AB S TRACT This study evaluates the performance of various classifiers in three distinct models: r esponse, r isk, and r esponse - r isk, concerning credit card mail campaigns and default prediction. In the r esponse model, the Extra Trees classifier demonstrates the highest recall level (79.1%), emphasizing its effectiveness in identifying potential responders to targeted credit card offers. Conversely, in the r isk model, the Random Forest classifier exhibits remarkable specificity of 84.1%, crucial for identifying customers least likely to default. Furthermore, in the multi - class r esponse - r isk model, the Random Forest classifier achieve s the highest accuracy (83.2%), indicating its efficacy in discerning both potential responders to credit card mail campaign and low - risk credit card users . In this study, we optimized various performance metrics to solve a specific credit risk and mail responsiveness business problem.

Credit scoring is a systematic approach to evaluate a borrower's probability of default (PD) on a bank loan. The data associated with such scenarios are characteristically imbalanced, complicating binary classification owing to the often-underestimated cost of misclassification during the classifier's learning process. Considering the high imbalance ratio (IR) of these datasets, we introduce an innovative yet straightforward optimized activation function by incorporating an IR-dependent asymmetric adjusted factor embedded Sigmoid activation function (ASIG). The embedding of ASIG makes the sensitive margin of the Sigmoid function auto-adjustable, depending on the imbalance nature of the datasets distributed, thereby giving the activation function an asymmetric characteristic that prevents the underrepresentation of the minority class (positive samples) during the classifier's learning process. The experimental results show that the ASIG-embedded-classifier outperforms traditional classifiers on datasets across wide-ranging IRs in the downstream credit-scoring task. The algorithm also shows robustness and stability, even when the IR is ultra-high. Therefore, the algorithm provides a competitive alternative in the financial industry, especially in credit scoring, possessing the ability to effectively process highly imbalanced distribution data.

By Ateesh TankhaConsider the following bit of reasoning: Grandmaster Garry Kasparov can anticipate any chess move by any man. Artificial intelligence (AI) can anticipate any chess move by Kasparov. So, AI can anticipate any chess move by any man. This argument holds as long as it refers only to the game of chess. The moment'any move' refers to something else -- say, a malafide intention to commit fraud -- all bets are off.

Credit scoring is without a doubt one of the oldest applications of analytics. In recent years, a multitude of sophisticated classification techniques have been developed to improve the statistical performance of credit scoring models. Instead of focusing on the techniques themselves, this paper leverages alternative data sources to enhance both statistical and economic model performance. The study demonstrates how including call networks, in the context of positive credit information, as a new Big Data source has added value in terms of profit by applying a profit measure and profit-based feature selection. A unique combination of datasets, including call-detail records, credit and debit account information of customers is used to create scorecards for credit card applicants. Call-detail records are used to build call networks and advanced social network analytics techniques are applied to propagate influence from prior defaulters throughout the network to produce influence scores. The results show that combining call-detail records with traditional data in credit scoring models significantly increases their performance when measured in AUC. In terms of profit, the best model is the one built with only calling behavior features. In addition, the calling behavior features are the most predictive in other models, both in terms of statistical and economic performance. The results have an impact in terms of ethical use of call-detail records, regulatory implications, financial inclusion, as well as data sharing and privacy.

The world around is full of classifiers. Classifiers help in preventing spam e-mails. Classifiers help in identifying customers who may churn. Classifiers help in predicting whether it will rain or not. This supervised learning method is ubiquitous in business applications.

By Ashwini Anand When virtually everyone claims to use "artificial intelligence", "big data" or "machine learning" to "disrupt"/ "revolutionize" one industry or the other, I would not blame you for being sceptical about the much-touted AI revolution in Fintech. But, before you throw the baby out with the bathwater, let us delve a little deeper and understand if there is a real problem in lending and if AI can help solve it. Is there really a problem in the banking system? Banks and financial institutions, with some notable exceptions, are struggling with bad loans. According to India Ratings the average Impaired Asset Ratio - the sum of gross NPAs and restructured advances (a measure of the stress on a lender's balance sheet) stands at 12% of advances and is slated to rise to 12.5%.

When people ask me what I'm working on, I'm often confused about the depth I need to go to in my response. 'Artificial Intelligence' is way too broad for my personal satisfaction, and image understanding probably too specific. Nevertheless, every single time, I do get this completely unrelated follow-up question that infuriates me to my core. And I can't even blame the skeptic -- most people think artificial intelligence is some unknown, mysterious entity which is conspiring infinitesimally, and will eventually kill us all, since it can predict that Sausage Party is the next movie we'd want to watch after we've binge-watched Evan Goldberg flicks all night. That's what makes predicting your favourite music, or suggesting the correct phone app to use while you're taking a dump -- an easy task for machines.