LEVERAGING AI FOR A FASTER, MORE ACCURATE, AND TRANSPARENT APPROACH TO ASSESS CREDITWORTHINESS
axeSCORING: a machine learning-based scoring framework
Financial institutions need to assess borrowers’ creditworthiness in order to make safe and smart credit decisions. Scoring approaches play a key role in this process, as they can provide accurate information that feeds lending processes.
For decades, banks have used static model scorecards where each customer is given a score based on various metrics: demographics (age, gender, marital status, occupation, income level), social indicators, (lifestyle, interests), and financial behavior (repayments & loan delinquencies, credit card balance). The classic approach of creating a biographical snapshot combined with a credit bureau history remains widespread.
Now, machine learning is unlocking lending potential and transforming the way financial institutions deliver credit. axeScoring is a comprehensive framework allowing bankers to explore and mine an extensive range of data sources to build innovative ML-driven scoring models that are fully integrated with the Axe Credit Portal (ACP). Applying an innovative approach, these models are built within the axeScoring framework and continuously monitored & adjusted over time, accounting for new credit and risk data feeds.
TOWARDS A MORE TARGETED SCORING MODEL
axeScoring is a risk modeling and profiling tool that can be used for both existing clients with historical data (behavioral and dynamic scoring) as well as for ‘new to bank’ customers without an extensive track record (scoring at origination). axeScoring utilizes AI algorithms to produce models addressing both types of profiles. These cutting-edge credit models offer greater insight into an applicant’s ability to pay their debt and result in the extension of credit to deserving applicants who, using previous methods, would have been denied a loan.
Accurate portfolio segmentation is a key factor in the performance of scoring models. axeScoring offers advanced techniques for customer segmentation such as multiple correspondence analysis and unsupervised clustering that leverages time series-engineered features to ensure that each segment has its specific scoring model. With this very granular scorecardization, loan officers can prioritize high-value customers with a higher propensity to convert.
Why choosing axeScoring framework?
- An ML-based scoring, able to build and monitor appropriate models for each customer profile.
- A dynamic self-tuned model continuously improving its scoring accuracy and decision-making quality.
- A natively integrated model to Axe Credit Portal offering a seamless end-to-end lending automation experience.
axeScoring: a robust ML-based Scoring Framework
MORE ACCURATE & FASTER DECISIONS WHILE ENSURING OPERATIONAL EFFICIENCY
Generally used methods of prioritization, such as rule-based systems, are no longer sufficient in an age of data-powered modeling. axeScoring is a data-driven framework relying on advanced ML-based analytical techniques to manage a wide range of factors that inform creditworthiness. All credit stakeholders and experts involved in the credit process rely on this framework to select the most relevant features and data to develop precise models.
ML-based scoring models are innovation catalyzers in the lending world. With further automation of the approval processes, financial institutions can strike the right balance between risk appetite, cost reduction, policy compliance, and customer experience. Automatic approval and rejections from axeScoring drastically reduce workload allowing bankers to perform tasks of higher added value. In working with our clients, we have seen more than an 80% reduction of operations workload, allowing employees to focus on credit applications that require further risk analysis and human assessment. This enables a significant shift in the roles of risk officers and managers, with them able to dedicate their time to analyzing credit portfolios, model performance monitoring & back-testing, and ensuring data quality, instead of manually processing the cases of individual applicants. This allows risk policies to be continuously fine-tuned and model parameters to be tweaked, with the ultimate goal of fully automated credit approvals.
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