Dealing with imbalanced data is a prevalent problem while performing classification on the datasets. Many times, this problem contributes to bias while making decisions or implementing policies. Thus, it is vital to understand the factors which causes imbalance in the data (or class imbalance). Such hidden biases and imbalances can lead to data tyranny, and a major challenge to a data democracy. In this chapter, two essential statistical elements are resolved: the degree of class imbalance and the complexity of the concept, solving such issues helps in building the foundations of a data democracy. Further, statistical measures which are appropriate in these scenarios are discussed and implemented on a real-life dataset (car insurance claims). In the end, popular data-level methods such as Random Oversampling, Random Undersampling, SMOTE, Tomek Link, and others are implemented in Python, and their performance is compared. Keywords - Imbalanced Data, Degree of Class Imbalance, Complexity of the Concept, Statistical Assessment Metrics, Undersampling and Oversampling 1. Motivation & Introduction In the real-world, data are collected from various sources like social networks, websites, logs, and databases. Whilst dealing with data from different sources, it is very crucial to check the quality of the data [1]. Data with questionable quality can introduce different types of biases in various stages of the data science lifecycle. These biases sometime can affect the association between variables, and in many cases could represent the opposite of the actual behavior [2].

It's been a while since cash was king here in the U.S., but in other parts of the world, such as South America, paper money has managed to retain its grip, albeit slightly diminished as a result of the pandemic's many lifestyle changes that exposed cash transactions as cumbersome and risky. It's a reality that Brighterion's Sudhir Jha told PYMNTS has resulted in a pan-regional progression that is moving more Latin American consumers into digital payment solutions, even though credit penetrations remain low. "There's not a lot of historical data about consumers who are new to digital ecosystem -- that's why there is a desire to go directly to an AI-based solution in many cases, because you want a solution that works today, but also scales really well and attracts more and more customers to your system," Jha explained. That growing regional need for artificial intelligence (AI)-based solutions is what motivated Brazilian insurer Porto Seguro to team with Brighterion. Announced recently, the engagement leveraged Porto Seguro's analytical expertise in combination with Brighterion's AI technology to build high-performance models custom-created to identify risks better.

In a rapidly changing financial environment, the race is on for AI implementation. It's no longer if an organization is using AI, it's when they get it and how they implement it in business. The big question is should they develop in-house, buy off the shelf or get a custom solution? Here's what the options offer and what to look for when choosing an AI solution. Surveys of major financial organizations show they recognize the need to use AI to leverage their complex data and mitigate business risks. MIT Sloan Management Review did a survey of more than 3,000 managers and interviewed executives, learning that a majority of companies have tried developing AI, but only 1 in 10 gained significant financial benefits.