Banking As A Service (BaaS) is basically providing underline technology and infrastructure of various banking functions as Current/Saving Accounts (CASA), Loan, Mortgage, Payments, etc in form of API-driven secure platform. These APIs can be consumed by other Banks, Fintechs or retail/e-commerce technology platforms. Banks are able to monetize their technology and ecosystem players can try out new business models with faster speed and low investment.

Moreover, BAAS providers collate ecosystem customer data to apply analytics for understanding customer’s expectation to find new customer segments and launch dedicated products

Some examples of BAAS:

Uber – Uber money with digital cards, wallet and credit for contracted driver and couriers

Amazon – Pay later for zero interest credit and EMI option

Simple, a Neo Bank powered by BAAS platform of CBW bank

Solaris Bank‘s BAAS platform is used by Alipay bank to expand POS in Europe

Snapdeal and Freecharge with Yes bank used to give ‘instant refunds’ service

Bigger picture: 

Moving from traditional banking to BAAS is a transition from traditional banking with a bouquet of services to provide choices to customers.

This transition is driven by the massive shift of the economy into the digital realm forcing radical changes in customers and in financial providers themselves.

Moreover, COVID has only accelerated this evolution by forcing banking and financial services to take the digital route of operation. Ecosystem players are kept on coming with new ways of solving customer problems whereas customers are also open to experimenting with these digital routes.
Hence, BAAS is a paradigm shift with two central pillars at its core: data and a relatively old technology that has become mainstream: APIs.

The critical success factor lies with third parties embedding BAAS in their customer journeys in such a seamless way that the two offerings become one. Despite the fact that the BaaS model has been around for some time, it has only started attracting mainstream interest and wider adoption lately with the introduction of open banking. It’s easy to see why open banking has been such a catalyst for BaaS adoption: only because it has democratized the main element based on which the BaaS concept is based – the use of APIs.

BAAS Future: 

 

As per World Retail Banking – 2021 Report, a survey of 122 global banks suggested, 38% of banks already have an in-house BAAS platform and  28% of banks are using third-party bank platforms for some specific function. Looking at this rapidly growing BAAS adoption, banks will also need to focus on avoiding commoditization. Brand building and its visibility are also a key challenge as end customers will never know which banking api is at the backend. Customer stickiness will remain with the service provider. Hence, creating an open banking api ecosystem is not the end goal but the start.

 

 

BAAS

How to find the right balance? 

To acknowledge the right business opportunity and finding out the best technology solution is the secret sauce of BAAS success. There are various framework can be accessed for the same. At very high level, we can think of:

  • Finding the target market segments by mapping the opportunity with the customer need. Segmentation can be done at different levels such as:
    • Demographic Segmentation
    • Psychographic Segmentation
    • Geographic Segmentation
    • Behavioral Segmentation
  • Statistical analysis of the target segment in terms of customer behavior, product/service usage patterns, macro-economic factors etc will be useful.
  • After data collation, next step is to narrow down the segments and quantify the target opportunity. In my opinion at least 3 opportunity bands to be considered – Optimistic, Pessimistic, and Realistic
  • The next step is to do functional analysis – understanding what we have at present, what customers like the most, and where do we have limited exposure. Deep dive analysis of API strategy if any prevailed within the current ecosystem also needs to be checked.
  • Correlation 1 – Mapping of opportunity market segment with the As-Is state functional data points to identify the gap
  • Result of this correlation needs to be assessed with the required technology landscape – architecture, workflows, and system specifications
  • As-is Technical analysis is the next step to identify all product licenses we have. The latest architecture and technologies are in use. The scalability, agility and security, and resiliency of the infrastructure need to be assessed.
  • Correlation 2 – Mapping of ideal technology landscape with the As-Is state to identify the gaps
  • Post this gap analysis – strategy formulation is required to fill the gaps in time bound fashion to maximize the benefit

In practice, target opportunity itself will be changed post this analysis, and this process needs to be created multiple times to find out the right business case. Ultimately profit is the king and cost-benefit analysis of technology change is the most important step in this process. Interestingly, this situation also provides an opportunity to Fintechs and System integrators to come up with solutions, frameworks, and tools to solve some of the technical problems that legacy system faces. A good example is – CBS system for core banking which is difficult to make api friendly.

Various innovations are going on and new players are emerging to take advantage. Hence, regulations are also needed to be tightened because customer data is prone to security concerns in this multi-party ecosystem. Governments with rules as PSD2 are supporting open banking constructs whereas data breaches keep on increasing and asking for more global regulations like GDPR. It will be interesting to see where the future of Fintech and Banking will shape up.