How AI-powered analytics transformed fintech marketing strategies,
improving customer retention and engagement.
Aktif Bank x B2Metric Use Case
With its innovative business models and technology investments, Aktif Bank continues to serve as Turkey's most extensive fintech ecosystem.
Aktif Bank, with its fintech ventures and business partners, provides 360-degree services to more than 10 million customers across over 10 lines of business, ranging from banking to insurance to transportation to payment systems to technology to entertainment, guided by its customer-centric approach.
Challenge Overview
Analyzing Vast Customer Data for Informed Decision-Making
It is a daunting task in the banking industry to analyze extensive customer data and make informed decisions. To draw accurate predictions and insights, a meticulous study is conducted, examining the demographics and bank account information of customers. In order to streamline the decision-making process, innovative solutions are needed to navigate the complexity of big banking data.
Challenges in Predictive Analytics
Big banking data can be complex, so predictive analytics is challenging. In order to ensure precise predictions based on customer behavior, financial trends, and demographics, it is essential to decipher meaningful information from the vast dataset. For valuable insights, advanced solutions are needed since traditional methods are often limited.
Due to these challenges, Aktif Bank opted for a solution based on B2Metric. Despite the inherent difficulties in data analysis, B2Metric simplified the process, making it remarkably easy to analyze vast datasets and take targeted actions on the right customers. Aktif Bank was able to improve its decision-making capabilities by using this solution, which simplified the complexity of big banking data.
Solution: B2Metric Advanced Predictive Analytics
With the help of the B2Metric IQ Customer Journey predictive solution, Aktif Bank, one of Turkey's leading financial institutions, embarked on a transformative journey. To make predictions with precision, the bank employed a number of machine learning models based on a detailed examination of customer data.
High-Reliability Model Validations
The implemented solution prioritized accuracy through high-reliability model validations. Aktif Bank has a commitment to reliability that has laid the foundation for informed decision-making, and rigorous testing ensured that the machine learning models functioned with high accuracy in real-world scenarios.
Adaptive Learning for Continuous Improvement
As new data accumulated, the predictive models evolved and refreshed to provide continuous improvement. An adaptive learning approach was used to ensure Aktif Bank's decision-makers had access to the most up-to-date insights, enhancing the effectiveness of their strategies.
Optimizing Marketing Budgets
In addition to providing actionable insights, the micro customer segments helped Aktif Bank optimize its growing marketing budgets. In response to detailed reports, Aktif Bank devised targeted marketing strategies to increase customer loyalty and satisfaction. This approach significantly improved the efficiency and effectiveness of Aktif Bank's marketing efforts.
Non-Performing Loan (NPL) Scoring for Risk Mitigation
By leveraging B2Metric, Aktif Bank improved the performance of its Non-Performing Loan (NPL) scoring model by creating features. To gain a comprehensive understanding of loan risk, past loan applications as well as demographic characteristics were strategically used.
Cleaning and Analyzing Data
In order to ensure accuracy of predictions, B2Metric's solution went beyond model creation. A meticulous process was conducted to clean dirty data from customers' past loan applications and analyze information entered during these applications. As a result, the NPL scoring model was further refined in terms of its predictive capabilities.
Proactive Churn Reduction with Churn Prediction
The insights gained from accurate churn predictions led to proactive measures by Aktif Bank that resulted in a 36% increase in user retention. As part of this strategic approach, the bank took precautions against customers who would likely churn, maintained valuable customer relationships, and generated significant cost savings.
Optimized Loan Application Approvals
With enhanced decision-making capabilities, Aktif Bank was able to accurately identify which loan applications should be approved. Using the advanced features created through the solution, the bank optimized its loan approval process, ensuring efficient resource allocation and enhancing the management of overall risk.
B2Metric is a Top-Notch Automated ML Modeling Platform For Banking And Finance. We use B2metric ML Studio for our Banking customer retention, segmentation, behavior analysis and propensity predictions.
M. Yaşar Sungur
Head of CRM
Challenges in Banking
Navigating the complexities of customer engagement, retention, and marketing spend optimization in the fintech industry.
The Solution: B2Metric Adaptive Learning for Fintech
To tackle these challenges, Papara leveraged B2Metric's advanced machine
learning models, designed to optimize fintech marketing strategies.
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