The reports of the increase in fraud, suspicious account activity, and money laundering have also increased spending on threat detection. Fraud impacts most industries, but financial institutions are often targets for the most advanced attacks. In financial institutions, these serious threats have historically been treated as IT problems, but are now seen as needing tighter integration with business analytics.
Summary of Findings from recent threat reports
- Since 2019, fraud success has increased by eighty-five percent (RT Insights).
- Merchants and financial services organizations will spend $9.3 billion annually on fraud detection and prevention by 2022 (RT Insights).
- Money laundering represents between 2 and 5 percent of global gross domestic product, or $800 billion to $2 trillion, according to the United Nations Office on Drugs and Crime (FISERV).
- Global finance controls haven’t kept pace with a globalized, digitalized world. The FinCen Files involving $2 trillion of transactions revealed in 2020 how some of the biggest banks have allowed criminals to move dirty money around the world (United Nations).
- The global banking system is coming under increasingly sophisticated fraud attacks, such that this long-running menace continues to siphon off billions of dollars from lenders every year. According to a report from the Association of Certified Fraud Examiners (ACFE), organizations are losing around 5 percent of their annual revenues to fraudulent activity (International Banker).
- A startling finding from KPMG survey: fraud costs are increasing at a faster rate than fraud risk management spend (International Banker).
- Open Banking may leave financial institutions vulnerable to more fraud (International Banker).
How might the financial services industry deal with these threats and risks to security?
The financial services industry, including banks, fintech and insurance providers, has a myriad of data from diverse sources. Regulators expect financial institutions to examine all forms of information, including structured and unstructured documents, web-based resources, social media and open sources, to inform transaction monitoring for suspicious behavior, fraudulent activity, and money laundering. They are finding that graph analytics lets them peer into connected data, detecting fraud many levels deeper than conventional analytics has access to.
What needs to be done
Graph-based Anomaly Detection (GBAD) approaches are among the most popular techniques used to analyze connectivity patterns in communication networks and identify suspicious behaviors (Science Direct). Quickly detecting patterns and anomalies can save financial institutions billions in losses, issues with regulators, and loss of confidence and trust by customers.
Katana Graph is a breakthrough graph intelligence platform that is changing the graph database paradigm. Coupling knowledge graphs with high performance computing enables organizations to not only avail themselves of sophisticated techniques to optimize AI, but also to employ it at the scale and speed of contemporary data demands (InsideBigData).
Katana Graph’s suitability to tasks like fraud detection is evident in levels of scale and performance unmatched in other data platforms, including 10-100x faster query performance and validated scalability to a 256-machine cluster.
