. The fact that personal financial data is accessible online by appropriate organizations makes the check fast and easy.
For example, they check if an individual attempted to file several claims in a row with minor adjustments. This suggests potential fraud – that individual might be filing as many claims as possible in hopes that at least one will get accepted.
AI Detection and Prevention: Final Thoughts
Although we covered just a few areas here, companies are using AI to detect and prevent phenomena in almost every industry imaginable. The major advantage of using this technology for the task is its ability to process massive volumes of data and detect patterns humans can’t.
This ability will continue to improve, along with the range of use cases. Companies are investing in custom AI algorithms tailored specifically for their market and niches, which results in unique models capable of tackling specific business problems and creating a competitive advantage.
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