Big data analytics entail digital forensics investigation, which involves locating, obtaining, and recording digital evidence using specific software tools to create evidence that can be used in court. Law enforcement uses digital forensics to gather evidence after a crime has been committed to prosecute a suspect or stop more crimes from occurring. A device’s data could help identify suspects, shed insight on their motivations, point out their connections to other criminals or prove their innocence. Forensic data can aid in discovering trends, patterns, and the correlation of additional data relevant to criminal activity. The resulting actionable intelligence can then be used to stop and prevent violent and serial crimes. In a business setting, digital forensics could be utilized as a component of the organization’s incident response procedure to help pinpoint what occurred and isolate what or who was at fault, whether it constitutes criminal prosecution or internal control.
The Digital Forensic Process
Five critical forensic analysis components are involved in a detailed analysis, contributing to a successful investigation. The first step is developing policies and procedures for all activities related to forensic analysis. Forensic evidence is highly sensitive; thus, cybersecurity experts must know how to handle and protect the data. The procedures include instructions on preparing systems for retrieving, storing, and retrieving evidence. In addition, the experts require relevant authority to authorize forensic investigators to recover potential evidence and document activities. The second step entails assessing potential evidence in terms of classifying crimes. The experts need to determine the integrity and source of data before entering it as evidence.
The third step entails acquiring the evidence, as all evidence should be preserved and recorded before documentation. The integrity of evidence is crucial since the lack of substantial evidence leads to an unfounded analysis. The general rules for evidence preservation include physically removing storage devices, utilizing controlled boot discs to retrieve vital data, and taking the necessary measures to copy and transfer the evidence to the forensic investigation team (Shehova). In a court case, it is crucial to authenticate and document all evidence that must conform to the Daubert standard. Abstraction layers are the most used tools during digital forensic analysis. These tools require other software tools to translate and provide error statistics to determine the validity of evidence. This ensures that the evidence obtained from investigations produces similar results when different tools are used. Forensic investigators then examine and document the evidence before presenting it to court.
Digital Forensics in Law Enforcement
Data analytics can make a huge difference in law enforcement. Law enforcement agencies can achieve significant results by having databases filled with data regarding number plates, criminal identities, crime statistics, and access to social media platforms. With the help of modern analytics, law enforcement agencies can turn the available data into actionable intelligence, giving rise to predictive policing. Fighting financial crime has never been more challenging due to increasingly strict compliance standards and rising data volumes. When digital forensics first emerged in the 1990s, it was seen as a supporting element in an investigation that sought to apprehend primarily white-collar criminals. By examining computer storage devices, network servers, and other types of digital data to find hackers, digital forensics experts can investigate an intrusion, acquire evidence of a crime, and expose fraud.
How Big Data Analytics are Used in Law Enforcement
Various open-source digital forensic tools can retrieve in-depth information regarding infrastructure. These tools help conduct data analysis in unauthorized server access or help solve murder cases. Some of these tools include autopsy, an open-source graphic user interface-based tool that analyzes smartphones and hard drives, investigating the history of devices. Wireshark is a network capture and analyzer tool that explores the network while network miner detects operating systems, hostname, and open sessions through packet sniffing (Poston). Cellebrite UFED tool is used to collect call detail records, including identification codes and cell towers used to route communications, thus determining if a suspect was in the vicinity of a crime scene. Encrypted disk detector helps crack encrypted drives while magnet RAM capture analyzes memory artifacts of a computer to determine transactions. These tools help prevent crimes by assisting in criminal identification, predictive policing, and crime prevention.
Data analytics help prevent crimes by profiling criminals through machine learning to identify patterns and create accurate criminal profiles used to capture criminals. Most criminals exhibit similar behavior; thus, analyses of that behavior or comparing it to individuals lead to identifying criminal intent, thus preventing future crimes using identified patterns to form prediction techniques. In addition, law enforcement input files from crime scenes into databases such as DNA samples to search for connections between cases, thus developing accurate suspect lists. GEDMatch, a database for genetic information, can identify DNA markers to a specific individual, thus arresting culprits (Matthews). Data analysis of social media accounts helps identify potential criminal intent by analyzing comments that point to criminal intent. In the same way, advanced analytics can assist prevent crime by analyzing social media; data mining techniques can be used to uncover patterns in various crimes, including murder, theft, and domestic abuse. One of the most common places to profile criminals is the airport, as the security relies on criminal knowledge to identify behaviors of smugglers preventing contraband from entering a country.
Data analytics drives predictive policing by identifying places with high crime rates and increased policing resources. Tools like PredPol help law enforcement agencies narrow down on regions where crime is most likely to occur. The tool collects data on the date, time, and type of criminal activities inherent in an area and makes predictions on where and when such crimes could happen. Machine learning mitigates crimes when sophisticated algorithms analyze data such as texts held in a single repository. Law enforcement agencies analyze extensive data looking for individuals who trade images of children, thus arresting pedophiles (Pham). Law enforcement agencies can analyze the data by capturing live camera feeds and complaints registered with the police. Law enforcement officers input data such as a burglar’s mean of entry, time of the break-in, and type of properties targeted nationwide to study burglaries on a large scale. This helps officers identify patterns and dispatch more officers in high-risk situations.
Big Data Analytics in Corporate World
Businesses in the financial industry are switching from providing generic products to providing customized services. A fresh approach to data and analytics is necessary when regulations and compliance requirements trend toward an open and collaborative corporate world, where the ever-increasing security concerns are considered. Data-driven capabilities will underpin financial services in the future. Businesses are reimagining customer experiences using data-infused analytics to create unique financial offerings depending on preferences. Data analytics enhance risk management by fully automating decision-making and integrating risk decisions with balance sheet optimization (EY Global). Automation through digitalization has increased agility and innovation at scale. There are streamlined regulatory compliance as data analytics create a business-wide integrated platform that enables near real-time pull frameworks with regulators.
Data analytics play a critical role in the industry’s rapid transformation in the banking sector and insurance and payments platforms. Each consumer will receive individualized, lifestyle-inspired experiences from banks, developing novel business models and vast digital ecosystems. At a lower cost, smooth end-to-end insurance experiences will be made possible through intelligent interfaces, automation engines, and event-based life services. Advanced analytics will enable contextualized transactions, and frictionless security as instant, simple, invisible payments become a part of our daily lives. There is a critical need for reform given the increased fines levied by international authorities in the previous decade for non-compliance with anti-money laundering (AML), know your customer (KYC), and sanctions regulations.
With more stringent compliance standards, governments and regulators are placing financial services companies at the forefront of the fight against financial crime. Financial companies have always emphasized manual, human engagement in the regulatory reporting people to write physical reports. Even in today’s workflow for case management, this is still a frequent practice. Before suspicious activity and other compliance duties are reported to authorities, many levels of case investigators physically check the information and prepare disposition narratives. However, humans cannot keep up with demand due to the massive amounts of data moving in and out of financial systems. Frequently, operations teams cannot keep up with the rapid growth of risk alert backlogs. Artificial intelligence, machine learning, natural language processing, and cognitive automation are advanced data and analytics techniques that can accelerate or automate a sizable portion of labor-intensive work, lowering operational costs and freeing up staff to focus on preventative interventions.
Compliance teams are utilizing sophisticated analytics in various preventive financial crime use cases, including enhancing the KYC process, improving sanctions screening performance, and monitoring transactional activity, helping identify risks and opportunities. This includes decreasing operational workloads in case management. Businesses can leverage big data and analytics techniques to help improve regulatory compliance and lower the cost of operational risk management (Bawaneh 79). Transaction monitoring improves anti-money laundering units to predict scenarios before they occur. Machine learning models add significant details regarding customers, accounts, and beneficiaries, including prior cases, processes that assess the risk of transactions, and external information regarding law enforcement inquiries and subpoenas. Analytics can enable customer segmentation and profiling for various corporate objectives, such as compliance and marketing. For instance, compliance teams might use customer profiles for consumer risk analyses or investigations.
Big data analytics is effective in fighting financial crimes and optimizing business operations. Digital forensic analysis help make the world better by identifying criminal patterns with increased efficiency. Big data analytics have changed how police conduct investigations and will be vital for information security and preventing crimes. Law enforcement operations have been transformed as data analytics help in profiling criminals and identifying high-risk areas leading to increased surveillance. In addition, big data analytics can drive efficiencies, reduce operational costs and help fight financial crime in the corporate world.
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“How Data Analytics Is Leading the Fight against Financial Crime.” Ernst & Young Global Ltd, Web.
Matthews, Kayla. “How Data Analytics Are Solving Murders”. Medium, Towards Data Science, Web.
Pham, Thu Anh. “How Big Data Helps to Fight Crime?”. Bestarion, Web.
Poston, Howard. “Popular Computer Forensics Top 19 Tools”. Infosec Resources, Web.
Shehova, Antonia. “What Is Forensic Analysis and Why Is It Important for the Security of Your Infrastructure”. VirtualMetric – Infrastructure Monitoring Blog,Web.