Banks are playing catch-up in the big-data game

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.

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