Enhancing Credit Risk Scoring using AI as well as Machine Learning
With the continuous increase in the number of services and products that mobile users can avail, including payment via mobile (M-payments) as well as e-wallets telecom operators, also known as telcos are transforming beyond their previous functions as communication service providers, with increasing capabilities for banking and facilitating payment transactions using mobile phones, and expanding their offerings of services to include FinTech solutions like digital microfinance, or even working with insurance companies to provide insurance products on their platforms.
Artificial Intelligence-based Credit Scoring Systems employ advanced algorithms and techniques for data analysis which allow greater precision and effectiveness in credit assessment and scoring procedures, as well as innovative risk control strategies which reduce the effect of delinquencies and aiding in more informed decision-making.
In the past, credit assessment has relied heavily on the analysis of credit reports as well as credit scores that were derived from previous financial transactions. Although these traditional methods for assessing credit score were crucial however, they are prone to not focusing on crucial financial transactions and resulting in inaccurate assessments and the potential for reaching the unbanked and underbanked population with no credit background.
Unlocking Creditworthiness using AI Enhancing risk assessment in Telecom
The rapid growth of mobile phone use has resulted in huge amounts of data telecoms can benefit from. By 2023, there were nearly seven billion mobile networks worldwide according to projections that suggest the number will exceed 7.7 billion by 2028.
Telecom data offers an accurate analysis of financial behaviour that provides insights into transactions history, social media activity calls, patterns of call use of data, SMS and text messages as well as top-up frequencies, mobile preferences, and so on providing a variety of benefits for improving credit score of customers:
- The Behavioral Insights: Telco data gives detailed insight into the behavior of consumers through indicators like calls patterns, data usage and payment histories. This information allows to gain a deeper analysis of a person’s financial behavior and stability. For example, regular and timely payment of bills could be an indicator of financial accountability.
- Constextual information: Telcos gather contextual data such as geolocation, the patterns of usage on networks. This data can be used to evaluate economic and lifestyle conditions. For instance frequent international calls may indicate a higher income or professional standing.
- Complete coverage: Telco data is accessible to a wide range of people, including people living in areas that aren’t served and may not have financial records as traditional. This extensive coverage of data gives a more inclusive method of scoring credit risk.
How AI and Machine Learning Models Optimize Credit Risk Scoring in Telecom?
Effective management of data is vital for telcos to use their data to provide precise credit risk scoring which is where data of high-quality guarantees accuracy consistent, completeness, and accuracy and provides a solid basis for risk assessment.
Artificial Intelligence (AI) and machine learning are a new layer of technology that is capable of analyzing huge quantities of data at a staggering speed. These tools are versatile and can speed up automation, speed up, and improve the analysis of large amounts of data. They can be adapted and constantly learn from the latest data, revealing subtle patterns and connections to creditworthiness. This allows for a more comprehensive credit evaluation, revealing areas that traditional analysis usually overlooks.
Analysis of Broader Datasets
AI models are able to integrate and analyzing a variety of telco information, including both unstructured and structured data sources. Credit scoring systems of the past might struggle with the sheer quantity and variety of data. AI models can analyse information from a variety of telecom sources, including customer phone records, payment histories and even social media interactions to determine risk factors that are emerging and give complete risk assessments through the automated management of data. For instance, AI can detect changes in spending habits or other unusual patterns in communications that could suggest financial trouble. This technology allows more detailed and accurate assessment of customer profiles and can result in more accurate and accurate credit risk evaluations.
Recalibration
The traditional credit score models are typically rigid, and require manual updates every time the new parameters for data are added which can make score-making and cause it to slow down. However, AI algorithms are highly adaptive and can self-update. They constantly improve their models by automatically incorporating new data gathered from sources such as payments histories, call records and usage patterns. Self-learning capabilities allow AI models to remove outdated methods of managing data processes and to incorporate new features in real-time, making sure they are effective even as new risk factors and information become apparent.
Explainability
One of the main benefits of the latest AI models lies in their capacity to give a clear explanation of score scoring for credit risk. In contrast to traditional models, which are obscure and difficult to understand, AI systems often include elements that allow transparency into the decision-making process. For example, AI can highlight the particular data points or variables that contributed to a specific credit score, allowing you to clarify the reasons for why a particular score was determined. This transparency does not only build confidence with clients and regulatory authorities, but can also allow for better understanding and improvement of the models, assuring that they are impartial and fair.
Precise Prediction
AI models rely on both real-time and historical information from telcos, like patterns of usage by customers and their payments, to improve their predictive abilities. AI’s powerful analytical capabilities enable it to process huge amounts of data, identify intricate relationships between variables and gain greater insight into a person’s financial behavior and results in more accurate credit predictions as well as a better understanding of risk by studying different sources of data, including non-structured data from telco transactions.
Neural Technologies’ advanced machine learning (ML) and artificial intelligence (AI) solution, ActivML, provides a solid framework to improve scores for credit risk. ActivML makes use of sophisticated analysis and Explainable artificial intelligence (XAI) to provide accurate transparent, automated, and clear information management for credit scores. With its MLOps capabilities, this software allows for rapid development, training, and the deployment in credit risk model even for those with no technical knowledge.
The most important aspects that make up the ActivML solution are:
- Business-Enabled Automated Model Building, Training and Deployment
- Self-learning Structured Analytical Profiling
- Unconstrained Anomaly Detection
- Predictive Classification
- Explainable AI Analytics (XAI)
- Continuously Learning from Live Data
The use of AI in credit risk management can bring a variety of benefits, including the recognition of risky areas in real-time, fraud detection, real-time monitoring and automated processes, enhanced accuracy of predictions and a reduction in the time required for credit management.
