The cloud is here, and here to stay. By 2025, it’s estimated that over 100 zettabytes of data will be stored in the cloud. And 95% of all data center traffic will pass through cloud data centers. Microsoft, Amazon, Google, IBM and others have all positioned their cloud offerings as a central component of their business strategy. It’s not surprising that cloud adoption is accelerating: there are many benefits to computing in the cloud. Like most industries, many of these advantages resonate with the needs of the credit modelling industry. Together, we’ll briefly explore why the cloud is ideal for credit modelling.

Credit modelling at an unprecedented scale

Perhaps the most striking attribute of the the cloud, and why it’s so enticing for credit modellers, is the promise of inexhaustible scale. As the role of credit and risk management increases in importance and prominence at financial institutions and businesses, so too does the importance of scaling these operations. There are more clients, more markets, more data, and more business needs than ever before. On the cloud, this problem becomes addressable. Enormous datasets? No problem. Massive computational requirements? Fine. Data needs to flow from millions of sources into one centralized location? Done. At the snap of your fingers, you can satisfy all of your scaling needs. There’s no free lunch of course, you’ll need to develop expertise to be mindful the associated costs of such power (or use a product like Praexia, which manages this for you), but the problem of “I need more RAM” or “I need a faster laptop” is largely a thing of the past when you switch to the cloud.

Cloud is everywhere, so you can be anywhere

The other big virtue of the cloud is that it’s accessible via the internet. This means access to the cloud is (almost) universally available. No matter your physical location, you will probably have access to your data and work environment. This makes the cloud fantastic for remote work. And it means data can be piped in from anywhere, and  used to create new credit models. Business systems are no longer unexpectedly isolated, or exist inharmoniously in silos. The struggle to move data into a centralized “common storage” space (what’s nowadays referred to as a data lake or data warehouse depending on the nature of the data) has been transformed into a mundane task. If you care about security (and you should!) there are other advantages: if all your data storage, processing and credit modelling operates in the cloud, then client information remains secure in that environment (rather than distributed across the credit team’s laptops). Centralization of resources, but diminished decentralized risk. It’s a win-win.

Better credit modelling: agility and agency

Centralizing on the cloud provides additional benefits, many of which are ideally suited to credit modelling. For example, let’s say you’ve created a credit model using a cloud-native credit modelling toolkit. Typically, you intend for the resulting credit model to be deployed into production, so that it can be utilized by to service  downstream business stakeholders. Pre-cloud,  a common process is to dutifully hand the credit model to a specialized “translation” team, who faithfully translate the model’s logic into SAS, SQL, or any other “production” runtime. The translated model is then handed to yet another team, whose job it is to deploy the translated model into a production environment. If any of the teams come face-to-face with a bug during this process, this process must be analyzed, until the source of the problem is identified and fixed. And this is just the technical aspects of credit modelling and deployment! We haven’t even factored in governance. In a world where the economic environment is constantly and rapidly evolving, what’s needed is agility. The traditional way of work is no longer acceptable.

Thankfully, with the cloud, this problem can be remedied.

When using a cloud-based credit modelling tool such as Praexia, which operates inside of a machine learning workspace such as Azure Machine Learning, credit models can be deployed immediately into production once they are created. Build, test, and deploy in minutes, not weeks. You no longer need a model translation team, especially if you use Python. Need to make a modification or update? Just change the code, re-train, re-deploy. No more lobbing a model over the fence. Everything is under the control of the people closest to the business problem: the credit modellers. In the near-future, it will seem perplexing to have done it any other way.

Governance and gateways

And governance? Speed is fine, sure, but only if it can be kept under control. Can the cloud help with this aspect of credit modelling too?

Typically, before a credit model enters production, a credit committee or credit manager approves it. Before the cloud, you would have to send a copy of the credit model, alongside supporting material. Unsurprisingly, this leads to slow back-and-forths, and often (tragically) over email, where every stakeholder is out of sync to various degrees. But the cloud centralizes everything. What if you could build these approval gates directly into the credit modelling solution? By integrating with a tool like Azure Pipelines, you can insert approval gates for each step of the process. And once all approvals have been given from the necessary authorities, the model can automatically deploy into production. Magic — not just speed, but control too. And the best part? It’s all captured and logged by the system — leaving an easy to understand audit trail.

Cloud + Credit modelling = ❤️?

Let’s quickly recap why is the cloud such a perfect fit for credit modelling. The cloud gives you unprecedented scale, and brings centralization to the operational process of credit modelling. The result is agency and agility for credit modellers, allowing them to operate and iterate at faster speeds than were ever possible before. Coupled with careful governance integrations, you have an extremely enticing recipe for success. Speed and control, in a single package. If you’d like to explore this new paradigm,  many of these features and benefits are already present in our credit modelling tool Praexia, which is already available as a cloud-native solution on the Azure marketplace.

 

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