Artificial intelligence, intelligence gathering & analysis, crime management


“The safety of a community is its top priority, and as a result, governments take necessary actions to reduce crime rates. This, in turn, ensures economic growth and quality of life. Crime analysis is a critical part of criminology that focuses on studying behavioural patterns and tries to identify the indicators of such events” (Mahmud et al., 2017).

Criminal activities have been part of human civilization since its inception. The phenomenon of “crime” has become a major subject of private and public concern throughout human existence. No society is free from crime. However, the question often asked is; even if a crime is part of human existence and behaviour, how much of it can a society tolerate? This question is linked to man’s instinct for survival, the ability to respond to any threat to his life and property. Crime poses such a threat, particularly in its violent form.

The recent upsurge in violent crimes in Ghana has created enormous uncertainties in the security of lives and property of persons in the society and has affected social stability as well. The incidents of traditional crimes such as armed robbery, arson, drug trafficking and abuse, murder, kidnapping, rape, hired assassinations and ritual killings are examples of the most serious and violent crimes which are often reported. Similarly, white-collar crimes in the form of Advanced Fee Fraud, contract deals, embezzlement, and mismanagement in both the public and private sectors are also on the increase. The aggregate of the traditional crimes mostly committed by the less privileged and white collar crimes mostly committed by the highly placed does call for a change in the strategies for the prevention and control of crimes in Ghana.

The existing patterns in criminal activities show that criminals are getting more sophisticated and organized in the manner they carry out their acts, either in the way they physically attack individuals with dangerous weapons or the methods they use in taking advantage of their official positions to embezzle public funds in foreign and domestic accounts. In addition to these new patterns of violent crimes against persons, there is also equally disturbing criminal behaviour against the Telco industry.

The degree and extent of crime commissions generally in the world and particularly in Ghana have necessitated the need to look beyond the methods or traditional means of preventing and detecting crime. In the United Kingdom, the situational crime prevention project has moved from the use of CCTV Surveillance systems to technological advancement that enables national authorities to record, monitor, and scrutinize phone calls, and messages, especially those posted on social media.

However, a lingering objection to this is the possibility of violating human rights especially the privacy of the person concerned. Technology has been identified as a major instrument for crime control, especially in developed countries. The techniques and technologies adopted in the prevention and detection of crime have improved and changed greatly. They have shifted from crude implements and traditional methods and devices to modern, scientific, and sophisticated techniques.

Modern technology including Artificial Intelligence has become a vital tool in crime control worldwide. The traditional and age-long systems and practices of preventing, detecting, and investigating crime have failed to withstand the existing trend of crime patterns. There is a shift to improved technology as an alternative to combating crime. The various law enforcement agencies in Ghana are still relying on traditional methods of crime prevention and detection with little or no modification to the use of advanced technology.

It is on this background that, it is worthy to state that the current and emerging technologies for crime surveillance, prevention, and protection in the world today are evidence of development and are capable of meeting the challenges posed by criminals and thus, Artificial Intelligence is that effective mechanism.

The origin and growth of crime levels are based on several characteristics; these characteristics can be different income groups, different racial backgrounds, age groups, family structure, levels of education, size of housing, number of employed to unemployed, police officers per capita for the region, etc.

The availability of crime statistics in the free domain has made it practically possible to use big data and machine learning (ML) techniques for predicting and preventing crime, by supporting the optimal allocation of limited police resources, as knowledge of the likelihood of crime occurrences for a particular area, predicted through a model will help allocate additional police personals to the known crime hot-spots for a particular time and therefore reducing the likelihood of crime occurrences.

Artificial Intelligence is slowly but surely becoming a proficient tool to punish criminals and also check unlawful actions. It is no more a concept just to be speculated upon. Many of the Law Enforcing Agencies across the world are using the most up-to-date solutions to prevent crime. One such solution is ‘facial recognition’ which is being widely implemented in various sectors other than the law to maintain security.

Artificial intelligence in policing is a framework that is evaluated with the help of computers. It can also be utilized to make decisions regarding final rulings. It is the technology that holds great promise for the future in crime detection.

Definition of AI and its Subfields

AI is a bigger concept where intelligent machines can simulate human thinking capability and behavior. AI can be described in two different ways, it can either be explained as a science that aims to discover the essence of intelligence and develop intelligent machines. However, it can also be described as a science of finding methods for solving complex problems that cannot be solved without applying some intelligence.

This means that one uses a large amount of data to make the right decisions. AI can be categorized into three different levels: weak, medium, or strong. Weak AI (Artificial Narrow Intelligence) is an AI system that is programmed to perform one single task or perform a combination of tasks in a narrow area of expertise. This means that a machine’s intelligence can be narrow when it is restricted to one specific domain.

The majority of all AI systems used today are considered weak since they are confined to a fixed set of tasks that they specialize in solving. Medium AI (Artificial General Intelligence) is when a machine is capable of understanding the world as any human would and have the capacity to perform several different tasks. However, medium AI does not exist in today’s society, only in our produced science fiction movies. Strong AI (Artificial Super Intelligence) does not either exist in today’s society.

There would be a machine capable of surpassing human intelligence in all domains. AI can be seen as an umbrella term covering a wide range of different applications. Machine learning [ML] is a subfield of AI where machines learn from data without having to be specifically programmed. ML can be used on various data sets. The methods of ML are primarily statistical, and ML aims to discover patterns in data. Whilst ML is a subfield of AI, deep learning is a subfield of ML. Deep Learning implies training an artificial brain so that it can imitate the functions of a human brain. Deep learning is particularly suited for acquiring information from unstructured data.

Data mining is related to machine learning since it uses such methods to extract information. Whilst ML search for patterns and learn from the identified patterns to adapt behavior for future incidents, data mining is used as an information source for ML to pull from. Data mining is particularly used when extracting usable data from a larger set of raw data so that ML can identify patterns in the data sets. In contrast to deep learning, data mining relies on human intervention and decision-making. Deep learning, on the other hand, uses an automatic extraction method for information, once the rules are in place in the machine. Therefore, deep learning can work without human intervention once the rules are set. The term used for data sets is called big data, which describes both structured and unstructured data.

The Use of AI in Crime Prediction and Prevention

Artificial intelligence is being used by law enforcement agencies to make their agents more efficient. Because of the many ways it assists law enforcement, it is quickly becoming an essential tool. Artificial intelligence (AI) is utilized in surveillance to keep an eye out for suspicious behavior, analyze security footage for signs of criminal activity, and improve facial recognition accuracy. It is anticipated that the use of AI in policing would result in significant improvements in public safety. The primary mission of law enforcement is not just crime prevention but also crime resolution. Artificial Intelligence already, has been a part of various sectors such as transportation, finance, energy, healthcare, etc. for a fairly good amount of time. In comparison to those sectors, the police forces have adopted AI quite recently.

The application of AI technology in crime prediction and prevention is a relatively new phenomenon that needs more exploration. In crime prediction and prevention, the term artificial intelligence can be understood as the growing use of technologies that apply algorithms to large sets of data to either assist human police work or replace it. AI can increase efficiency and provide insights from big data; therefore, it can contribute to police work. AI fulfills the task better than police officers since they are more efficient in finding patterns in large sets of data. AI can be seen to have great potential in crime prediction and prevention since it can empower the practice by efficiently finding patterns from large sets of data, which for a human would take a great amount of time. Thus, resources can at a more rapid pace be placed to prevent crime.

The use of AI technologies in crime prediction, intelligence gathering, intelligence analysis, and prevention includes predicting the time and place of future criminal activity by conducting data analysis. The use of these technologies, such as AI, ML, deep learning, and data mining, can improve the practice of crime prediction and prevention. The use of AI technologies in crime prediction, intelligence gathering, intelligence analysis, and prevention is to efficiently identify patterns and accurately predict future behavior, such as criminal conduct, therefore AI can predict criminal acts to prevent them.

This process begins with big data, a large set of data that different AI techniques utilize information from. Through the use of big data, which in this case includes crime history such as police-reported crimes, different AI techniques can identify patterns to predict where a crime is most likely to occur/be reported to have happened. The crime can then be prevented because of AI’s prediction. Crime prediction with the help of AI includes variables about time, weather, location, annual income, and the literacy rate within the area. These categories serve as risk factors and this can potentially cause or influence the probability of a crime to be committed or not.

The Segmentation of AI

AI cameras aid police in their investigations of crime sites. There are occasions when the crime scene expands to the point where it is no longer safe to approach on foot. The use of AI in law enforcement would be useful in shedding light on such predicaments. It can help locate evidence long after a crime has been committed. When there is a large gathering of people, law enforcement agencies not only rely on cameras but also on video technology to keep an eye out for any suspicious activity.

Artificial intelligence is typically employed during festivals and major sporting events to spot any suspicious behavior. When used in conjunction with video technology, facial recognition allows for heightened crowd surveillance. The Police can better monitor public spaces like train/bus/transport stations and stadiums with the help of AI. Few police officers would be able to scan a large gathering, much less do it with such astonishing results, underscoring the need for artificial intelligence in policing in such situations.

Robotics is another area where AI has been applied specifically. Increasingly, LEAs are making use of actual robots capable of performing a variety of functions. These robots can execute activities that humans should not do with relative ease. When it comes to preventing criminal acts and saving lives, artificial intelligence in law enforcement is crucial. Robots programmed with artificial intelligence are capable of performing dangerous and complex jobs, such as detonating a bomb. They can also go where it’s dangerous to control and identify everything there. A robot powered by AI would be better equipped to deal with illegal situations.

Making Use of AI for Criminal Investigations

Artificial intelligence (AI) systems assist law enforcement in spotting suspicious trends that cannot be picked up by humans. Through “Artificial Neural Networks,” the police can utilize and foresee a criminal’s next move. Security system regulators make use of the millions of data points available to them to arrive at such estimations. Social media posts and IP addresses from shared networks are included in these data sets. Today, money laundering and other scams can be uncovered with the help of artificial intelligence in law enforcement.

Law enforcement agencies are not the only ones who could benefit from using AI to track criminals. It can be used to identify contraband in transit. Artificial intelligence would be most useful for a delivery service in determining whether or not a package contains a restricted item. If they do, they must report it to the proper authorities so that action can be taken. There are several artificial intelligence (AI) solutions that pharmacies and other retail establishments can use to spot suspicious customers. Any major purchases of chemicals or other items must be reported immediately by store owners. Shipping businesses can use AI and their data to combat human trafficking. This would allow them to identify the containers being used in the unlawful transportation of people. This would be a fantastic first step toward saving a lot of people’s lives.


With an increasing crime rate, existing Artificial Intelligence applications are proving to be very useful. Crime and criminal behavior can be predicted with greater accuracy with the use of these systems. However, you should also ensure that the operational structure is clear and logical. The author suggests setting up a committee within the organization to oversee AI policy. The creation of an AI system necessitates input from all regional capitals. The protection of human rights is another important issue that must be resolved. It’s becoming increasingly clear that law enforcement agencies might benefit greatly from using AI in various capacities. It will be standard procedure, but it must be ensured that no one’s rights will be violated by the laws or other Acts.


  1. Dilek, S., Çakır, H., & Aydın, M. „Applications of Artificial Intelligence Techniques to Combating Cybercrimes: A Review‟.(arXiv preprint, Illinois, 2015) P.52.
  3. Rademacher T. (2020) Artificial Intelligence and Law Enforcement. In: Wischmeyer T., Rademacher T. (eds) Regulating Artificial Intelligence. Springer, Cham. doi:10.1007/978-3-030-32361-5_10
  4. Saidi, W.A., Zeki, A.M. (2019). The use of data mining techniques in crime prevention and prediction. 2nd Smart Cities Symposium (SCS 2019), Bahrain, Bahrain, 1-4. doi: 10.1049/cp.2019.0225
  6. Yu, D. (2019, January 22). PepsiCo Sees Future in Artificial Intelligence After Launching SnackDelivery Robot. Forbes. Retrieved from
  7. Yu, H., Liu, L., Yang, B., Lan, M. (2020). Crime Prediction with Historical Crime and Movement Data of Potential Offenders Using a Spatio-Temporal Cokriging Method. ISPRS International Journal of Geo-Information, 9(12):732. doi: 10.3390/ijgi9120732

The writer is a Ph.D. candidate, CEPA, Ch. ME, ChMC, CFIP, MSC, MPHIL, BSc, LLB, Dip CFI, & Dip P.I). Contact: 0246390969. Email: [email protected]

Leave a Reply