Optimization, AI in Banking & Top of the Wallet
Closer Consulting
A Data Science company specialized in Business Intelligence, Advanced Analytics and Artificial Intelligence.
Optimization
Why do high-performing Companies optimize their business with AI?
🤖 As we see companies looking to gain a competitive edge in today's fast-paced market, the role of AI in the optimization of their operations is as critical as ever. From enhancing operational efficiency to driving innovation, AI is proving to be a game-changer.
📋 However, integrating AI for optimization poses challenges, such as a robust data infrastructure, skilled talent and a clear strategic vision. In manufacturing, AI-driven optimization enhances predictive maintenance, minimizing downtime and maximizing productivity. Retailers are utilizing AI to refine inventory management, personalize customer experiences and predict market trends, ensuring they meet consumer demands effectively. Financial institutions are deploying AI to optimize trading strategies, detect fraud and manage risks more efficiently.
🌱 Furthermore, AI is playing a crucial role in sustainability efforts. By optimizing energy usage and minimizing waste, companies can adopt more sustainable practices, contributing to a greener future.
📈 AI optimization empowers companies to operate smarter, faster and more efficiently, making them more agile and resilient in a dynamic business environment. This strategic use of AI is a key factor behind the sustained success of high-performing organizations.
AI in Banking
Closer Project
Top of the Wallet
🏦 A financial institution sought to increase revenue from credit card commissions. They suspected a significant portion of their high-value customers were using credit cards from other institutions. To address this, they built a customer data mart specifically focused on credit card transactions. This data mart allowed them to analyze customer spending habits and identify high-value individuals.
💳 Leveraging this data, the bank developed a machine learning model to score these high-value customers. The model particularly focused on those not using the bank's own credit card. This essentially highlighted customers likely relying on competitor credit cards. Armed with these insights, the bank launched a targeted campaign offering their credit card to these high-value, non-user customers.
📊 The campaign proved successful, with 15% of targeted customers adopting the bank's card and spending within their desired categories. This resulted in a significant boost to the bank's credit card commission revenue.