Banking AI: Credit Scoring and Practical Explainable AI

26/08/2026

Are you worried that AI models will decide loan approvals without explanation?

The answer is no, and this is a critical failure point if you cannot control it. In banking AI projects I have participated in, forcing models to "justify" the reasons for rejecting or approving a loan is more important than accuracy itself.

Many operations directors I meet are still stuck in old thinking: just buy AI software and you're done. The harsh reality is that technology is merely a tool; how you set the rules of the game is the deciding factor. Let's look at the real-world crossroads you must navigate when deploying credit scoring solutions.

Choosing Between Assessment Speed and Absolute Transparency

This is the first and most difficult trade-off. You can choose a complex Deep Learning model to automate file assessment at twice the speed of manual processing. It handles thousands of variables in seconds, from transaction history to consumer behavior across different platforms.

However, the price you pay is the "black box." When a customer asks why they were rejected, your staff cannot answer other than "the algorithm said so." In the context of increasingly strict regulations in Vietnam, as well as neighboring markets like Thailand or the Philippines, a lack of explanation poses a significant legal risk.

Conversely, if you choose more traditional models like decision trees or logistic regression, you lose some ability to predict complex correlations. However, every decision can be mapped into a clear logical diagram. This is the core of Explainable AI (XAI). I have seen many banks choose a middle path: using powerful AI for preliminary screening but mandating an accompanying explanation layer for senior staff approval.

Historical Data vs. Real-Time Data from Agentic AI

The second crossroads lies in the data source used to feed the credit scoring engine. The old way relies on historical data: salary, assets, and repayment history from the last three years. This method is safe but passive.

The new strategy is to use Agentic AI to collect and analyze real-time data. These AI agents can scan unstructured data streams, even this month's spending trends. But be careful. This requires massive system synchronization and can infringe on privacy if not designed carefully.

I have worked with companies like Masan and TTN Group in retail and supply chain, where real-time data is vital. Applying this mindset to banking AI, you will see that small business borrowers can be evaluated based on actual cash flow rather than just tax reports. However, the trade-off here is infrastructure cost and the complexity of data risk management. You cannot use an old model to process new data, and vice versa.

Ethical Risks and Bias in Algorithms

This is an issue few speak about directly, yet it is always present. When you delegate credit scoring to AI, you inadvertently transmit biases present in the training data. If past data tends to reject certain areas or specific customer groups, the AI will learn and amplify that bias.

Detecting this risk cannot rely solely on technical reports. You need a periodic review process to eliminate bias-inducing variables such as gender, religion, or sensitive geographic regions. In markets like Mexico or Vietnam, where the economic structure is diverse and includes many informal sectors, this is even more challenging.

The solution is not to eliminate AI, but to establish "ethical guardrails" directly within the source code. I recommend that banks establish an independent AI ethics board, including non-technical members, to monitor system decisions. This is the only way to ensure fairness and avoid discrimination scandals.

Deployment Strategy: Build In-House or Partner with AIVISION

Many operations directors ask: is our in-house IT team capable of building such a complex credit scoring system? The answer is usually no, or it will be very costly and slow. Building from scratch requires a team of data scientists, data engineers, and legal compliance experts.

This is where partners like AIVISION become meaningful. We do not just provide software; we accompany you in designing processes, fine-tuning models, and ensuring Explainable AI. We have deployed solutions with many large enterprises across various industries, from manufacturing to distribution, helping them transition from manual thinking to intelligent automation.

However, remember that partnership does not mean outsourcing entirely. You must still understand the operating logic to make decisions when incidents occur. Do not let yourself become someone who only knows how to press the "process" button without understanding what is happening inside.

Frequently Asked Questions

Can AI completely replace credit assessment officers?

No. Current AI is only a decision-support tool, especially in initial screening and risk analysis. Humans are still needed to handle exceptions, evaluate specific contexts, and bear final legal responsibility.

Does Explainable AI reduce model accuracy?

In some cases, there may be a slight trade-off in accuracy when prioritizing transparency. However, with modern techniques, this difference is usually negligible compared to the benefits of compliance and customer trust.

How high is the cost of deploying AI for credit scoring?

Costs depend on the scale and complexity of the data. A basic system can be deployed at a reasonable cost, but achieving high automation and strong explainability requires significant investment in infrastructure and high-quality personnel.

AIVISION helps enterprises turn AI into working systems. Explore our enterprise AI solutions, read more on the AIVISION blog, or talk to our team about your own use case.