Advancing Credit Risk Scoring with AI and Machine Learning
With the continued expansion of products and services available to mobile users, such as mobile payments (M-payments) and e-wallets, telecom operators, or telcos, are evolving beyond their traditional roles as communication service providers with growing functionalities to do banking activities and making payment transactions from mobile phones and expanding their services offerings to Include FinTech solution such as digital microfinance services and even partnering with insurance providers to offer insurance products through their platforms.
AI-based credit scoring systems utilize advanced algorithms and data analytics techniques enabling enhanced precision and efficiency in credit assessments, scoring processes, proactive risk management strategies, minimizing the impact of delinquencies, and facilitating more informed decision-making.
Historically, credit assessment heavily relied on the analysis of credit reports and credit scores derived from past financial transactions. While these traditional credit scoring methods have been pivotal, they exhibit limitations by overlooking crucial financial activities, resulting in incomplete assessments and opportunities for reaching the underbanked and unbanked populations lacking traditional credit history.
Unlock Creditworthiness Through Telecom Data
The global explosion in mobile phone usage has created a wealth of data that telcos can utilize. As of 2023, there are nearly seven billion smartphone mobile network subscriptions worldwide, with projections indicating this number will surpass 7.7 billion by 2028.
Telecom data provides a nuanced view of financial behavior, offering insights into transaction histories, social media activities, call patterns, SMS and data usage, top-up frequencies, handset preferences, and more, offering several key benefits to enhance customer credit scoring:
- Behavioral Insights: Telco data provides detailed insights into consumer behavior through metrics such as call patterns, data usage, and payment histories. This data allows for a nuanced understanding of an individual's financial habits and stability. For instance, frequent and timely bill payments can be indicative of financial responsibility.
- Contextual Information: Telcos gather contextual data, including geolocation and network usage patterns. This information can help assess lifestyle and economic conditions. For example, frequent international calls might suggest a higher income bracket or professional status.
- Comprehensive Coverage: Telco data is accessible for a broad range of individuals, including those in underserved regions who might not have traditional financial records. This comprehensive data coverage provides a more inclusive approach to credit risk scoring.
How Are Machine Learning and AI Models Used in Credit Risk Scoring for Telco?
Effective data management is crucial for telcos to leverage their data for accurate credit risk scoring, where high-quality data ensures accuracy, completeness, and consistency, providing a reliable basis for risk assessments.
Artificial Intelligence (AI) and machine learning introduce a sophisticated layer of technology capable of analyzing vast amounts of data at unprecedented speeds. These versatile tools can accelerate, automate, and enhance the analysis of big data. They can adapt and continuously learn from new data, uncovering hidden patterns and connections related to creditworthiness. This enables a more thorough credit assessment, revealing aspects that traditional analysis often overlooks.
Analysis of Broader Datasets
AI models excel in incorporating and analyzing a wide range of telco data, including both structured and unstructured sources. Traditional credit scoring systems may struggle with the volume and diversity of data. AI models can analyze data from various telco sources, such as customer call records, payment histories, and even social media interactions, to identify emerging risk factors and provide comprehensive risk assessments using automated data management. For example, AI can detect changes in spending behavior or unusual patterns in communication that might indicate financial distress. This capability allows for a more nuanced and thorough evaluation of customer profiles, leading to more accurate and actionable credit risk assessments.
Recalibration
Traditional credit scoring models are often rigid, requiring manual updates when new data parameters are introduced, which can complicate the scoring process and slow it down. In contrast, AI algorithms are highly dynamic and capable of self-updating. They continuously refine their models by automatically integrating new data from telco sources, such as call records, payment histories, and usage patterns. This self-learning capability allows AI models to discard outdated approaches from data management processes and incorporate improvements in real-time, ensuring they remain effective as new information and risk factors emerge.
Explainability
One of the key advantages of modern AI models is their ability to offer explainability in credit risk scoring. Unlike traditional models, which can be opaque and difficult to interpret, AI systems often include features that provide transparency into their decision-making processes. For instance, AI can highlight the specific data points or factors that influenced a particular credit score, helping to explain why a score was assigned. This transparency not only builds trust with customers and regulatory bodies but also allows for better understanding and refinement of the models, ensuring that they remain fair and unbiased.
Precise Prediction
AI models leverage both historical and real-time telco data, such as customer usage patterns and payment behaviors, to enhance their predictive capabilities. AI's advanced analytical power enables it to process vast amounts of data, uncover complex relationships between variables, and gain deeper insights into a customer's financial behavior, resulting in more accurate credit predictions and a better understanding of risks by analyzing diverse data sources, including unstructured data from telco interactions.
Neural Technologies’ advanced machine learning (ML) and artificial intelligence (AI) solution, ActivML, provides a robust framework for enhancing credit risk scoring. ActivML utilizes sophisticated analytics and Explainable AI (XAI) to offer precise, transparent, and automated data management for credit assessments. With its MLOps capability, this solution enables seamless development, training, and deployment of credit risk models, even for users without specialized technical expertise.
Key features of the ActivML solution include:
- Business-Enabled Automated Model Building, Training and Deployment
- Self-learning Structured Analytical Profiling
- Unconstrained Anomaly Detection
- Predictive Classification
- Explainable AI Analytics (XAI)
- Continuous Learning from Live Data
The implementation of AI in credit risk management offers numerous benefits, including the identification of potential risks, fraud detection, real-time monitoring, automated processes, improved accuracy in predictions, and reduced credit management time.
Discover more about AI-powered credit risk management with Neural Technologies today.
