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What is decision intelligence, and why is it the next generation of advanced analytics for law enforcement?

Cutting-edge platforms can enhance organizations’ capabilities, improve their performance, and empower faster, better decisions

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Decision intelligence represents the next evolution of advanced analytics technology.


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Decision intelligence is the application of artificial intelligence (AI) and machine learning technologies, along with data fusion, data visualization, and collaboration tools to augment and improve decision-making. The goal is not to replace humans but to empower them to make faster and more accurate decisions and gain insights they could never achieve using only manual processes.


Leveraging data to make better, faster decisions in law enforcement and public safety work is not new. Still, with the increasingly sophisticated capabilities of criminals and threat actors and the vast volumes of data that are now available to be analyzed, the ability to make smarter, data-driven decisions has never been more crucial.


Law enforcement, national security, and other government personnel must quickly make critical decisions in complex and uncertain situations. One of the primary challenges they face is information overload – from mobile phones, computers, social media, surveillance sources, open-source intelligence, license plate readers, security cameras, records management systems, dispatch records, and many more sources. Data is presented in various forms, from structured to unstructured, and increasingly from “other” sources like video, audio, still images, and other media.

Another challenge is data fragmentation – law enforcement and public safety organizations can access useful data from many sources. However, combining all that data to be comprehensively analyzed is often time-consuming, labor-intensive, and slow.

Traditional analytical tools often summarize trends or provide insights that are not actionable. In organizations where more powerful analytics solutions are deployed, they are often only available – and useful – to a limited group of data scientists or technical experts. One of the key principles of decision intelligence is to promote data democratization – making data analysis tools and their benefits as widely available as possible – not just to technical people but also to nontechnical people, including analysts, investigators, and decision-makers. Data democratization empowers more people to ask questions, challenge ideas, and use data-driven insights instead of relying on guesses or gut instincts to make critical decisions.


Decision intelligence represents the next evolution of advanced analytics technology. Here are several key areas in which decision intelligence benefits law enforcement agencies:

Data aggregation and integration – Decision intelligence platforms excel at aggregating customer data from many sources, including digital forensic data from mobile devices, arrest records, computer-aided dispatch (CAD) systems, records management systems (RMS), ballistics data, license plate recognition systems (LPR), social media, and other open-source intelligence (OSINT).

Machine learning and advanced analytics – Decision intelligence leverages machine learning (ML) and advanced analytics to identify patterns, trends, and anomalies within the data and show connections between people, places, organizations, events, devices, weapons, and more. For example, ML decision intelligence platforms can infer relationships between individuals by analyzing photos or videos and then visually display that connection or link. The same can be done with voice recordings if a speaker’s voice is known. ML can suggest attributes or characteristics that can help investigators improve their search results for possible suspects or high-risk threat actors.

Risk scoring – In many types of law enforcement, investigators and security professionals must prioritize in terms of focusing their efforts and resources on potential suspects or geographical locations or transportation modes, etc. That challenge applies to drug enforcement, human trafficking, border control, customs enforcement, fraud investigations, and many other scenarios. The machine learning capabilities in decision intelligence platforms can dramatically improve the prioritization process through modeling and risk scoring, drawing on whatever data is available to identify the most likely targets. In the experience of one agency whose mission is to find and stop contraband from entering a country, field inspectors increased their discovery rate of illicit goods and customs violations by 155% over random inspections after implementing the NEXYTE decision intelligence platform risk-scoring model.


Decision intelligence leverages predictive analytics to make predictions and model likely outcomes, enabling investigators and analysts to make more accurate decisions. Predictive analytics uses statistical algorithms and supervised ML techniques to identify the likelihood of future outcomes based on historical data. This enables investigators and analysts to come up with more accurate answers to questions like these:

  • Where is the next attack or crime likely to take place?
  • Which online followers of a radical extremist group are likely to conduct violent attacks?
  • Which brokers or import/export companies will likely engage in customs fraud?
  • Which travelers entering a country are likely to smuggle contraband, engage in drug or human trafficking, or overstay their visas?


Financial crime isn’t restricted to only “white-collar” activities. There is usually a financial component in many criminal investigations, whether drug smuggling, human trafficking, or organized crime, and those components can include financial fraud, tax evasion, and money laundering. These crimes can range from basic theft or fraud committed by individuals to large-scale operations orchestrated by multinational criminal organizations. Deloitte estimates that the amount of money laundered globally is 2% to 5% of global gross domestic product (GDP), or between $775 billion and $2 trillion.

Law enforcement investigators and analysts typically analyze various data sources – including financial records, transactions, documents, and more – to trace money flows, identify suspicious patterns and anomalies, and gather evidence of illegal activity. Financial crime investigations also must grapple with cryptocurrency, encrypted communications, virtual transactions, and more. Many transnational crimes demand international cooperation among law enforcement agencies across multiple jurisdictions. These multifaceted investigations require teams to fuse and analyze data from many sources and formats, such as bank transactions, mobile payments, blockchain ledgers, and company ownership records. Developing a cohesive investigation picture is crucial for analysts to develop actionable insights, which makes decision intelligence software essential.

For example, an agency responsible for enforcing banking laws and pursuing fraud and money-laundering cases had been investigating a suspected money-laundering case for three years. The volume and pace of transactions and the large number of entities involved presented major challenges to investigators, as did other tactics used by the suspects to obfuscate their activities. The agency worked with the NEXYTE decision intelligence platform to accelerate the investigation. Within three days, investigators identified a key “straw man” who played a major role in the money-laundering operation. They could also map all the companies and beneficiaries associated with the money-laundering operation and dramatically reduce their time to resolution for other complex cases.


Experienced law enforcement leaders know that more than a few promising technology tools have failed to meet expectations because users found them too difficult to use. Some technology vendors have addressed this by requiring their employees to be embedded in the customer organization to provide expertise and manage many operations. That “solution,” however, drives up costs dramatically and has other negative outcomes.

Decision intelligence platforms like NEXYTE have been designed with intuitive and easy-to-use interfaces, which enable fast and successful user adoption. Training takes only five days.


More and more private sector companies are rapidly adopting decision intelligence applications as part of their digital transformation initiatives, and now the same is happening in the public sector. There is a convergence of factors driving adoption – the evolution of the technology itself, advances in design that make it intuitive and easy to use, the explosive growth of data, the pressures to improve efficiency and performance without increasing staffing, and the growing sophistication of both individual criminals and criminal organizations.

By combining the power of data with advanced analytics and human decision-makers, decision intelligence platforms can empower organizations to enhance their strategic capabilities, improve performance, and make faster, better decisions for better results.

Visit Cognyte for more information.

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