Banks are playing catch-up in the big-data game
With advances in data analytics, machine-learning models and the creation of vast amounts of data, there has been an explosion in model development and deployment across every industry — financial services is no exception. Banks and other financial organizations are using machine learning to automate call centers, make personalized financial recommendations via mobile apps and identify financial fraud.
Unfortunately, banks are at a disadvantage relative to other industries in three interrelated areas: the hiring of talent, the efficient development and deployment of advanced analytics and compliance with regulatory expectations. Without talent, it is difficult to create the appropriate analytics to better serve bank customers. Banks are competing for talent with Netflix, Google, Facebook and Uber, among others. Aside from the cachet of working for a big tech firm, top talent will want to work in the fast-paced environment of a modern company rather than at a legacy institution. Banks also suffer from an aging technology infrastructure. Technology is improving at an increasing rate, and most banks’ infrastructure is ill-equipped for rapid implementation.
Banks are also at a disadvantage due to the length of time it takes to develop and deploy models. Banks are familiar with the semi-annual cadence of regulatory stress tests. The processes around models including governance, controls, validation and promoting models to production were created to align with this 6-month schedule. Developing, testing and validating models often takes months at banks. These model activities typically occur sequentially rather than in parallel, further increasing the time to market. In contrast, Netflix software to production thousands of times a day.
One of the reasons that banks are slow to develop and deploy models is due to heightened regulatory expectations around model development and independent model review. In some cases, large regional banks’ primary introduction to modeling was through the regulatory CCAR and DFAST stress test exercises. Soon after the CCAR and DFAST models were introduced, the Federal Reserve prescribed additional requirements for independent model validation. Some banks scrambled to comply with the new modeling requirements, resulting in suboptimal workflows around model development and validation.
The good news is there’s a solution to these disadvantages: Banks need to create a framework or ecosystem for efficient and compliant model development and validation. Such an ecosystem is necessary to compete in the new world of big data and analytics while satisfying the needs of customers, developers and regulators. Such an ecosystem must allow for the efficient deployment of high-value analytics while still retaining the governance and controls expected of a financial institution. To achieve this goal, the ecosystem must deliver on a number of fronts.
The model development environment is vital for attracting and retaining quantitative talent. The environment should support familiar and popular development platforms such as Jupyter notebooks or markdown. The environment should support a wide variety of programming languages so that developers can use their favorite libraries. The environment should integrate with enterprise source control management systems such as source control management for code and model versioning.
