Forensic data analytics: new technology for financial crime investigation and risk management
As fraud and other financial crime schemes grow more sophisticated and costly, organizations require advanced solutions to analyze risks, improve prevention systems and enhance investigations. In this context, forensic data analytics is emerging as a key asset in the future of financial crime and fraud risk mitigation.
Forensic analytics are now starting to drive investigations as analysts seek out more efficient ways to understand patterns of fraud, money laundering, and other financial crime. This article looks at what forensic data analytics is and what role it plays in investigation and risk management. We’ll also look at how graph technology and network analysis can be part of the solution to facilitate and accelerate the analytics process.
What is forensic data analytics?
Forensic data analytics is a branch of digital forensics and is defined as the analysis of large amounts of data for patterns of criminal activity. Investigators use forensic data analytics to counter various crimes including fraud, cybercrime, data theft, and more. It combines advanced analytics techniques with forensic accounting and investigative methods.
Forensic analytics frequently involves integrating and analyzing large datasets, sometimes combining both structured and unstructured data. It is often a process that happens in four stages: data acquisition, examination of data, analysis, and reporting.
Forensic analytics can deliver key information on suspicious activities, anomalies, the timeline of given incidents, etc. even when investigations involve heterogeneous data sources. It helps analysts identify needles in their haystacks of data to mitigate fraud risk, AML risk, and more.
Forensic data analytics against fraud and financial crime
Being able to manage risk has always been essential for organizations, particularly in the financial services sector. But with digitization and global events like the Covid-19 pandemic has come more diverse and costly fraud schemes. The cost of fraud for US financial institutions has increased between 6.7% and 9.9% compared to before the pandemic. (1)
It has become even more critical in this context to effectively manage financial crime risk. Failing to do so can cost an organization money, its reputation, and its customers. Businesses also have increasingly stringent regulatory requirements to comply with.
The limitations of legacy systems
Organizations are experiencing the limitations of legacy systems as they attempt to uncover the insights and information they need within their data as they face more challenging financial crime threats. Traditional rules-based systems often limit analysts to a single dataset and tend to give yes or no answers without much nuance or context. These systems also turn up a large percentage of false positives, wasting time and resources.
Towards advanced data analytics
Forensic data analytics, with its reliance on AI and machine learning, network analytics, and natural language processing (NLP) techniques offers analysts and investigators the insights they need to understand both criminal motives and methods. This type of analytics can combine multiple data sources to gain a complete picture. It breaks down fraud and other white-collar crimes to get to the bottom of a problem. By understanding the root causes, analysts can also improve prevention methods to stop fraud schemes, money laundering, or other crimes from recurring.
Organizations using forensic analytics to monitor for risk are better equipped to manage threats and have fewer blind spots in their defenses. In seeing the big picture, organizations can proactively pinpoint where they have vulnerabilities and take action before it’s too late. And if fraud or other criminal activities do occur, they are better able to respond quickly and mitigate damage.
Network analysis and forensic data analytics
Forensic data analytics involves several different types of technology working together to extract actionable insights from within the data. This might include different types of machine learning modeling, text analytics, and network mapping and analytics. All these systems working together essentially ensure that no stone goes unturned within your data.
What role does network analytics, also known as graph analytics, play in forensic analytics as a whole? Network analytics is based on a data structure that analyzes not only individual data points but also the relationships within that data. It quickly analyzes which entities are connected to each other and how. In this context, it’s a powerful tool to analyze and explore the networks around fraudsters and money launderers and to reveal even very complex illegal schemes.
Working alongside a graph database, a graph analytics platform can break down the silos within data enabling better data integrity and consistency, thereby enhancing monitoring, detection, and prevention. Accessing all your data in one place and being able to analyze the connections within avoids important insights falling by the wayside. (2)
Network analytics easily combines with predictive tools based on AI and machine learning. And it offers scalability to accommodate increasing amounts of data or additional data sources.
Deloitte: delivering disruptive technology to more efficiently combat financial crime
Read about how Deloitte Switzerland is applying graph technology to forensic data analytics:
(2) "Using graph data analysis to combat financial crime," Deloitte. https://www2.deloitte.com/ch/en/pages/financial-services/articles/graph-data-analysis-financial-crime.html
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