In terms of economic costs, crime is expensive. In the United States alone, it makes up 2% of the GDP. The technology used to predict crime or automatically catch suspicious behavior can help society, government, and businesses with loss prevention.
The unfortunate truth is that whenever a crime happens, either there is no information or too much vague information. When there are multiple reports, the information received is often contradictory.
The use of AI an ML in this could prove effective in order to triangulate precise information. AI has the ability to make surprisingly accurate predictions based on the collected data. The design and operations need to be efficient in order to be relatable and sustainable. The goal is to not just catch criminals but prevent crimes happening in the first place. Analyzing existing data on the past crimes can predict where new is more likely to occur. By using historical data the future crimes can be detected and hence prevented. With the use of advanced AI technology like face recognition and gait analysis, it could help find and track individuals. Any suspicious change in the behavior or unusual movements can be recognized and detected.
The use of AI and ML to detect crime through sound or cameras are currently available, is proven to work and is expected to expand further for more utility.
Businesses are perpetually experimenting with new ways to use advanced tools and technologies for higher risk management and quicker, additional responsive fraud detection and even to predict and forestall crimes.
Pattern Detection to Identify Crime
Conventional monitoring systems used by businesses need manual interference which isn’t 100% correct. AI tools allow detecting patterns that are invisible even to experts. These tools can identify patterns and link data points and can trigger the information to the system.
Evaluation and Internal Risk Mitigation
Though AI is advanced technology yet it needs to be looked after AI backed risk management and crime detection should not be conducted in isolation. Fraud detection techniques are thus evaluated transparent machine learning models to remove false alerts and prediction biases.
Security
With the wide use of technology and services, security becomes an important and challenging factor. Suspicious behavior, fraudulent emails, can be tracked down to predict security threats.
“Cloud Walk, the Chinese facial recognition enterprise is foraying into a new scope of technology where it would be possible to predict if a person decides to commit a crime, even before he attempts to. As a result, they have built a system to detect suspicious changes in the manner or behavior of an individual. For example, if a person buys a hammer, that’s completely fine. But of course, if he buys a knife and a rope, he comes under the radar of suspicion.”
Social media platforms take advantage of machine learning to block illegal content like child pornography. Social Media platforms like Instagram, Facebook, etc. provide access to report an activity or posts that may prove to be malicious and inappropriate. Certain algorithms are being employed in such tasks to identify and detect any suspicious activity or content. Most machine learning solutions today utilize predictive rules that identify anomalies existing in data sets automatically. These algorithms help identify false alerts through filtration techniques
With time, AI-driven crime-fighting tools have emerged as a necessity for big enterprises, as there’s no alternative way available for rapidly detecting and interpreting patterns from the billions of data pieces.
