Transforming Financial Crime Investigations

A case study in defining decision graphs to improve quality & consistency in AML investigations

AML QA Processes are Flawed Today.

Banks’ anti-financial crime efforts today have an intractable challenge of balancing the need to continuously improve consistency of investigation performance and quality with the
requirement of maintaining the operational efficiency of a cost center. 

What if the right balance of technology and expert resources could change the paradigm, moving teams from firefighting to being on the offense? Decision graph technology is the answer to transforming the quality and consistency standards for risk investigations at banks globally.

In this paper, we will demonstrate how decision graph technology was deployed to Tier 1 and Tier 2 banks to automate and augment investigations, and through this process, overall Quality Assurance Programs were enhanced with QA fail rates that were improved by up to 80%.

Case study insights include:

Current QA Processes + Performance variation

Learn about current QA processes and outcome assessments operation in global banks and how considerable differences in performance greatly impact the volume of different QA outcomes.

Fail Error Reduction

Banks can break a high fail error cycle by identifying key driver for failure and targeting specific interventions. Recognize these drivers earlier.

The Human and Machine Force Multiplier

Automated machine investigation technology has been proven to directly impact quality. How can this work in your organization?

Augmented Performance Improvement

Understanding investigator performance at an individual level helps augment and transform performance across whole teams. Learn how.

Minor Faults versus Fail Errors

Review the failings if current evidence trails coupled and the major drivers behind minor fault in relation to errors.

Real-time Quality Assurance

Real-time, holistic investigation analysis will support human investigators at the point of need. See what this can look like.

Download Case Study: Defining Decision Graphs to Improve Quality & Consistency in AML Investigations

AML Investigations Management, Re-imagined.
Nasdaq's Automated Investigator drives up to
improvement in efficiencies
AML Auto Invest Demo
Nasdaq Automated Investigator for AML
The Nasdaq Automated Investigator for AML is a machine-based solution that distills complex human decision-making at scale to automate and augment review of alerts produced from Transaction Monitoring Systems during AML risk investigations. Empowered with clear rationale and evidence at all phases of the investigations, banks leveraging the solution can re-focus analysts on high-value activities while improving QA, consistency, efficiency and productivity.
How can the Automated Investigator empower your teams?
Nasdaq's Automated Investigator drives up to
improvement in efficiencies

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