CASE STUDY:

Developing a Fraud Detection System for a Financial Institution

Introduction: In Australia, a large financial institution wants to improve its fraud detection capabilities by

implementing a new fraud detection system. The system will use data from various sources, including

transactions, customer profiles, and external data sources, to identify and prevent fraudulent activities. The

financial institution wants to ensure that the system is efficient, accurate, and easy to use.

Problem Statement: The financial institution wants to develop a fraud detection system that meets the

following requirements:

$1.$ Technical Complexity: The system should be able to process large amounts of data from various

sources in real$-$time and use advanced algorithms to identify fraudulent activities.

$2.$ Data Complexity: The system should be able to handle data from different sources with varying

formats, structures, and levels of complexity. The system should also be able to integrate data from

external sources, such as third$-$party data providers.

$3.$ Process Complexity: The system should be able to identify and prevent fraudulent activities in a timely

manner and coordinate with other systems, such as the customer support system, to resolve fraud

cases. The system should also be able to comply with relevant regulations and standards.

$4.$ Integration Complexity: The system should be able to integrate with the financial institution's existing

systems, such as the customer relationship management $($CRM$)$ system and the transaction processing

system. The system should also be able to share data with other systems, such as the anti$-$money

laundering $($AML$)$ system.

$5.$ User Complexity: The system should be easy to use and should provide a user$-$friendly interface for

fraud analysts to access the fraud detection data and manage fraud cases. The system should also

provide training and support for fraud analysts to effectively use the system.

$6.$ Project Complexity: The project should be managed effectively and efficiently, considering the

technical, data, process, integration, and user complexities. The project should also consider the risk

involved and have contingency plans in place.

The fraud detection system is a complex computing problem that requires a deep understanding of data

structure and algorithms. By considering the criteria outlined by the Australian Computer Society, the financial

institution can ensure that it has a comprehensive understanding of the complexity of the problem and can

Kent Institute Australia Pty$.$ Ltd$.$

Assessment Brief ABN $49003577302$ CRICOS Code: $00161$E RTO Code: $90458$

Version $2$: $18$th March $2024$ Page $3$ of $5$ TEQSA Provider Number: PRV$12051$

implement a solution that meets its requirements and provides the desired outcomes. The solution will likely

require the use of advanced data structures, such as graph databases and machine learning algorithms, to

effectively analyze the large amounts of data and identify fraudulent activities. Research should be conducted

to get some ideas about the graph database and ML