Industrial organisations involved
Financial Services, Retail
Technical Challenge
AI Analysis of Customer data and probability of default
The financial industry grapples with assessing the creditworthiness of customers. Traditional models rely on static data, ignoring dynamic factors affecting a customer’s financial behavior. The challenge is to create an AI-based system that continuously analyses customer big data, considering economic changes and personal spending habits, to predict the likelihood of default accurately.
The technical challenges are organising the collection and assessment of large volumes of data (luckily this data is more and more available with the increased adoption of SaaS ERP/CRM systems) and of course selecting the most precise Machine Learning Algorithm for Default Prediction, incorporating machine learning models to accurately predict defaults. These models are evaluated based on metrics such as the AUC (area under the ROC curve), accuracy rate, and Brier score, indicating their ability to make precise predictions.
Solution
Multi-Dimension AI Analysis of Customer Diverse Data
This solution leverages the power of Artificial Intelligence (AI) to analyze a wide variety of customer data from various sources, enabling businesses to gain deeper customer insights and make data-driven decisions. The Multi-Dimensional AI Analysis of Customer Diverse Data empowers businesses to unlock the hidden potential within their customer data, leading to a more customer-centric approach and improved business outcomes. AI-powered Risk Assessment Platform consists of different components and modules.
- Data Aggregation Module: Develop a module to aggregate customer financial data, including account transactions, loan history, and external economic indicators.
- Data Preprocessing: A vital step to ensure data quality, involving cleaning, normalization, and transformation for consistency across various data sources.
- Analytic Engine: An advanced machine learning model trained on historical data to identify patterns and predict future default risks with high accuracy.
- Interactive Dashboard: A user-friendly interface displaying real-time risk assessments, customer profiles, and predictive insights for loan officers and risk managers.
- Predictive Modelling: Incorporate predictive analytics to forecast future economic conditions and their impact on customer creditworthiness.
- Feedback Integration: Implement a feedback mechanism for incorporating loan outcomes and customer feedback to refine.
- Customized solutions: Create specifically trained models for business customers and end-user customers, serving different industries.
Business impact
By harnessing the power of Multi-Dimensional AI Analysis of Customer Diverse Data in financial services can unlock a wealth of customer insights, personalize their approach, and achieve significant improvements in customer engagement, loyalty, and overall performance.
- Risk Mitigation: Enhanced ability to predict defaults, reducing financial risks and potential losses.
- Strategic Decision Making: Empower decision-makers with actionable insights for better risk management and loan approval processes.
- Operational Efficiency: Streamline risk assessment processes, saving time and resources while increasing accuracy.
- Adaptive Risk Models: Stay ahead of economic changes with models that adapt to new data, ensuring relevancy and accuracy.
- Customer Insight: Gain deeper understanding of customer financial behavior for improved service and targeted offers.
Benefits
An innovative platform for Multi-Dimensional AI Analysis of Customer Diverse Data offers a comprehensive solution for financial institutions seeking to gain a deeper understanding of their customers. It empowers leveraging the power of AI efficiently, make data-driven decisions, and achieve significant improvements in customer performance analysis, behavior prediction and overall ROI.
- Comprehensive Risk View: Aggregated and analyzed data offers a detailed risk profile for each customer.
- Optimized Loan Portfolio for banks and financial institutions: Insights into risk levels across the portfolio allow for informed lending decisions.
- Informed Strategic Planning: Predictive analytics inform future risk trends, aiding strategic planning and resource allocation.
- Time Savings: Automation of data collection and analysis processes reduces manual effort and increases productivity.
- Customer Retention: Proactive risk management strategies can enhance customer satisfaction and loyalty by offering tailored financial advice and solutions.
- Saved cost: On fulfilling orders to non-paying customers in the sector of online sales and services.