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Whitebox Machine Learning
Discover how whitebox ML enhances lending with transparency, compliance, and trust.
Discover how whitebox ML enhances lending with transparency, compliance, and trust.
Artificial intelligence (AI) technologies, particularly machine learning (ML), are transforming the modern lending industry.
The use of AI in credit risk assessment provides numerous benefits for the financial sector. It enables fast decision-making on loan applications, reduces the need for human resources, and ensures accurate creditworthiness evaluation.
However, implementing this technology comes with challenges. Many scoring platforms utilize machine learning models that operate on a "blackbox" principle.
Such systems fail to meet key regulatory requirements, which state that ML models used in the lending industry must be transparent and explainable.
The alternative to blackbox AI is whitebox machine learning models. What are they, and what are their advantages?
Let's delve deeper.
Blackbox AI involves the use of ML models with algorithms that are incomprehensible to the end user.
In other words, the lender receives the outcome of an application review but cannot explain why the decision was made that way.
In the case of credit scoring, the process looks like this:
The system analyzes the data received about the borrower and provides an answer regarding the feasibility of granting a loan. However, neither the lender nor the applicant can explain the conclusions reached.
Whitebox machine learning models use a completely different approach. They not only provide a ready-made decision on loan issuance but also explain the reasoning behind it.
The process can be visually outlined as follows:
The use of whitebox machine learning in credit scoring offers several advantages:
1. Transparency and explainability. Users understand and can easily interpret the decision made regarding a loan application. This fosters customer trust, as they can independently verify the objectivity of the results.
2. Easy of model improvement. Given the clear algorithms of such models, modifications can be easily made. For example, errors or biases can be eliminated, increasing the accuracy of assessments.
3. Compliance with regulatory requirements. Unlike blackbox models, whitebox AI credit scoring fully meets legal requirements for the explainability of ML models. For instance, regulations such as GDPR in the European Union address these standards.
4. Cost efficiency. Whitebox machine learning models require less computational power, making them accessible even to small companies with limited resources.
Along with the obvious benefits for creditors, whitebox ML models have several limitations that are also worth considering.
These models:
Are less productive. Such models indeed operate more slowly, as their algorithms must not only provide the user with a result but also demonstrate the reasoning behind the decision.
Require a certain degree of human involvement. Their functioning is overseen by humans, who are needed to set the necessary parameters and adjust the rules.
It should be noted that the above-mentioned limitations are insignificant compared to the benefits creditors can expect when using whitebox machine learning in credit scoring.
Whitebox ML helps optimize several aspects of lending institutions' operations.
Modern scoring systems use artificial intelligence to evaluate alternative data on potential borrowers. This includes their social media activity, online payments, and other relevant information.
Whitebox ML models can track historical patterns in such data and accurately predict the likelihood of default based on these insights.
With such models, lenders have access to real-time identity verification. This process involves:
Any discrepancies found may be seen by the creditor as a potential sign of fraud.
The scoring system RiskSeal employs whitebox machine learning models in the credit scoring process.
This means our clients not only get the applicant’s creditworthiness result but also access to the formula explaining how it was determined.
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