Phát triển mô hình rủi ro – Thế hệ tiếp theo

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Trong thế giới dịch vụ tài chính nơi quản lý rủi ro là điều tối quan trọng, tất cả chúng ta đều thấy trí tuệ nhân tạo và học máy đang thay đổi nhanh chóng cục diện. Trên thực tế, gần đây

khảo sát của Ngân hàng Anh và Cơ quan Quản lý Tài chính (FCA) tiết lộ rằng
72% các công ty tài chính ở Vương quốc Anh đã sử dụng hoặc phát triển các ứng dụng AI/MLvà xu hướng này đang tăng tốc với tốc độ đáng kinh ngạc, với
số lượng ứng dụng ML trung bình được dự đoán sẽ tăng vọt 3.5 lần trong ba năm tới. This growth is not surprising – AI/ML models hold the promise of unlocking insights from vast amounts of data, enabling financial organisations
to make smarter, more informed decisions, and enhance their risk management strategies.

The survey’s findings are consistent with observations that I’ve made through my work with UK financial services institutions. Although, I have found that the progression towards AI/ML methodologies is more advanced within Fintech and Challenger Banks that,
unlike the High Street Banks, may not suffer from actual limitations due to legacy systems or perceived limitations relating to their IRB status.

Fintechs and Challenger Banks have typically recruited tech-savvy data scientists with deep understandings of the array of alternative advanced techniques that are available. Meanwhile, major banks still hold a significant advantage in terms of experience
and data. They have decades of experience in building credit models, have established model development standards, and have a thorough understanding of the underlying data.

Câu hỏi bây giờ là liệu các nguyên tắc làm nền tảng cho sự phát triển của các mô hình truyền thống có còn phù hợp hoàn toàn với thế hệ mô hình mới được hỗ trợ bởi AI vốn được rút ra về mặt toán học theo một cách hoàn toàn khác hay không.

Phát triển mô hình: VS AI/ML truyền thống

Traditional scorecard development has long adhered to meticulous sample design, ensuring that the applications during the sample window are both stable and reflective of proposals most recently received. It is typical for Population Stability Indices or Characteristics
Stability Indices to be calculated, and for a detailed investigation of any patterns that extend beyond reasonable expectations of seasonal variation. This approach hinges on the notion of a bespoke development sample tailored to the specific population it
serves. The composition or segmental mix and its specificity is seen as a key factor in the suitability of the model development sample.

Interestingly, we often see that AI/ML models exhibit a significant degree of cross-learning. This is where models display stronger performance when the training sample is extended to include additional observations that might not traditionally be considered
directly relevant. For example, we see superior performance from models trained on an expanded sample window versus equivalent models optimised on a period that simply aligns to the independent test sample. This is unlikely to happen using linear models!

Similar findings can be seen when adjacent segments or groups are added to the training samples. Indeed, AI/ML models thrive when developed upon large and diverse data sets. These phenomena will have implications for sample design and choice of exclusions within
model developments of the future, potentially rewriting conventional wisdom.

Similarly, many credit scorecard developments have incorporated segmentation, whereby a model is built for each of a number of sub-populations (eg. Thin File / Thick File, Clean / Dirty). The benefit of this approach is that, by building multiple models, some
non-linearity can be captured. Of course, the choice of segmentation is not always obvious and is unlikely to be optimal, however some performance uplifts are achieved. Given that AI/ML models are built because of their ability to capture non-linearity, there
is limited need for segmented models here, unless there are fundamental differences in data structure. Therefore, AI/ML models are more complex, fewer of them should be required.

Another area of focus within traditional scorecard development is the process of moving from fine-to-coarse classing. Hereby the modeller seeks to effectively divide continuous data into several ordinal groups so that the underlying bad rate shows a logical
progression and is based on sufficient volume to give a reliable result. Advanced methodologies within AI/ML models eliminate the need for fine-to-coarse classing as the grouping is achieved by the underlying methodology, generating smooth response profiles
rather than the step-changes seen as scorecard attribute boundaries are crossed. Furthermore, many training routines now include the option to add constraints to ensure features have a logical impact on the model predictions.

As the wave of AI/ML model development surges in the coming years, a fusion of deep knowledge of underlying credit data and advanced methodology is key. While new challenges arise in this new generation of models, such as unintended bias and explainability,
historical concerns will become less relevant.