Forensic Data Analysis

What Is Forensic Data Analysis?

Forensic data analysis refers to a process that involves analyzing substantial data to identify patterns of activities concerning financial crime. Such an analysis delivers vital information about anomalies, suspicious activities, etc. Businesses can utilize it to counter different crimes, such as financial fraud, data theft, etc.

This process combines investigative techniques, advanced analytics methods, and forensic accounting. Moreover, it may combine unstructured and structured data. It is a crucial element in bringing informed decision-making, conflict resolution, and business transparency to an extensive range of matters. Organizations can utilize different forensic data analysis techniques, like data carving, file system analysis, etc., to prevent financial fraud.

• Forensic data analysis refers to a process that analyzes substantial data related to financial crime. Its purpose is to spot, thwart, and prevent illegal activities that negatively impact an organization, for example, frauds, cyber-attacks, and more.
• Some noteworthy forensic data analysis techniques include data carving, live analysis decryption, data acquisition, and network analysis.
• There are various benefits offered by this analysis. For example, it helps retrieve deleted data and revise policies. Moreover, the process helps spot vulnerabilities in applications, infrastructure, or websites.
• A disadvantage of the process is that the underlying data quality determines its effectiveness.

Forensic Data Analysis In Finance Explained

Forensic data analysis refers to a process that reviews structured data of activities related to any form of financial crime. It aims to identify and analyze patterns regarding fraud or other suspicious activities. On account of the data type, this process often focuses more on the data’s content than on the database that consists of the information.

This process enables businesses to make more targeted and informed decisions, especially regarding internal controls. This helps in reducing the risk of financial crime. This analysis is commonly used in different sectors, such as healthcare, commerce, finance, etc. Forensic analysts spot irregularities and play a vital role in detecting illegal activities and ensuring data integrity.

This process involves the following steps:

1.  The first step involves developing and following strict procedures and policies for every activity associated with this type of analysis.
2. Next, forensic investigators need to evaluate the potential evidence in a financial crime. Then, they have to determine the data source and integrity prior to entering them as evidence.
3. The third step involves preparing a rigorous, comprehensive plan to obtain evidence. Note that all data must be recorded, preserved, and documented during, after, and before the acquisition of the evidence.
4. After that, a forensic investigator needs to examine the potential evidence. Individuals must remember that there must be procedures in place to copy, store, and retrieve evidence within the suitable database. Analyzing the information may involve different approaches, for example, utilizing analysis software to seek data archives having certain keywords or file types.
5. Lastly, forensic investigators must maintain a record of the hardware and software specifications and include every technique utilized in the investigation.

Techniques

Some key techniques used by businesses to carry out this analysis are as follows:

• Data Carving: This technique involves rebuilding or recovering files from raw data utilizing footers, file structures, and headers.
• Log Analysis: Log parsing involves accumulating information regarding access times, device interactions, user activities, etc.
• Keyword Searching: When businesses use this method, they need to run keyword searches to determine which files, communications, and documents are relevant.
• File System Analysis: This method involves examining file systems to analyze metadata, reconstruct timelines, and recover deleted files.
• Data Acquisition: The data acquisition technique requires businesses to utilize drive imaging tools, writer blockers, and other techniques that are forensically sound for extracting information without changing the original evidence.
• Network Analysis: Network analysis involves inspecting router logs, server logs, and network traffic to comprehend data flows.
• Decryption: This method involves circumventing or cracking encryption to get access to encrypted and password-protected data.
• Cross-Drive Analysis: Also referred to as anomaly detection, this method involves cross-referencing and collating information across different computer drives to preserve, analyze, and find any data relevant to a particular investigation.
• Live Analysis: It examines operating systems of computers utilizing custom forensics to obtain evidence in real-time.

Examples

Let us look at a few forensic data analytics examples to understand the concept better.

Example #1

Suppose company ABC was a financial services provider. At the end of financial year 2023, the organization hired forensic data analysts to identify financial fraud. Forensic investigators identified financial fraud within the organization. Through comprehensive analysis of company-related financial records data, they found that the salary for a specific department was increased at the beginning of the financial year without any authorization.

Further investigation revealed that none of the employees received the excess salary as the person disbursing the salary had transferred the amounts to different bank accounts outside the organization. The forensic investigators documented all the evidence and sent a report to ABC. Thus, besides exposing the financial crime, forensic data analysis provided evidence for the legal actions against the person responsible.

Example #2

Reveal, the international artificial intelligence-powered investigations and review platform announced a collaboration with CBIT Digital Forensics Services (CDFS) on September 11, 2023. The purpose of the partnership is to offer improved eDiscovery tools and solutions to corporate and legal clients worldwide.

Reveal’s chief executive officer Wendell Jisa said that everyone in the company is excited regarding the collaboration. This is because, according to her, CDFS’s reputation concerning forensic data analysis is second to none. She believes that the combination of Reveal’s eDiscovery platform and CDFS’s deep expertise will redefine digital investigations taking place in Australia and beyond. This objective can certainly be fulfilled, considering CDFS’s best-in-class forensic data solutions have been instrumental in helping corporate, government, and legal clients with complex data challenges.

Benefits

Some key advantages of forensic data analysis are as follows:

• Such an analysis provides key information regarding websites, applications, or infrastructure. Security experts try to fix the vulnerable areas based on this information.
• It provides an extensive review of trends on the basis of current and historical information. This review allows businesses to identify the scope or areas for improvement.
• The outcomes of this analysis can perceive, develop, or revise policies. Such outcomes support businesses to determine certain areas that require employees or workers or employees to gain additional expertise and training.
•  This analysis involves deploying complex techniques and tools to recover deleted business-related data.
• The process reviews financial transactions and helps businesses determine if the internal controls are functioning efficiently.
• It helps businesses develop strategies that can stop hackers from getting access to a device or network. 
• Businesses use this process to examine substantial data in internal investigations or audits. This enables forensic investigators to carry out more complicated tests utilizing technology that minimizes human errors.

Limitations

Let us look at some key disadvantages of this process.

• The effectiveness of the analysis depends on the data quality. If data is incomplete or inaccurate, the analysis may fail to prevent financial crime.
• Encryption can make it challenging to get access to data on a network or device, which, in turn, makes it difficult for foreign investigators to accumulate evidence.
• Ethical and legal considerations can restrict access to specific digital evidence.

Frequently Asked Questions (FAQs)

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