Supernova Award Category
Data to Decisions
The Problem
In any service industry, waiting time represents a critical KPI not only for customer experience but also in terms of operational efficiency and cost. With respect to commercial banking services – specifically in under banked regions – the most commonly used channel is the branch, as it remains the first point of contact with the customers. Additionally, some customers (especially older generations) still prefer face-to-face interaction with the bank personnel and accordingly, bank branches can reliably be used as an accurate indicator of the customer satisfaction level. Although CIB had been making significant efforts in enhancing its customers’ branch experience in terms of waiting time and service level, managing branch waiting time remained an ongoing challenge. The Branch Management team approached the Data Science team to find a practical solution for managing, tracking and improving the branches performance in terms of the tellers average waiting time (AWT).
The Solution
Data from different source systems, touch points and social data was refined and analyzed in real time. Advanced analytics and exhaustive data exploration was undertaken. The solution consisted of 2 phases. Phase 1 resulted in a branch performance management/monitoring tool. Phase 2 was a branch process optimization engine to re-engineer and provide customized solutions by branch. The engine evaluates different process enhancement scenarios based on their effect on various KPIs, mainly the AWT and recommends the optimum scenario for execution. Moreover, changes in the simulation settings can be easily applied to answer "What happens if…?" questions and avoids the cost of trial and error. The most important factor for choosing simulation was to enable the end users – Branch Management – to test various scenarios for process optimization. This transformed the branches decision-making process from a trial and error approach to a data-driven method.
The results
As the leading private sector bank in Egy[t, CIB developed a fully in-house Branch Simulator specifically designed to imitate the branch process to evaluate different scenarios of proposed improvement solutions and their impact on a real system, if applied. To mimic the exact branch processes, the Branch Simulator takes several inputs including the customers’ observed behavior of arrival rates, segment distributions and service times, in addition to CIB branches queuing system logic. This was constructed using real branch distributions reflecting the natural variations of the actual human interactions within the branch. The simulator is quite flexible as it allows for testing a wide range of process improvement scenarios. Subsequently, the engine evaluates different process enhancement scenarios based on their effect on various KPIs, mainly the tellers average waiting time.
Finally, the engine recommends the optimum scenario for execution. The solution introduces an innovative scientific flare to regular branch managerial tasks by placing the users at the heart of the design, enhancing their decision-making experience and rendering it as a continuous learning tool. Moreover, it allows for a transformation of the branches decision making process from a costly trial-and-error approach to a data-driven method. This is the first tool of its kind to tackle the conventional challenging “waiting time” problem within the banking industry in MENA region.
Metrics
The Branch Management and Branch Operations teams provided the Data Science team with several possible scenarios for testing purposes. Some of the suggested scenarios were: offloading foreign currency transactions to the ATM, and operational enhancement in terms of cash transactions serving time. The preliminary testing results highlighted the scenarios with the highest expected reduction in average waiting time (AWT). The top first and the top second scenarios showed an expected reduction in AWT of around 70% and 50% respectively.
Keeping in mind the CIB large network of around 200 branches (CIB has the second largest network in Egypt), this translates into huge cost savings for the bank. Depending on the targeted optimization strategy of different processes within the branches or the type of offloading, the expected average monthly cost saving is around 0.29 Million EGP. If we conservatively consider around approximately 30% of the branches per year to have effective cost savings, there could be an annual cost saving of around 211.3 Million EGP.
CIB has achieved huge ROI applying its analytics strategy. In 2016 and 2017 alone, CIB achieved more than 250% ROI.
The Technology
As a first of its kind in the region, the department hosts the full data production line, choosing Teradata as its sole partner in the Enterprise Information Management journey. Driven from CIB’s philosophy of committing to create great customer experience, the department adopted Unified Data Architecture, capitalizing on Teradata technologies.
Disruptive Factor
Improving branch operations for a bank with a proven record of success and a strong legacy, was a challenging yet revolutionary one, as it elevates the bank’s operational performance from good to great. Tackling the problem with the right approach and right tool, within an extremely collaborative business structure, made all the difference.
Utilizing optimization techniques to empower data-driven decision-making was a perfect demonstration of how Operational Research is “The Science of Better”. This solution introduced an innovative scientific flare to the regular branch managerial tasks, put users at the heart of the design, enhanced decision-making experience and rendered it as a continuous learning tool.
Through joint efforts of CIB branch management, staff and the Data Science team, a harmonious process of learning and improvement was accomplished, resulting in amazing success. Through their cooperation, flexibility and dedication to perfection, the branches management ensure every new insight or recommendation revealed by data is immediately translated into action. This accomplishment set new standards for a successful innovation story of an analytics-driven business.
Moreover, it transformed the branches decision making process from a costly trial-and-error approach to a data-driven method. This is the first tool of its kind to tackle the challenging “waiting time” problem within the banking industry in the MENA region.
Shining Moment
The CIB Advanced Analytics and Data Management team won the Euromoney as well as the Global Finance Best Innovation Awards.CIB has become the first Middle Eastern company to be analyzed in a case study conducted by the Leadership Institute of the London Business School (LBS). CIB was selected in recognition of its data-driven, human-centric approach to leading transformation in the face of macroeconomic challenges. Please also find the CIB London Business School video.