Modeling takes many forms – from the physical building of a model (such as a small-scale railway layout) to the digital modeling of virtual worlds in Minecraft. What drives all modelers, however, is always the same: modeling reality – the past, present, or a potential future event or scenario – as closely as possible. It’s what drives them to constantly refine their skills, develop new techniques, and master new technologies. Even when traditional modeling methods are often the best.
Quantitative risk and finance modeling is no different. Data scientists use a mix of old and new, emerging technologies and algorithms to produce increasingly accurate representations of the economy, their customers and more – past, present and future. As these models are increasingly based on more and better data generated from diverse sources, the more accurately they will be able to assist humans in making faster, better decisions that help them understand and navigate the real world.
Accurate modeling is particularly critical in the world of banking
Today, modeling teams play a key role in helping their banks along the journey to digital transformation on two fronts: by enabling step-level improvements in efficiency through automation and by fully exploiting data for insights and competitive advantage. As banks increase their dependence on models to thrive, they will also need faster, more efficient ways of developing new and powerful models – including risk models that use artificial intelligence (AI) and machine learning (ML). These powerful models hold immense transformational potential to improve the accuracy and efficiency of risk analytics and help banks proactively detect stress signals.
The push to quickly adopt new analytical methods (including AI and ML) when delivering models and gain tangible benefits places tremendous pressure on modeling and management teams to execute and deliver business value. For example, while a growing number of banks are exploring the use of machine learning in production for credit decisioning, many more are using these methods as benchmark comparisons or for auxiliary analytics. Enabling deployment of new innovative models in a timely manner to meet the individual business needs is the principal benefit of having a true, end-to-end risk modeling platform – because models that are stuck in a long development cycle can’t deliver business value, no matter how innovative they are.
Innovative risk models give businesses tremendous power and opportunity
However, as explored in the SAS white paper “Future of Risk Modeling: Taking the risk out of next-generation risk modeling − from data to decision,” with this power comes great responsibility – to your business, as well as to regulators and customers. Using and scaling next-generation risk models – especially those powered by AI and ML – is a complex endeavor full of both incredible opportunity and considerable risk. Regulators around the world, still reeling from the last global financial crisis, know just how big the risks of dependence on complex models can be. So as your bank deploys complex and interconnected risk models, anticipate intensifying regulation worldwide and expectations for model explainability and accuracy, data protection, ethical conduct, treatment of customers and more.
To model accurately, modelers need to expand their toolsets and skills
To model effectively, risk modelers will need a diversity of interfaces so they can work according to their own preferences. These interfaces should be open (so they can flexibly use preferred vendor and open source analytics) and make the development of models easy and intuitive (so banks can harness their existing talent – people who already know their firm’s data and domain).
In addition, they will need faster, better ways of operationalizing models into decision flows. There won’t be time to wait months for development, weeks for final approvals, and time to recode model logic to align with where it’s going to be used. Models may need to be created and executed in one to two days across the enterprise – and in the case of ML models, be governed more closely than any traditional model has ever been governed before.
Dealing with change
Finally, regulations for risk models will keep changing – and to stay compliant, you must be able to identify which models deployed across your enterprise are affected by changes, adapt or rebuild them, and then deploy your risk models within a tight time frame. This will require highly efficient model development and deployment capabilities and exceptional model governance (so you can isolate models affected by regulatory changes and where they have been deployed across the enterprise).To model effectively, risk modelers will need a diversity of interfaces so they can work according to their own preferences. These interfaces should be open. Click To Tweet
When these types of next-gen capabilities run on a single platform in a smooth and integrated way, risk modelers can quickly onboard new data, develop an innovative model based on SAS data mining and machine learning or open source algorithms, and validate its performance using backtesting and benchmarking. Validation is essential to ensuring outcomes are accurate and everything is functioning in a way that the business, customers and regulators require. In addition, risk modelers can easily extend the platform to support the deployment of complex model logic within decision flows and realize immediate business value. The deployment of a new model can happen in one to two days, if necessary.
The quest for greater model accuracy continues
Modeling in financial services is evolving fast – and it brings tremendous opportunity to deliver significant business benefits, including improved productivity and risk-informed decisions that boost the bottom line. But just as in train and Minecraft modeling, every risk modeler’s trash is full of failed attempts to deliver the right model. There will always be room for improvement, which will require new skills, tools, and capabilities. Want to learn more? Download the free white paper: “Future of Risk Modeling: Taking the risk out of next-generation risk modelling − from data to decision”