Modern banking has continuously evolved– diversifying into commercial, retail, and investment streams– through its history that has lasted nearly a millennium. However, the ever-changing global business landscape has ushered this sector into a new era with the emergence of development banks. These banks are established to provide financial and professional advice for the economic development of a country or a region. Hence, for a development bank, the request does not close once the fund or the advisory services have been disbursed. The economic development here comprises indicators or checkpoints drafted by the client on the basis of which a loan or advisory service is availed.
What makes these banks special
Though there are only a limited number of development banks, the value they add is enormous. Compared to their counterparts, these banks have more volatile processes and significantly larger funding since their end customer is a country itself. In the current scenario, there are a number of challenges that development banks face in their daily operations. Some of these are:
- Fund disbursement for ongoing projects is sourced from various donors. It needs to be calculated where the implemented projects have a one-time hindrance, and where locations need to be mapped to the entire project or to a specific donor.
- An economical path needs to be figured out since project activities should align with various economic indicators specified in the sustainable development goals (SDG).
- Manual grouping of various activities necessitates a series of collaborations involving various business units, finance, divisions, locations, economic indicators, and other areas to thrust out the data as per the user requirement.
Overcoming the hurdles
Getting accurate information on live projects involves a series of complex mechanisms. It requires respective divisions to constantly collaborate and cooperate using a common portal, which again involves huge IT cost.
Here are some suggestions on how the said challenges can be overcome with minimum resources and infrastructure:
- The first step involves capsuling of areas or divisions along with their respective economic objectives. This calls for aligning the taxonomy in which multiple areas of practices are classified and fused using keywords or phrases. Whenever a set of keywords occurs in a project or activity, the respective area or division should be used. Once the area of practice is sorted out, other sets of taxonomies should be merged. It involves calibration of keywords for economic goals, activities related to climate change, and SDG. This will reduce the effort to half as there will be classified and segregated processes that can be easily applied appropriately by just looking at the taxonomy.
- After capsuling, financing and geo localities should be captured based on the information available. This will lead to one-click accessibility to the data of the entire project or transaction.
- Artificial intelligence (AI) should be used for data extraction to ensure all keywords can be extracted pertaining to the set goals and taxonomy. The extracted keywords then act as an interface between the AI tool and the output by creating a well-designed flow chart. This will help in generating end reports with greater accuracy while engaging people.
- A set of people, who can easily contour the above steps, should be empowered with the right resources to make one-click reports available on the cloud for stakeholders, especially the senior management of banks, to access anytime, anywhere. This may lead to on demand startup information of projects which is available even before any activity being implemented.
Each development bank has its own processes based on its business strategy and requirements. Changing the core operating models is neither viable nor fruitful. However, collecting, grouping, and organizing units, funds, goals, and locations through a centralized portal will resolve many complications. These may even lead to round-the-clock support for operations instead of hiring contractors for data extraction. The processed information can be stored using a cloud solution to offer anytime and anywhere access to stakeholders. Meanwhile, decision-based analysis process can be automated using AI, cognitive thinking, or robotic process automation (RPA) tools.
What do you think? Are there other ways in which next-gen technologies can help development banks achieve their business goals effectively?