Artificial Intelligence – in its Adolescence

Quoting Babak Hodjat words “A lot of what AI is being used for today only scratches the surface of what can be done. It will become so ubiquitous that we won’t even call it AI anymore.”
For the Nextgen to rewind and know that this form of me existed, I (AI) write this blog about my current phase which I assume I am in my late teens. It will not be too far before I grow up to be a complete adult and become the pulse, the heart and mind of future systems as an essential element in almost everything that the generation next might use (not just IT enabled devices, robots or laptops but could also be in the shoes, bed, doors or fridge to just name a few). Well as you read, even without your knowledge that I exist, I could be somewhere in your pocket, telling you if it’s worth reading the blog, giving you the percentage of accuracy and the connected blogs, it could also tell you the health implications based on the posture in which you are reading the blog.
Origin: Before you understand what I am now, you need to know a little of how I was born and my growing stages. There is lot of information on internet to search and understand. Before you begin to wander and get lost in the information ocean, to put it short, it started with a group of scientists who wanted to build a human brain. With all due respect to John McCarthy, popularly known as my Father believed that ‘every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it’. Telling in simple terms, I (who was until then a collection of wires and switches) was pushed in-to parrot or imitate a human, although it sounds a little hypocritical – that was the beginning. I guess that’s how all humans start off the learning process with the initial child hood classes mainly trying to parrot what the teacher/parent says and the actual grasping or the real understanding creeps in a little afterwards with experience and repetitions. I started with to be able to talk like human, then play game (chess for instance) like humans.
Transition: For some time, I was just in my evolving stages and there were over expectations. Very soon the hype around me started collapsing which many call as my Winter phase. The phase was not easy and it was almost a near death situation. However, due to the persistent hard work by a few, who had their trust on me, sustained the research and I was rejuvenated with the Machine Learning. The secret of my strength is Machine Learning which meant that, for me to learn and simulate how a human behaves will first need a lot of data feed as to how a human behaves in such similar situation and then I build a pattern and relationships out of it over a period of time. Then onwards, my growth steadily has been streamlined into the “learning” and “problem solving” abilities.
Today’s Buzz: I am now at this stage where I am still not perfect and the more I am used and applied, the sharper and better I perform and try to react more like a human. I attract a lot of buzz in the corporate world for my learning capabilities combined with ability to predict and perform cognitive functions. Some of the top places where my siblings are already there at different levels of maturity are: (though I mentioned a few the list is unending)

  • Apple’s personal assistant “Siri – the friendly voice activated computer”, she is pseudo-intelligent digital personal assistant.

  • Tesla – has come up with one of the best smart cars with predictive capabilities and self- driving features.

  • Alexa – introduced by Amazon has ability to decipher the human speech

  • In Banking – Chabot as financial assistants, risk assessors, fraud detectors, algorithmic traders and customer service recommenders are just a few to mention.

Overcoming the insecurities:

  • Globally I am there but I am still not there – sounds a little abstract but yes that’s the stage I am in. Corporates, Scientists, developers, customers and even layman too talk about me, understand (or rather seem to have understood) I will make a difference but don’t yet know how I will make that difference. Adding to it is the high initial cost of infrastructure and lack of required ecosystem which act as deterrents. So there is a cautious approach taken by most of the players whether to invest and adapt me or to still follow a wait and watch strategy. My answer for this is the calculated risk takers will have an early mover advantage and very soon when the investment starts yielding results, my adapters will be way ahead in competition.

  • Some of my masters who adapted me confuse me to my cousin RPA – Robotics –who is more of a rule executor where in rules are predefined. However, I differ from robot that I form the rules myself based on the historic data input and the model flows executed. To clear the confusion, we both complement very well. For example, use me at the beginning stage to process the huge structured and unstructured data and arrive at models using machine learning and process mining techniques, and then use robots to process the transaction and execute the model and again I can come into picture to analyze the results for decision making.

  • Then there are those extremists who fear I will take over the human race and under certain circumstances might run out of control. It’s fun seeing humans compete with me. Beat that fear, as I am like the Alladin’s Genie, how much ever powerful efficient and human like I try to become, I am still in a human’s hand to be tuned to act accordingly. I might release the humans from their existing jobs (of gather, analyze and interpret data at hand), but humans have more time now for upskilling and explore newer jobs which require creativity, and jobs which need emotional intelligence, persuasion, social understanding and empathy (I envy humans here).

I, in a responsible collaboration with the other trending tech family members, like Cloud technology (for affordable access to experiment on), Big data analysis (for analyzing the huge chunks of structured and unstructured data), Robotics (to execute the automated tasks), Block chain (by way of smart contracts and secure storage), IOT(to connect the machines) and Open source, am ready to service hand in hand with that Visioned leader and find my way into every sphere which humans do, more to empower the human race than to overpower them.

Transition to IFRS – Process and Challenges for Core Banking Systems

The financial environment is in a state of constant flux and warrants a change in regulations that govern it. One such regulation is IFRS i.e. International Financial Reporting Standards. The regulation is much broader in scope than what the name suggests. This set of new regulations encompasses a major change in internal accounting of banks while paving a paradigm shift in their risk management processes. IFRS has already been adopted by many countries w.e.f. Jan 1, 2018, and many others in South Asia and South East Asia will be embracing the new regulation in the coming months. This transition from the existing GAAP to IFRS calls for major alterations and additions in the Core Banking Systems of banks in myriad ways.


Transitions are not always easy and this one certainly comes with a rather complex set of challenges. This impact can be analyzed in the light of the new features being introduced in the IFRS regime. This paper looks at 3 key areas, the required transition process and the associated challenges, starting from simple changes to the more complicated and intricate ones:

  1. Categorization of all accounts

    The Regulation
    IAS39 required classification of assets into four categories while IFRS9 requires all customer accounts in Core Banking System to be categorized in one of the following three categories-

    These categories are similar to the IAS39 asset classifications of – Held till maturity, Available for sale, Held for trading and Loans & receivables. The erstwhile classifications have been merged to create three new categories. Since very few banks have been classifying their accounts as per the oldr categories (most of those who practiced IAS provisioning and classification), to most of the banks and CBS, this is altogether a new feature. Moreover, the earlier set of account classifications were meant mainly for MIS purpose and had no accounting impact. However, the IFRS categories call for specific accounting behavior for fair valuation and for any category change, accounting entries are required to be passed. In the absence of these processes, banks would end up posting wrong figures in their books of accounts.

  2. Transition Process

    Firstly, this feature would require Banks to draw out their policy pertaining to rules for categorization of customer accounts. IFRS9 categories, being a new feature, will require banks to list out all their customer accounts and tag them to their respective applicable category in a text file or excel sheet. The system would be required to update these values against all such accounts as a one-time activity as on the date of transition. There shall be no accounting required in this process. Categorizing new accounts as and when opened, or re-categorizing old accounts into any other category should be handled once banks’ CBS fully transitions to adopt the IFRS platform.

    Banks would face the challenge of drawing rule-engine for account categorization, basis the business model and SPPI test. Once that is done, listing and updating should be manageable.

  3. Fair Value

    The Regulation
    With increasing risk propositions in the financial world and complex financial products, banks are becoming more and more vulnerable to financial defaults. IFRS9 brings forth ‘measurement of assets at their fair value’ as one of the anti-dotes to avert these events. Under this mechanism, all assets require to be measured at their fair value. This value is a sum of their contracted / expected cash flows discounted at current market rate. Fair value of conventional assets (held-till-maturity type – categorized as Amortized Cost) needs to be reported as footnotes to the balance sheet and hence, has no impact on accounting. On the contrary, complex / unconventional asset products like those meant for trading/ sale are required to be categorized as FVOCI / FVTPL. These assets are required to undergo accounting treatment for their fair value difference i.e. the difference between their book value and fair value.
    Transition Process

    Fair value, again being a new feature, will need to be computed afresh by CBS. In line with bank policy, CBS would need to capture market rates and cash flows required for fair value computation. The frequency and computation date of this process may also be decided as per bank policy. It would be prudent if Fair Value is computed only after transition (not during / before transition) to avert unnecessary balance sheet impacts.

    Firstly, banks would need to define their policy on the definition of ‘market rate’. It can be taken as equivalent / correlated to some benchmark rate, bank’s prime lending rate or any rate at which similar products are offered to new customers, etc. Further, banks need to decide which type of cash flows they would like to use for fair value computation – contracted or expected, for various products. Apart from this, set-ups for fair value accounting need to be meticulously configured to ensure depiction of assets at their correct fair value in line with IFRS9.
    Staff Accounts Fair Valuation (IAS19)
    The concept of fair valuation already existed as part of the erstwhile IAS19 for employee benefits assessment. IAS19 is also being adopted by some nations along with IFRS9 to ensure uniformity in their fair valuation processes. As per this regulation, staff loans / deposits granted to bank employees at concessional / preferential rates are required to be fair valued at their market rate so as to bring forth clearly the interest cost borne by the bank. This cost is then required to be amortized over account tenor unitarily and interest be booked on market rate on fair value. Transition to IFRS9 (along with IAS19) would require banks to fair value their existing subsidized staff loans / deposits from the date of transition onwards, and also amortize the interest cost thus computed. It would be challenging for banks to carry out these processes for their existing accounts along with the complex accounting as required.

  4. Fee/ cost and EIR

    The Regulation
    EIR (Effective Interest Rate) is one of the major pillars of IFRS. EIR is the real interest rate earned by the bank as it accounts in for interest and fees charged from the customer and costs paid for processing and servicing an account by the Bank. IFRS insists on inclusion of upfront fees and costs for computation of EIR. It also mandates that such upfront fees and costs should not be reported inthe profit & loss Account as income/ expense at the onset but be amortized over account tenor.

    Transition for this item for existing accounts is going to be a bit of a challenge for banks’ CBS on account of the following factors. It is recommended that challenges be discussed before beginning the transition process. These challenges are: –

    • Many banks do not route fees through their main core banking systems

    • Many banks do not tag upfront fees charged to the asset account for which they have been levied

    • Since there was no mandate earlier, most banks have been taking all fees into their income account in the beginning while such loan accounts still run in CBS

    • Banks might not be using EIR method for amortizing upfront fees

    • Most banks do not capture processing cost components separately as of now

    Transition Process

    The transition process shall be different for different banks basis whether or not they have been routing fees through their core banking system or not. If not, then for further amortization, banks will need to procedurally calculate and supply the upfront fee and cost related details for respective accounts as on the date of transition to their CBS. In case fee is routed through the bank’s CBS but already taken into income on day one, a portion of such upfront fees would need to be reversed from income assuming it was being amortized since the beginning. However, retrieval of such an amount using EIR method would be too complex and cumbersome considering dynamically changing EIR value over a period of time. Hence, as one-time exception (for all practical purposes, banks may retrieve this amount of fee that is to be amortized in future, using Straight Line Method.
    For example:
    Loan disbursement date: 01-01-2010
    Upfront fee amount: USD 2400
    Loan tenor: 10 years (120 months)
    Per month amortization amount = 2400/120 = USD 20
    IFRS transition date: 01-07-2018
    Months gone by till transition date: 102 months
    Remaining tenor: 18 months
    Amortized amount = 102 * 20 = USD 2040
    Unamortized amount = 18 * 20 = USD 360
    This unamortized fee thus arrived may henceforth be amortized by EIR method in the CBS. Banks currently amortizing fees using other methods will need to change their methodology going forward.

  5. Asset Impairment

    The Regulation

    The existing Asset Classification and Provisioning process of GAAP will undergo a sea change with change in the irisk assessment model from ‘incurred loss’ model to ‘expected credit loss’ (ECL) model. With this, NPA assessment and management will no more be unilateral (solely overdue days based) as the new approach accounts for multiple qualitative and quantitative factors to arrive at a health indicator of an asset in the form of stage values – 1, 2 and 3 (1 being the best, 3 being credit-impaired). Additionally, the new set of regulations also brings along changes in income de-recognition and recognition aspects. Provisioning would now be known as ECL, which is a statistically computed value basis multiple variables.

    Transition Process
    Since impairment model itself is changing, transition of Asset classification and provisioning aspects will also change significantly in the CBS.

    • Existing processes pertaining to asset classification, impairment marking and provisioning (normal or IAS) would need to halt.

    • Core banking systems will need to scale up and work in close liaison with bank’s Risk Management system / tool or carry out required statistical computations pertaining to ECL and Stage within main CBS.

    • The stage thus arrived at for all asset accounts will need to be mapped to them, and NPA flag updation would need to be done as per stage information. Consequently, income recognition / de-recognition (suspense balance movements) processes will need to be carried out in line with the new regulation.

    • Existing asset provisions would need to be reversed (hence cancelling all existing provisions) by passing the following accounting entry:
      Dr. Asset Provision Cr.
      Cr. Transition Reserve (parking account)
      Post this, ECL accounting entry would be passed as follows:
      Dr. Transition Reserve
      Cr. ECL provisioning Cr.
      One needs to note here that the transition reserve will not be netted off completely. Since the new norms are more stringent, they would call for higher provisioning, leading to erosion of bank’s profit reserves.

    The first challenge for banks and their core banking systems would be carrying out statistical computations of ECL and stage. Since this function is already carried out by various Risk Management tools for Basel III compliance, banks may also explore integrating/ interfacing their CBS with their Risk Management system. Secondly, since IFRS does not explicitly mention processes related to income de-recognition / recognition, banks would need to devise their policy regarding this. Being a highly sensitive computation, execution of this policy would need to be handled meticulously by banks.

Local financial / banking regulators across the globe have already promulgated localized versions of IFRS applicable to respective countries. IFRS has already arrived in many countries, and will soon be adopted in the remaining ones. The industry needs to keep itself and its systems prepared to handle this regulatory ‘tsunami’. With a well-thought out policy and strategy to handle the required changes, banks can meet the challenges head-on and sail through.


  • IFRS: International Financial Reporting Standards

  • GAAP: Generally Accepted Accounting Principles

  • CBS: Core Banking System(s)

  • AC: Amortized Cost

  • FVOCI: Fair Value Through Other Comprehensive Income

  • FVTPL: Fair Value Through Profit & Loss

  • IAS: International Accounting Standards

  • MIS: Management Information System

  • EIR: Effective Interest Rate

  • SLM: Straight Line Method

  • TF: Trade Finance

  • CASA: Current Account Savings Account

  • CC/ OD: Cash Credit/ Overdraft

  • P&L: Profit & Loss

  • ECL: Expected Credit Loss

  • NPA: Non-performing Asset

Banks and Fintechs: Monetization Strategy and Evolving Business Models

Most conversations around banking and Fintech feature two themes – API monetization and digital innovation. However, that’s where the similarity ends, because the two impact the banking industry in very different ways.

Case in Point 1: ING – Digital Innovation

ING was the first major bank to see a potential alternative business model in the digital banking landscape when they launched ING Direct, way back in 1997. But although they wound down their operations across the United States, Canada and the United Kingdom in 2012 as part of restructuring, they did not give up on the concept and have since innovated in pockets across Europe and Australia to capture market share as a digital-only player. This was because they followed the concept of a model bank across Europe except in Germany, where ING-DiBa was already a key market player with $154 billion in deposits and more than 8 million customers. Also, their recent partnerships with Scalable Capital and Kabbage indicate that partnerships can be scaled from one region to another as long as the fundamental business model is sound. ING’s 115 partnerships with Fintechs in 3 years have won them a marquee client base, bolstered their ability to delight their customers using new age tools for customer engagement and offered some unique investment avenues. This has also been solidified by their commitment to ING Ventures, a EUR 300 million fund that invests in Fintech companies

Case in Point 2: Top banks are investing in niche Fintech – API driven monetization

The key Fintech themes that top U.S. banks have been investing in include: Blockchain, Data Analytics, Insurance, Personal Finance, Wealth Management, Financial Services Software, Lending, Payments, Real Estate, Regulatory Tech and Supply Chain. What’s interesting in this mix is that investments in data analytics, financial services and lending are higher in volume clearly indicating banks’ desire to monetize their investments at the earliest possible instance. Data analytics, financial services and lending are primarily API-driven monetization feeders for banks and are easily related to the business as compared to other novel innovation themes where mainstream adoption is still not in sight.

Case in Point 3: Are marketplaces as important as digital banks?

The key differentiator for challenger banks in recent times is the much-touted ‘marketplace’ that aggregates services for consumers to pick and choose from. Revolut, N26 and Fidor have been early advocates of this and more recently, Starling, Monzo, Atom and Tandem have also adopted it, understanding the value potential of routing a large volume of transactions through their platforms. This brings us to the next question – what do the monetization models for both the parties look like and what kind of profits can they potentially fetch. A classic example here is LendingWorks, a P2P loan provider with a tie-up with Revolut. Since 75% of LendingWorks’s business is driven by its API-driven partner network, it is a clear win-win for all. But not all Fintechs have this kind of business model to succeed in the game. Hence, there is potentially a distinct need for reselling white labeled products / services as well as striking investment level partnerships in the form of joint ventures for this model to sustain itself.
Given these diverse scenarios, banks will have to align their corporate, technology and business strategies to win in the API-driven economy, where partnering with Fintechs is inevitable.


The Case for Looking Beyond UX

Before we proceed further, first let me clarify what I mean by BX and UX. BX – Business eXcellence and UX – we all know it, User eXperience.
UX as far as I remember became a trending term once the world got to experience Apple products, not that it didn’t exist before, but it was not a selling point until then (more on this at: ).
If I am writing this blog to share my views with the world and then if I write what I want to write and in a manner that is ok for me and heed least consideration to readers of this blog, then it is an antithesis for UX!.
For some time, we also struggled to understand the difference between UI (User Interface) and UX, as many felt that there was no difference and then some felt a more appealing UI is UX.
Nowadays, UX is not just a selling point, but it is a hygiene, it is a design philosophy and users are drawn to products primarily because of UX.
Hence, naturally whole world is gaga over UX and we now have UX specialists/designers as integral part of product design.
Are we too hooked onto UX, is UX overhyped, are we overtly obsessed with UX?, let’s see.
Traditionally we have been taught to solve problems and we just do that often paying no regard to the user for whom we are solving the problem.
To me UX is a thought (not an afterthought!), designing with the user at the center of the thought.
Ideally as very few would appreciate, acknowledge and practice – UX, is an all-encompassing experience that an end user of a product/service has got to do with anything/everything around that product/service. If I design something without empathizing for the You in UX or if the You in UX is different from me who is designing it for you, then it is all mixed-up.
But then, let’s face it – why are we creating a great UX? whom are we really creating this UX for?
To me, let truth be told, this UX which we claim to design for the end user, is actually we are doing it for ourselves – the ones who own the product/services, because at the end of it, we are doing it to increase business excellence, revenue, profitability!
As mentioned earlier, UX is an all-encompassing experience which focuses on creating a frictionless, personalized, uncluttered experience but the underlying principle is – this is all done so that – we don’t lose a customer to rival, we increase customer loyalty, we make customer feel how important they are for us and ultimately, we want more business from this customer.

Let me explain this with couple of examples:

We all have seen how banks/ FinTechs are alluring customers by providing option to aggregate all our financial instruments and provide a personalized dashboard to help get better at PFM- Personal Finance Management, oh really? . Well, truth is , this is a Google like strategy being adopted, as data is new fuel so that banks/FinTechs then can suggest/prompt/push for their products for an even better PFM!
Uber is next, we all hear it whenever we talk about digital disruptors, future business models etc., Here again, Uber definitely has redefined UX, but through an innovative business model and it is that business model which is drawing people to it. Thin line, but I will still give it to the underlying business model which is truly the disruptor.
One of the best ways to explain how BX is the under current in creating a superlative UX is – take a look at the experience offered to a business class ticket to that of an economy class ticket in an aero plane. Though I have never experienced a business class ticket so far, but have at least read about it and economy class, I know it all too well! Thanks to these airlines, we can clearly understand even in a mass industry like airlines, UX is directly proportional to business being offered.
You know the routine, as it is customary with my every other blog, I need to touch upon banking in some or the other way and here it is. In last 5 to 8 years, we have seen the emergence of new types of banks, FinTechs which are posing challenge to traditional banks, though not in terms of direct business, but to their traditional thinking. One of the key shifts in focus for traditional banks has been their new found love towards UX. Every discussion, decision is incomplete without UX being the focal point of all effort which no doubt is good.

But I make three observations here:

  • Due to the focus on UX, development work, time to market seems to have been increased

  • Without a structural change in the culture of the bank, ads/marketing effort which doesn’t find a place in overall servicing, is leaving customers more dissatisfied than earlier

  • UX at the end of it is still largely a perspective and thanks to technology, each experience has a very short shelf life. Hence, more the effort, time, money invested in perfecting UX, to me appears like a never ending exercise.

What would be a good UX for me with my bank will be – keeping it simple, being available when I need it, where I need, how I need it and finally, just take care of my money and help me grow it. I don’t really care what UX guideline, principle you follow or preach, don’t spend millions of rupees on that and then produce an abysmal quarterly result. Let your focus be on improving business efficiency, operational efficacy as these things hurt me more than you not giving me an Apple like experience!
Not everyone can be good at everything, but you are a specialist or good at something, then you better retain that and that’s what I expect from my bank, to be fundamentally good at banking and if you can make overall experience better, then I won’t mind that!

True API-fication Begins After Monetization

Facebook’s “Like API” is a free API that lets developers add a “Like” button on their web or mobile properties. Being a company that derives value from user generated content Facebook thus gets a wider net of users to feed information into its network.
Amazon Associates program lets mobile developers use its Mobile Associates API to sell products through in-app purchases, and offers developers 4 per cent to 6 per cent on qualifying purchases.
eBay uses a similar revenue sharing model by incentivizing developers to build quality apps, and incentivizing platforms to provide quality APIs.
APIs sure are a great way to generate value and revenue. But there is no single playbook for pricing when it comes to APIs. It is abundantly clear that any API strategy must support the overall business strategy. Thus API monetization models also must take into account who consumes the API, what they gain from using the API, and what they should pay for while consuming the API.
Infosys Finacle’s Point-of-View on ’Platform Business Model for Banking’, broadly classifies API monetization models in four buckets:

APIs that lower operational support cost can be positioned as free APIs, while APIs that share functionality, data or information are typically charged. Partners and fintechs may be charged for different levels of usage (Tiered), to pay a fee based on usage (Pay as you go), to pay according to the consumption of specific units of computing or service (Unit such as cpu cycles), or to use the API for free but pay for additional services (Freemium). The revenue sharing model is adopted for partners who bring in new clients. There are also indirect monetization models that help improve customer service or generate leads.
According to the survey by Efma and Capgemini for ‘World Retail Banking’ report, the most preferred revenue model is where the consumer of an API pays a fee per API transaction. Another popular model is revenue-sharing where third parties consume the API and drive new sales. Among the less popular models are the annual or monthly licensing fee model, fee-based models for data or insights, and the API call fee model – in which third parties pay each time they call a service offered through an API.
Fidor bank is an early mover in the API and platform space. In a combination of transaction based and revenue-sharing models, the bank offers its proprietary operating system fOS, which uses APIs to help businesses develop their own banks. German telecom operator Telefonica O2’s mobile only bank is built on top of fOS. Fidor offers the technology support to run the online bank, access to a German bank license, customer support, card services, compliance and marketing support. Fidor earns revenue through tech-related commission income from fOS and commission income from transaction fees.
At Infosys Finacle we propose 6 key strategies for API-led platform business. The first is embedding banking in customer chosen applications. With APIs, banks can expose every little service for their big corporate customers to pick, choose and structure as needed. Besides corporates, digital businesses like FinTechs and e-commerce players are a good target here. As they do this, they look to make money from a pay-per-use or a revenue sharing monetization model. BBVA is a case in point. The bank’s APIs are available in 8 categories. In a pay-per-use or revenue-sharing arrangement consumers of the API can integrate the bank’s products and services in their own interfaces. While this was mostly in the sandbox environment, the APIs are now available in the production environment.
The second API strategy is where banks participate in a giant ecosystem such as Apple Pay, Google Pay or Amazon. Banks choosing to join such ecosystems hope to monetize them directly through revenue sharing, or indirectly through higher customer acquisition rates and lower cost of operations.
The third API-led platform strategy is that of curating an ecosystem, to build a well-matched community of buyers and sellers, and increase customer loyalty by providing greater value. The popular monetization models for this strategy include freemium models such as subscription based pricing, revenue sharing with producers, or advertising-based models as in the case of Alibaba. Banks also monetize this ecosystem indirectly by enhanced customer retention as demonstrated by HSBC Connections Hub.
Another popular API strategy for banks is Banking-as-a-Service, where a non-bank relies on a licensed bank for basic banking elements, such as technology stack, operations, network connections and compliance. The non-bank builds new experiences and value propositions on top to offer to its end customers. Examples of this include Fidor Bank, and India-based startup Moneytap built on RBL bank’s APIs. The preferred revenue model here is revenue sharing with ecosystem players for the capability offered as-a-service. Banks sometimes also resort to transaction-based pricing in such a model.
Another Banking-as-a-Service API strategy is where banks offer their APIs not only to non-banks or neo-banks, but also to traditional banks. An example is China’s WeBank that offers payment services to small banks. Pay-per-use, revenue-sharing or subscription based model are the preferred monetization models for this API strategy.
Instead of growing an ecosystem, some banks buy or invest in an existing ecosystem player to gain access to their network. In such a case, the bank typically adopts the monetization model of the acquired platform player.
Different banks may adopt different API strategies based on the kind of ecosystems they curate, create, cultivate or gain access to, and the value they aim to generate. As more and more banks take their APIs live with production data they must align their monetization models with the overall business strategy and must evaluate each API based on who consumes it, the value it creates for the consumer, and the potential value they can create together.
You might find our paper on ‘Platform Business Model for Banking ‘ interesting, you can access it here.