Building Smart and Resilient Operations
Find how DBS is leveraging a data-driven operating model to sustain business growth
By Alok Kumar, Data Science and Analytics Lead, Group IBG Operations
In the mid-90s, Bill Gates said,” Banking is necessary, banks are not”. At DBS, we foresaw the changing landscape in banking and pivoted our efforts to transform ourselves into an “invisible” bank in our customer journey. Operations, as the key engine which powers banking experiences, will play a significant role in making banking seamless and effortless for our customers.
The Changing Role of Operations in Financial Services
Over the past few decades, banks have been shifting from traditionally delivering banking products to offering more customised and collaborative services. The role of operations is also ever evolving, propelled by the twin effects of the negative public opinion towards banks in the aftermath of the financial crisis of 2008, as well as technology’s long boom teed off by the recession. Being data-driven is a key step in enabling operations to be smart and resilient, so it can transform from being merely reactive to customer requests, to continuously engaging in the customer servicing journey. Of late, our self-enabled front-to-back digitalisation has given us the capabilities to provide more proactive and predictive services to be the future of smart operations.
Building Smart Operations
Our smart operations are built using three building blocks:
1. Mega-trends driving smart operations
2. Data architecture and data capability
3. Weaving analytics into operations
From the table above, you can quickly see how the various technological trends all point towards opportunities for us to reimagine bank operations by putting data front and center. DBS has come a long way in its digitalisation efforts and now, with the capability to perform predictive analysis, we are proud of providing our customers with hyper-personalised servicing (N=1).
From a data architecture perspective, we have our operational workflow for data instrumentation, along with an ops control tower to narrow the gap between data and business values that leverages on state-of-the-art technologies in data science to maximise value from our treasure of data.
We are able to achieve our goal of smart operations through the continuous weaving of analytics into our daily operations. One successful example would be our analytics-based Operations Resource Management to manage, forecast and plan resourcing needs. With this, our operations managers can now get insights on a day-to-day basis from a deep learning powered forecasting engine to reallocate resources proactively on upcoming transaction spikes.
Driving Operations with Data
Our bank operations are currently supported by three data pillars:
1. Forge data-driven culture
2. Speed to insights with ops control tower
3. Create advanced analytics capabilities
Forging a Data-Driven Culture
Our transformation to become a data-driven bank requires a cultural change within the organization, especially in the operations field where many “legacy” processes are still in use. We are now adopting a design-for-data methodology for new initiatives, and have a task force comprising data scientists, data analysts, and data translators who collaboratively solve business problems and deliver data products. In this initiated task force, we have data novices (business users) who are picking up the data thinking process and incorporating data in solving business problems; data translators who help minimise the gap between business and data force; and our data scientists and data analysts who work collaboratively in building and exploring ML models.
Speed to Insights with Operations Control Tower
The missing link between data and business values is an area of focus which has been addressed by our second data pillar. With our Ops Control Tower, we can have a complete view of key performance metrics that allow us to develop faster and better actionable insights.
Creating Advanced Analytics Capabilities
Finally, we also leverage data science to provide hyper-personalised solutions and build a future-ready workforce. We have successfully launched personalised customer servicing needs with customer science which predicts/priorities customer needs and distributes these insights via digital channels. Another example is the resource management tool mentioned above for managing/planning resourcing needs in a data-driven manner.
Conclusion
With these smart and resilient operating models, we are moving towards a more data-driven operating model. This will go a long way in keeping our promise to make DBS banking a truly invisible experience, enabling our customers to Live more, Bank less.
(This article is also translated in Traditional Chinese language.
