Falcon

Falcon is an AI-based which was used to detect any kind of malicious
transaction or any anti-money laundering transaction on the system.

Introduction.

Anti-money laundering (AML) transaction monitoring software allows banks and other financial institutions to monitor customer transactions on a daily basis or in real-time for risk. By combining this information with analysis of customers' historical information and account profile, the software can provide financial institutions with a "whole picture" analysis of a customer's profile, risk levels, and predicted future activity, and can also generate reports and create alerts to suspicious activity. The transactions monitored can include cash deposits and withdrawals, wire transfers, and ACH activity. Providing software to analyze transactions in an attempt to identify transactions or patterns of transactions is a completely different industry where the data and the information architecture plays a crucial role..

Falcon used patterns of the users and the patterns of the transactions made on the platform. Falcon is used to monitoring bank customer transactions on a daily basis and, using customer historical information and account profile, provide a "whole picture" to the bank management. Transaction monitoring can include cash deposits and withdrawals, wire transfers and ACH activity. In the bank circles, these applications are known as "AML software".

The Problem.

Falcon was the application, which was designed for the banking professionals, the major challenges while designing the application was the application had a lot of information, which was required to be shown on the screen at a single instance. If dealing with the heavy data was one part, challenge that we were facing the major challenge was making the application intuitive and efficient for the users while performing the tasks. While designing such transaction monitoring application picking of the right colors, icons and maintaining a proper visual language played a vital role. Along with these, the kind of data the users were looking at was the most important factor to decide the kind of emotions that were invoked.

We wanted to use more amount of statistics in the form of plain graphs or tables or pie charts to make it easy to summarize the information that was required to be scanned. Another major challenge was coming up with the right environment and more intuitive user experience that would help the users to perform the tasks in a better way. These users had to work on the application on a daily basis; we had to ensure that the application has a seamless navigation process that would allow the users to go back and forth in the application.

Users & Audience.

Persons or organizations who are trying to generate income through illegal actions designed falcon for companies to detect suspicious activities. The major audience of the application was the banking professionals who were the internal employees of the companies. These users were professionally trained accountants and manager from all departments; they used the software in various transactions right from evaluating new customers and suppliers to monitoring heavy transactions made on the system to detect any kind of malicious actions on the system.

Additionally, the falcon was also designed to detect suspicious and fraudulent activities that affect the profitability of the firm. Falcon was designed keeping a varied set of audience and it was designed to be robust and responsive so that people will not face multi-device problems.

The Design Solution .

The major goal while designing applications like falcon was to help the user do what they want to do when interacting with the business. The flacon was an enterprise application that contained sensitive data pertaining to the company. The complex nature of the application and the lack of examples of patterns that work or do not work in specific scenarios was one of the major concern while designing the application. We ensure that we ensured that we use the industry standard visual language as the users of the application were the subject matter experts of the domain and we focused on improving the User experience of the application by keeping it visually close to the applications that are available in the market.

We focused initially on the information architecture of the application where we used organized the information into sections and labeled the sections to make it easy. We went with the traditional tables as the amount of data that was to be displayed on the huge. Along with tables, we used analytics to summarize the information and organize the information in a better way. We provided options for the user to compare the data and look at specific order of information to help the end user to perform the task more efficiently.

After the launch of the application, it was initially tough for the network administrators to adapt to the system. Soon after getting used to the device, it increased the efficiency of the users exponentially and with the new geeky devices on top, the eyes the admins could diagnose the systems in a much faster rate than by using the traditional way of diagnosis. We understood that Designing for Glass challenged us to overcome various technological limitations and unusual contextual scenarios. It also paved a path to solve problems and to stay at the edge and understand the technology in a better way.

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