Credit card fraud is a major issue. Last year, the Nilson Group reported that credit card fraud losses climbed to as much as $28.65 billion across the globe. EMV Chip Cards were supposed to fix this problem, and while we have seen a decrease in CP (Card Present) fraud, there has been a rapid increase in CNP (Card Not Present) fraud.  By 2025, total payment card volume worldwide is projected to be $56.182 trillion, with gross card fraud globally expected to be $35.31 billion.

Some of the typical challenges which are faced while solving the credit card fraud problem are:

• Large number/High volume of transactions on a daily basis
• The requirement of high processing power to handle real-time transactions
• Accuracy of the decision
• Creating a robust machine learning/AI model

How can we help organizations like you looking for machine learning solutions to counter credit card fraud?

We build custom machine learning pipelines for you to help you make more effective decisions when countering fraud. As part of these machine learning pipelines, the following stages are incorporated to make the entire process seamless and effective:

• Ingesting Data From various sources
• Data Cleaning and Data Preparation
• Custom Machine Learning
• Feedback and Logging
• Reporting

We use various tools to help architect the solution either on-premise or on the cloud. We work with cloud tools such as Databricks, Amazon Web Services, and others depending on your requirements.

How will incorporating machine learning enhance the current process?

Higher Accuracy

Working with Machine Learning models provides a much higher accuracy over time than manually trying to identify fraud based on a set of rules. Models have the capacity to learn and provide assistance to make decisions.

Avoid Manual Work

Creating Machine Learning Model pipelines will greatly reduce the amount of manual work to be done by the team. Having an automated system in place will greatly help in reducing the amount of time and effort.

Fewer False Positives and Negatives

One of the main benefits is to avoid re-iterating between correctly identified and incorrectly identified cases. We want to minimize identifying frauds erroneously. Machine learning models can focus on reducing these and hence greatly improve the accuracy of being able to detect frauds over time.

The Procurement department is a key unit of any company, irrespective of the industry. Procurement is not only limited to product based companies in terms of raw materials, but it also takes into account the services that a company outsources to other vendors. For example, all the laptops that you use at your work has been procured from some vendor. The paradigm of Procurement is vast. The purchasing department should have optimized strategies that makes the procurement process smooth and efficient!

But how do we optimize these processes?

Procurement Analytics, to the rescue! Procurement Analytics takes into account procurement data from various sources and drills deep into the data to extract valuable and actionable insights. Organizations can leverage procurement analytics to make data driven purchasing decisions and manage effective vendor relationships.

Procurement Analysis can be divided into three broad categories:

Vendor Analysis: Vendor Analysis gives a better solution on selecting the best suppliers from a range of other competitive suppliers. Is your organization choosing top tier suppliers? Is your vendor providing the best price and discount than others?

Spend Analysis: One of the key factor in business is cost saving and it becomes important to optimize the spend. Analysis around spends and discounts across months are helpful to understand when your organisation can plan for purchases.

Item Analysis: You can closely look into the commodities being purchased and the various suppliers supplying the same commodity. This gives an organisation a better understanding of the cost incurred and discount offered by the suppliers.

If you haven’t optimised your Procurement strategies yet, below is a glimpse on how you can go about it!

Procurement analytics reports and dashboards – How do I go about doing it?

Below is a short video demo on how PowerBI, a growing Microsoft Business intelligence tool, has been used on a sample procurement data, to give insights to the business about vendor, supplier and products. The demo walks through multiple reports and drill downs to give the organisation deeper information. It also shows the ease of use of a tool like PowerBI with huge data, both for simple and complex reports.

The above report gives an overview about the total spend of an organisation on procurement. It gives an understanding of the various vendors that this organisation outsources its products and services and the amount each vendor charges for the same products and services. This report also gives an essence of the savings attained due to discounts offered by various vendors and the months during which the vendors offer discounts. This will help the organisation to plan its procurement months in order to achieve maximum savings and lesser spend.

This report further gives an overview of the items that the company spends the most. Drilling down further gives the details of the items being purchased and the respective vendors related to the items. It gives a better understanding about the vendors that gives better price for the same item. This way the organisation is able to select the most profitable vendor.

Enhancing the reports

In our next case study, we will talk about how such reports can be extended to have elements of predictive analytics and machine learning which will further help the business use these insights to drive more productivity and reach out to customers better. Systems such as recommendations, price prediction, understanding and planning product procurement and others can enhance the usability and effectiveness of such reports.