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AI and Big-Data are widely used across many industries. However, there are 3 major pain points waiting to be addressed. They are sub-optimal accuracy, low efficiency, and poor interpretability. As more and more enterprises are moving through digital transformation, many of them have turned to graph technology providers for help. The core promise of graph database (or graph computing) is XAI, which originally stands for eXplainable AI, but has been loaded with more meanings, particularly in the sense of accelerated AI and augmented AI. In short, graph augmented XAI.
This case study showcases a top-tier retail bank’s leverage of Ultipa’s graph database and graph algorithms to significantly improve their credit card business performance.
The credit card spending (turnover) prediction is to forecast bank-wide credit card spending over a certain period. Grounded on historical transactions and other relevant factors, the prediction results enable credit card issuers and financial institutions to make informed decisions about credit limits, promotions, and marketing strategies. By accurately predicting customer spending behavior, those companies can tailor the offerings to individuals and fine-tune bank-wide business strategies to improve loyalty, maximize revenue, and lower credit-induced liquidity risks.
Significance
This use case demonstrates the power of combining graph technology with traditional ML/DL techniques to achieve significant improvements in business scenarios. By incorporating Graph Augmented Intelligence (XAI or GAI) into existing AI/ML/DL systems, companies can experience faster time-to-value, lower TCO, and higher ROI. As the field of AI continues to evolve, augmented intelligence will become an indispensable tool for businesses, providing a powerful combination of insights and predictive capabilities. XAI and GAI perfectly complements BI, unlocking new possibilities and enabling companies to elevate their operations to the next level. With XAI/GAI, businesses can empower themselves to reach unprecedented levels of efficiency and future success.