AI can be applied in financial services in many ways. Here are a couple of use cases illustrating the unique benefits AI brings to credit scoring. Recalibration
Traditional models are cumbersome because the addition of new parameters slows them down and complicates the scoring process. AI algorithms are much more dynamic in self-updates, improving over time by discarding non-efficient approaches and adding improvements without human interference. Precise Prediction
Traditional credit scoring algorithms work linearly by analyzing historical data to produce estimates of future creditworthiness. Self-learning AI systems, in contrast, use historical and current data to improve their forecasting capacity. The advanced technical power of AI allows them to analyze big data, drawing connections between fragmented variables, and giving a much more in-depth insight into the borrower's profile. These features contribute to the steadily rising predictive potential of AI algorithms and better analysis of unstructured data. Cost-Effectiveness
Though many users think of AI solutions as too costly to implement, in reality, AI models' application is more cost-efficient in the long run. Most providers of scorecard-based credit scoring solutions charge the users on a per-user principle. At the same time, AI models represent an entire customizable and continuously learning system able to meet all your credit scoring and customer profiling needs. For instance, Datrics
currently offers flexible ML-based credit scoring systems able to provide accurate eligibility forecasting and intelligent borrower ranking to minimize the number of potentially "bad" loans. Analysis of Broader Datasets
Today, the recognition of new risk drivers is essential in sensitive, responsive credit risk scoring. Traditional systems can't evaluate these risks adequately, with AI serving the innovative needs better. For example, AI systems can analyze unstructured data from the customers' social media to detect risks and alarm the financial institution (e.g., posts exhibiting the customer's car damage or a fire in their house). A vital criterion of business sustainability for business lenders is what AI can capture, while humans and scorecard-based systems can't. All these aspects can be included in a self-learning AI solution to receive comprehensive and realistic evaluations of customer profiles, leading to smarter customer differentiation and credit risk calculation.