How AI helps rein in crimes while generating other challenges

Artificial intelligence (AI) is rapidly transforming traditional approaches to crime prevention and investigation. In the past, investigators relied heavily on eyewitness accounts, suspect interviews, physical evidence and the painstaking examination of documents to uncover clues. Today, machine learning, data analytics, facial recognition and automated surveillance systems are becoming increasingly important tools in law enforcement. By analysing thousands of CCTV recordings, phone records, financial transactions and social media data, AI can help investigators identify potential leads more quickly.

While these developments offer new opportunities to combat crime, they also raise serious concerns about privacy, personal freedom, misidentification and the misuse of technology. The use of AI in law enforcement is therefore no longer merely a technological issue; it is also a challenge of balancing public safety, human rights and the rule of law.

One of AI’s greatest strengths is its ability to identify patterns and connections across vast amounts of information. An investigator may need hours or even days to examine hundreds of documents in search of a single clue. By contrast, a properly trained AI system can analyse millions of records in a short time and highlight potentially significant connections.

The technology can help determine whether the same phone number is linked to multiple criminal incidents, whether unusual patterns exist among several bank accounts, or whether different crimes share common characteristics in terms of time, location or financial transactions.

Data analytics is becoming particularly valuable in investigations involving organised crime, money laundering, fraud, human trafficking and cybercrime. As criminals increasingly use multiple individuals, institutions and digital platforms to conceal their activities, identifying connections between seemingly unrelated pieces of information has become crucial.

However, an AI system identifying a connection does not establish that a person has committed a crime. The same phone number, location or financial transaction may be associated with several people for legitimate reasons. AI-generated leads must therefore be independently verified, and decisions must be based on reliable evidence.

Another application of AI is predictive policing, which uses historical crime data to identify areas and periods where certain offences may be more likely to occur. The objective is not to predict the future with certainty, but to help police determine where additional attention and resources may be needed.

In 2011, the Santa Cruz Police Department in California, the United States, began experimenting with a system that used historical crime data for this purpose. According to a report published by the Federal Bureau of Investigation (FBI), burglaries in the area declined by 19 per cent over six months. However, this result relates to a specific place and period and should not be interpreted as proof that AI will produce similar outcomes everywhere.

Such technology can help police plan patrols, analyse crime patterns and allocate limited personnel more efficiently. Nevertheless, if historical data reflects bias against particular communities or neighbourhoods, AI systems may reproduce that bias. Treating a crime-risk map as proof that a particular person is likely to commit an offence can therefore be dangerous.

The spread of AI is changing not only crime prevention but also the methods criminals use to commit offences. In the past, creating convincing fraudulent messages, establishing false identities or conducting targeted cyberattacks often required specialised skills. Generative AI has made it easier to produce persuasive scam messages, imitate familiar voices and create fabricated images and videos.

This can increase the risks of fraud through social media, phone calls and emails. Criminals may impersonate trusted individuals or organisations, fabricate emergencies to demand money, or manipulate victims into disclosing sensitive information.

Europol, the European Union’s law enforcement agency, has warned that AI, encrypted communications and digital platforms are creating opportunities for criminals to expand their activities and reduce the risks of detection. Criminal networks can use technology to operate more rapidly and across wider geographical areas.

The important distinction is that AI itself is not a crime. The central concern is its use for criminal purposes. An AI-generated fake video can seriously damage an innocent person’s reputation, while digital evidence analysed with the same technology can help investigators identify actual offenders.

Privacy Concerns: One of the most sensitive aspects of AI in law enforcement is personal privacy. Facial recognition, location analysis and the integration of information from different databases can create detailed pictures of people’s movements and communications. It is therefore essential to establish how such information is collected, how long it is retained and who is authorised to access it.

Errors in facial recognition can lead to innocent people being wrongly identified as suspects. The risks may increase when training data is incomplete or biased. Similarly, when an algorithm’s decisions cannot be adequately explained, individuals may find it difficult to challenge those decisions.

For these reasons, AI-generated findings should be treated as investigative leads rather than conclusive evidence. Trained investigators and analysts must assess the results, verify their accuracy and consider the wider circumstances before taking action. Responsibility for decisions affecting people’s rights must remain with human authorities.

As Bangladesh expands its use of digital financial services, mobile communications, online commerce and social media, digital information is becoming increasingly important in criminal investigations. AI-assisted analysis could accelerate investigations into online fraud, identity theft, financial irregularities and organised cybercrime.

However, adopting AI requires more than purchasing software. Effective implementation depends on reliable databases, skilled personnel, strong cybersecurity, clear data-protection rules and lawful investigative procedures. Authorities must also establish mechanisms to verify AI-generated findings, correct misidentifications and allow citizens to challenge wrongful decisions.

Excessive dependence on technology can be as risky as ignoring its potential. AI can make investigators’ work easier, but human judgement, an understanding of context and the responsibility to ensure justice cannot be delegated entirely to machines.

In today’s criminal landscape, keyboards, fabricated identities and algorithms are becoming as significant as conventional weapons. The challenge for police and investigative agencies is not simply to adopt technology faster than criminals do, but to ensure that its power is exercised in accordance with the rule of law and the protection of citizens’ rights.

The future of crime prevention will increasingly depend on a contest between algorithms. Yet the ultimate objective must remain clear: technology should serve justice, not replace it.