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Become a true Data Driven Organization

By April 20, 2020 No Comments

In Commercial Banking it is increasingly important that business processes are digital, data driven and can leverage AI. In the current times of unexpected change we see this magnified. IG&H data scientists observe that organizations who already transformed their processes now truly benefit.

Commercial banks are confronted with a sudden wave of SME client requests, changed risk drivers and changes in risk profiles. Banks want to help and need to figure out what (temporary) policy changes would be meaningful for clients. And also, what the impact of specific changes would be on the bank’s business.

Those who have already transformed their processes are now able to handle this situation much faster and more confidently. Their business processes are already more efficient and more consistent. And in the current time of crisis they also prove to be much more Scalable, Transparent, and Adaptable and they offer more options for looking forward in a smart way.

Scalable
Digital, data driven business processes with a high rate of straight-through-processing and where decisions are made (partly) by AI decision models, require much less human effort. Therefore, they can deal more easily with peaks in workload, especially in times when human capacity may be limited.

This benefit can only fully materialize when there are no bottlenecks in other parts with a crucial dependency. This stresses the fact that individual point solutions are not the way to go. The effective way is a transformation to become a true Data Driven Organization in People, Process, Data and Technology.

Transparent
Monitoring the impact of the current situation on the client experience, on process performance metrics and on KPIs is much more accurate and near real-time in a data driven process. This facilitates communication and coordination throughout the organization and allows management to take more effective actions.

For example: Dashboards can quickly be shared to observe what is really happening. Such as which teams have the highest workload increase. Or where clients’ payment behavior is most impacted.  Analytics can be used to signal early warning indicators such as trends and significant deviations.

Adaptable
AI decision models and business rules can be configured easily to effectuate policy changes like (temporary) higher risk thresholds, lowering the weight of specific risk drivers, higher or lower maximum values, etcetera.

For example: It can be easier to change a few parameters in a risk review decision model, than it is to communicate such changes to whole departments of specialists and coach them to quickly and consistently execute these.

Smart forward looking
Finally, AI decision models can be used to ‘test out’ different scenarios and evaluate very fast the likely effects on individual loans and on portfolio level.

For example: Changing the values of specific risk variables along the lines of different scenarios and observing the predicted effects, is being used to zoom in on those clients who likely require first attention.

AI models can be a very powerful tool to provide insight in likely future outcomes. A data scientist and business specialist who understand how the underlying machine learning works and on what data it was trained can provide a range of quick scan insights within a very short turnaround time.

IG&H’s data scientists and banking consultants continue to work with clients (especially now) to transform commercial banking organizations to remain competitive and benefit from being a true Data Driven Organization.

Would you like to talk about what you can do while your processes are not yet as digital and data driven as you would like? How you can best take the first step? Or how you can leverage your first progress and truly turn the corner to transform into a Data Driven organization? We are ready to help you explore and make data work! Just drop me a note!

Mando Rotman
Manager Data Science IG&H
E: mando.rotman@igh.com

Mando Rotman

Author Mando Rotman

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