The credit score is a numeric expression measuring people’s creditworthiness. These scores and other data like the expected approval rates, profit, churn, and losses, are then used to support decision making. Credit scoring means applying a statistical model to assign a risk score to a credit application. It is a form of Artificial Intelligence, that assesses the likelihood of a customer defaulting on a credit obligation, becoming delinquent or insolvent. A modelling technique used for implementing credit scoring is the Credit Scorecard model. It is used to provide direct input for risk-based pricing.
The Credit Scorecard Demo.
The scorecard created is based on attributes of each customer like their Age, Number of Time 30-59 Days Past Due Not Worse, Revolving Utilization of Unsecured Lines, Number of Times 90 Days Late and similar. The data from a bank or financial firm consisting these customer details are uploaded. The machine learning model learns on the “Weight of Evidence value” and the “Information Value” (based on WoE), calculated from the banking data. The machine learning model coefficients are then used to scale the scorecard- making the scorecard conform to a particular range of scores. The final credit score, a simple sum of individual core values of each feature in the data, can be taken from the scorecard which can be downloaded into comma-separated value (CSV) file.
The benefits of AI-based Credit scoring: .
The Predictive Process
A self-learning AI analyses data, learns from it, improves itself and provides predictions at a high scale and detail depth.
Ease of Re-calibration
AI-based scoring is dynamic – can update itself. With the availability of alternative data, the model can be retrained to solve and define greater challenges.
Reduce time to credit decisions
In the event of specific data of a customer is lost or invalid, AI can be leveraged to extract meaningful insights and put the data to complete use. This also reduces the necessity for physical investigation.
Meet regulatory requirements
AI applications help reduce data bias and create a transparent approach to enable credit scoring. Hence, diligence for granting loans or refinancing existing transactions can be ensured thus meeting regulatory requirements.