The Proven Benefits of Network Analysis

The financial sector is experiencing an increasing incidence and impact of fraud, partly because modern fraudsters are more difficult to detect. These perpetrators blend in seamlessly with legitimate customers, and as a consequence, APP fraud fed by social engineering is on the rise. They are highly organized and use sophisticated tactics to exploit organizational weaknesses.

To combat fraud, many institutions rely on transaction monitoring systems, which are effective for individual cases. However, a broader approach is needed to monitor customer behavior across multiple cases, business lines, and systems to identify those operating "below the radar." Unfortunately, few financial institutions have a comprehensive understanding of customer behavior across various lines of business, products, payment transactions and accounts, often due to a lack of analytical and ML based technologies to detect trends and suspicious activities.

The current competitive landscape and margin reduction has compelled financial institutions to reduce spending and operate with fewer resources. Inefficient manual processes used to detect fraudulent activity waste valuable time and resources, a luxury these institutions cannot afford. However, adopting suspicious network identification can address these challenges effectively.

Evolving fraud trends:

Increasingly sophisticated criminal tactics and the explosion of social engineering makes detecting fraud difficult and preventing it even more challenging.

Growth of organized crime networks:

Such networks are drawn to the low-risk, high-return nature of fraud based on social engineering.

Analytical limitations:

Current systems don’t support robust analytical and ML based modeling, and there’s usually a lack of ?intuitive interface for understanding customer relationships in a meaningful way when it comes to detecting suspicious patterns.

Data management challenges:

Siloed product lines and geographically dispersed customers make it hard to access the right information in the right format and detect the important anomalies that can prove to be relevant enough.

Suspicious network identification provides a comprehensive enterprise fraud framework that prevents, detects, and manages financial crimes across all business lines. Ideally, it combines detection, alert management, and case management with investigative oriented workflows, content management, and advanced analytical capabilities. This approach ensures accurate fraud detection, enhances case management insights, and improves operational efficiency while reducing overall costs.

Advanced, large-scale network analytics:

This is required to work across internal and external data sources to link customers, accounts and payments based on common attributes or more subtle patterns of behavior.

Network visualization is key to investigators:

Investigators will actually see network connections so they can uncover previously unknown relationships and conduct more effective and efficient investigations.

Advanced profiling capabilities:

The risk scoring of customers based on rules, is not enough however links to known fraudulent networks can be key to a real detection.

Alerts from multiple systems should be consolidated into a true enterprise wide view of fraud.

Benefits:

Network analysis when well configured enables financial institutions to achieve unrivaled detection rates, more accurately identify compliance risks and reduce fraud losses.

How a holistic view of fraud can be achieved ?

This allows, when implemented,? a view which goes beyond the typical customer profiling in order to provide a holistic view of fraudulent activity including related fraudsters and a much clearer understanding of customer behavior.

This approach when adopted will enable:

? the extraction of data from all relevant sources that can contribute to a better detection ?including third-party related data.

? the creation of a complete set of connections between all entities together with their key linking attributes.

? the ingestion of risk scores providing an aggregated score at network level. Because of a metadata configuration when being set up, all records will be linked exhaustively based on combinations of attributes within the data. Then, through the use of graph detection based algorithms, common entities will be identified and collapsed to produce meaningful views of entities within networks.

No other approach can provide a good enough level of detection, automation, ease of use and proven ROI for financial institutions as long as it can:

? Overcome poor data quality issues associated with imperfect matching and wrongly linked entities.

? Can operate on high amount of records and is fully scalable both in real time and in batch.

? Supports incremental updating of networks as new data and entities are added.

? Ingests employee data and audit records for an advanced detection of insider or collusive fraud.

Time has come for a quick and easy adoption of such approaches, with a major benefit, which is the reduction of noise, confusion, and false positives, reverting back into an earlier and more accurate detection under cost control, therefore less exposure to a risk of non compliance.