Fighting fraud with analytics: The future of investigations
How can organizations better identify and investigate fraud?
April 12, 2018
A blog post by Don Fancher, US and Global leader, Deloitte Services LP.
Financial crime is a $2.1 trillion threat to corporations today.
It is characterized by complex inter-connectivity. It is unconfined by geographic borders, by the type of organization, or by the industry.
And, it's characterized in many different forms:
- Electronic crime
- Money laundering
- Terrorist financing
- Bribery and corruption
- Market abuse and insider dealing
- Information security
Fraudsters proliferate and constantly adapt to more sophisticated controls and monitoring. Internal fraud alone costs the typical organization 5 percent of annual revenue.1
Organizations lacking antifraud controls are predictably worse off, suffering twice the median fraud losses of those with controls in place.2
Even organizations with antifraud controls can have their investigative efforts impacted by several factors including:
- Reliance on rules-based testing, which typically assesses and monitors fraud risks across a single data set, giving only a yes or no answer.
- Information silos that further impede analytics-aided investigative efforts. Organizations often struggle to balance the need for locally-tailored processes with the potential benefits of integrated data sharing, unintentionally creating barriers to investigative exploration as a result.
- Volumes of unstructured data amassing in organizations, such as videos, images, emails, and text files.
It's become increasingly clear that the current "check and report, catch and investigate approach" is not enough to deal with the emerging forms of financial crime and the increasingly sophisticated groups who commit them.
Rather, the risk mitigation mantra needs to be future focused—predicting the fraud before it happens by using the right combination of technology and subject matter specialists.
One step organizations can take to better identify and investigate fraud, as well as thwart future attempts, is to combine artificial intelligence (AI), machine learning, and statistical concepts of cognitive analytics with a skilled forensic investigation of fraudster motives and methods.
The four dimensions of this new analytics-driven approach will be covered in our blog series where we will also explore:
- Aspects of an analytics-driven fraud-fighting approach: The need for available and accurate data;
- Technologies that can be utilized to extract data and realize its value; and
- Ongoing monitoring of transactions and activities, a process that produces invaluable input for forensic analysis.
Stay tuned for our next post!