Explainable Artificial Intelligence (XAI)

Explainability is one of the fundamental elements in Artificial Intelligence models to establish trust.

Explainable Artificial Intelligence (XAI)

Explainability is one of the fundamental elements in Artificial Intelligence models to establish trust. The term transparency, as stated in the European Commission Ethics Guidelines for Artificial Intelligence, is closely linked to the principles of traceability and explainability, encompassing transparency of elements relevant to an artificial intelligence system: data, system, and business model.

How Can We Create Transparency in Our Artificial Intelligence Models?

Traceability and explainability can be achieved through 'Explainable Artificial Intelligence (XAI)', which is the main concept behind transparency. We can consider two main approaches:

• Explaining black-box models
• Developing interpretable white-box models with high accuracy

Explainable Artificial Intelligence (XAI) enables you to understand what features influence the outputs, which data features the model uses for learning, and whether the model is biased or misleading.

KoçDigital XAI Suite

Through KoçDigital XAI Suite models:

• Model outcomes can be examined,
• Customer behaviors and satisfaction can be tracked,
• The impact of market changes on the model can be assessed,
• The potential outcome for a similar customer can be generated by the model,
• Counterfactual reasoning is possible (e.g., What should be the customer attributes for the customer propensity model to yield a high outcome?),
• Model neutrality can be monitored and managed.

KoçDigital Risk Management Models

There is a high demand for Explainable Artificial Intelligence in banking due to various reasons:

• Regulatory constraints in the risk domain,
• The need for control over decision processes stemming from closed and unknown model contents,
• The possibility of biased model creation during training processes,
• The ability to scrutinize and examine model outcomes,
• Customer behavior inference capabilities.

In the finance sector, the requirement for regulatory models to be explainable is evaluated even under Basel II for IRB (Internal Ratings-Based) frameworks.

Given the necessity for PD (Probability of Default) models to be explainable, trackable, and manageable in risk management, relatively accurate models with lower accuracy are often employed.

With KoçDigital XAI Suite:

• Compliance with risk regulations is ensured
• High accuracy is maintained,
• Models are manageable,
• Easily explainable models are made available.

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