The Evolution of Intelligence-Led Investigations

Intelligence-led policing is a method of preventing crime instead of only reacting once a crime has happened. Although the idea is over 30 years old, recent technology and organizational factors have made it more effective than ever. The rise of AI, predictive analytics, and cloud computing gives law enforcement the tools to gather and interpret data for valuable insights effectively. Plus, police reform has created a greater emphasis on evidence-based policing that helps prevent violence.

As law enforcement continues to grapple with a crippling shortage of police officers and an increase in crime, finding efficient solutions is critical for maintaining the safety of communities.

Intelligence-led policing (ILP) is a valuable model that uses data collection and analytics to prevent crimes.

Evidence-based policing, which focuses on research to push police reform, and postmodern policing, which seeks to localize policing to individualize justice through legislation and reforms, both depend on ILP to meet their goals effectively and efficiently. ILP enhances the tools that inform evidence-based policing. It also aids in identifying potential victims, which is central to the victim-centered approach of postmodern policing.

These three distinct and complementary policing models are critical for ensuring the safety of modern society. The fusion of these three practices is where we discover true intelligence-led policing practices 30 years after the term was introduced. Technology and legislative measures have advanced rapidly to help realize the underlying principles of ILP. It has grown into an opportunity for law enforcement to stay ahead of crimes and use their resources more effectively.

Technology in the Evolution of ILP

Digital innovation is one of the most vital opportunities for law enforcement to stay ahead of threats. The past decade saw a rise in technology that offers police a commanding position to respond to and evaluate dangerous incidents. They now better predict hot spots and strategically use their resources to improve public safety.

Intelligence data does not replace the need for skill in real-time decision making. However, it does provide critical information to inform strategy and an accurate overall view of the world that officers operate. It enhances decision-making out on the field, but it does not supersede or over-rule the skills law enforcement officers develop out in the real world. Instead, intelligence data offers a critical push-and-pull to real-time decision making. ?

Some of the technology at the forefront of ILP include:

Predictive Analytics?

Predictive analytics make it possible for law enforcement agencies to anticipate where crime will occur and strategically prevent it. To more effectively use their resources, many law enforcement agencies across the country focus on particular areas where crime is highest and most predictable.

Predictive analytics use data to make connections that determine these “hot spot” areas. For example, some technology triangulates critical information to pinpoint the location of gunshots. Not only can predictive analytics predict where crime is likely to take place, but it also identifies potential criminals through tools such as facial recognition technology.

Predictive technology makes one of the core goals of ILP possible: stop crime before it happens rather than reacting only once a crime has occurred.

Cloud Computing

Data is fundamental for law enforcement work. Police need to be able to track and analyze a large amount of data quickly to make the most significant impact. With cloud computing, this is now possible for agencies. Police have access to accurate statistics and real-time dashboards that monitor and alert for any changes.

Not only does cloud technology make it easier to compile data and get insights, but it is also easier to centralize criminal justice databases for better partnership between departments. Collaboration is critical for ILP.?Siloed agencies cannot work as effectively, whereas working together helps police officers leverage information and resources they don’t have on their own. Cloud computing improves efficiency by providing a critical means for an open exchange of information.

Artificial Intelligence/ Machine Learning

While collecting data is critical to law enforcement, it is in making connections between this information that fuel insights and enhances effectiveness. Artificial Intelligence (AI), specifically its subset, Machine Learning (ML), enables investigators to quickly and accurately make these connections. This technology can use information about individuals and their criminal backgrounds to improve investigations and guide decision-making regarding detention, prosecution, and punishment.

When combined with the skilled expertise of law enforcement officers, AI fuels deeper insights and helps remove human bias from decision-making. For example, some AI assigns a risk score to suspects who take their history and other data factors into account to see if they are a risk to be held pre-trial.

Biometrics

Biometrics are nothing new to law enforcement and police departments have used finger scanning devices for decades to identify individuals. However, biometrics are evolving and offering police better ways to improve the speed and accuracy of their identification.

Facial recognition is a common practice for many law enforcement agencies and fraught with controversy. Although some worry that it could be abused and result in a loss of privacy, it is a critical tool for identifying suspects, especially at large events, airports, and border crossings. As a result, The World Economic Forum released guidelines for responsible facial recognition technology that they started testing in January 2022. It enables law enforcement to balance the safety of communities with privacy.

In addition to facial recognition, biometrics discover critical habits that could identify individuals, such as:

·?????Signature recognition

·?????Gait analysis

·?????Typing pattern

·?????Reading speed

·?????Olfactory analysis

Each of these provide a way for police to identify individuals and prevent future crimes.?

Other Factors Impacting ILP

Beyond technology, there are also other factors at play in the growth and evolution of ILP:

·?????An increase in collaboration between agencies has given officers access to more resources. Intelligence Liaison Officers are now used to improve sharing systems and general partnership between departments.

·?????Legal policy changes are increasingly becoming implemented to enhance cross-branch collaboration. The enhanced partnership between the executive and legislative branches helps prioritize evidence during the budgeting process and creates a shared commitment to ILP work across the government.

·?????Reforms in policing have called for implementing evidence-based changes to reduce fatal citizen encounters. ILP enables officers to deescalate situations proactively before they turn violent.

Creating a More Effective and Safer Police Force with ILP

The rise of collaboration, reforms, and technology have made ILP a more effective and efficient policing model than ever. As law enforcement seeks to find ways to address the growing shortage of officers, it continues to add value and improve public safety.

With the continued advances in technology, ILP promises to be the future of policing with increased accuracy and efficiency.