This assessment is an individual report about the design of an OOP system using Data Structures and Algorithms. The report should follow the following structure using the actual case study that will be available on Moodle $($in assessment section$)$ by week $8.$

$1.$ Title Page

$2.$ Executive Summary

$3.$ Introduction

$4.$ Background

$5.$ Case Study and the Design

$5\mathrm{.}1$ Variables, Ranges and Keys

$5\mathrm{.}2$ Operations and the Justification $5\mathrm{.}3$ Algorithms and the Justification $5\mathrm{.}4$ Modifications

$6.$ Conclusion $7.$ References

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 complex