“AI-Led Contextual Banking Will Lead to Higher Customer Centricity”
Artificial Intelligence (AI) is becoming ever-more importance in our lives and at work. This is particularly true in the financial services sector, where leading banks and finance firms are already launching disruptive AI-powered innovations. In the process of becoming “AI first” in vision and execution, banks are also getting more empathetic toward customers, getting to know them better, feeling their pain and delivering solutions that can make their lives better and easier. This calls for personalized, contextual banking experiences.
In a recent interaction with CXOToday, Manish Maakan, Chief Executive Officer (CEO) at Intellect Global Transaction Banking (iGTB), explains the importance of contextual banking in the age of customer centricity and why AI led customer engagement is the next monumental shift in the transaction banking landscape.
- What is contextual banking and what does it mean for the payments industry?
Contextual banking is a banking experience that takes into account the intent, the situation, the language, the habits, the urgency and the legitimacy (regulatory constraints) behind each transaction. To substantiate the meaning of context, let me talk about the calendar apps in the non-banking world– these calendar applications are designed to give a gentle nudge to the user five minutes before a meeting is set to take place. This app also relays real time information to the user by taking into account the venue of the meeting, the traffic nearby and the weather in the locality, so as to provide that information beforehand to the user, thus ensuring that the user’s value for the app is realized. This level of anticipatory thinking has now been extended to the world of corporate banking.
For example, some banking systems ask the user to first choose the payment mechanism to use, i.e. how to pay. That’s a bit like booking a flight by choosing the fuel the plane should use! Many systems today instead start with “who do you want to pay?” and then follow it up with questions like: how much? What currency? What value date? What reference? What bank? What bank account? Where to pay it from? And many other SWIFT fields. A clever bit of contextual thinking, however, makes you realize all the answers are there for the taking if you ask first what you want to pay – salaries, or an invoice. The invoice holds details of the amount, the due date, the bank and bank account, the reference field and so on. And as for where to pay it from,. the ‘habit’ context says it’s probably the same account used when you last paid this supplier. And the mechanism can be advised by the bank – the cheapest, the fastest etc – from the possible rails. That can make it a one-click payment.
But we can go even further. If you ask the question why you want to pay, suddenly the context shifts from the mechanics of the operation to the smarts. Why are we paying this invoice today? We may get no better settlement discount tomorrow or the next day, we are a bit low on cash, and an incoming payment is expected tomorrow. Contextual banking can work this out and even make it virtually a no-click action.
- What is the role of AI in contextual banking? Do you think it has the power to supercharge and completely disrupt the landscape as we know it by becoming the bridge between financial brands and customers?
As contextual banking is all about doing the right thing to fit with the situation -a variety of AI technologies can be used, from rules to machine learning to neural nets trained on example data. For example, every time the context of ‘habit’ is used, machine learning is involved – from a simple sensible use of defaults to the complex analysis of past behavior, ML plays a vital role. For example, our cash flow forecasting issues a prompt if the user does not include a previously regular outgoing or incoming payment in a future projection. It learns from what the user has done before. Contextual banking gives the perspective that the treasurer’s job is to set financial policy: it’s the bank’s job to execute that, and with AI the bank can increasingly choose or suggest the best way to do the same. We call it the ‘best next action’. Additionally, it does a great job to harness the system to engage in cross-selling – we call that, ‘best next offer’ (for example, “would you like me to set up a sweep to cover shortfalls like this automatically? The fees will be…”).
- With the use of tech rising exponentially in banking, how can we ensure that companies build products in a way that it acts as the emotionally adept translator between customers and the complexity of the challenges uncovered by new technologies?
The key to contextual banking is ensuring that the persona of the user gets codified into the user journeys. It is also important to take into consideration the persona of the corporation and its customer journeys. For example, a user journey might be “to see all my cash in one place”. This might be true, but it doesn’t really capture the ‘why’ of the journey. There is often a deeper customer journey, maybe “to see when we will have enough cash to buy a piece of plant XXX”. That journey shows that the cash visibility is just one step of a journey that might benefit from other bank offerings, such as cash flow forecasting, leasing or trade finance. Corporate banking is more repeatable and arguably less emotional than personal banking, so it lends itself better to AI, especially for use cases where a clear history of success data exists.
- What do you think is the relationship between embedded banking and contextual banking? How do you see it changing over the years?
Embedded banking, or invisible banking refers to having the banking happen as a by-product of the corporation doing its normal business, so that ‘banking’ becomes an outcome, not a chore. Contextual banking is a vital precondition of embedded banking. Global industries across the spectrum use virtual accounts in different ways and with various languages. So– a landlord has tenants who pay rent; an insurer has brokers who pay premiums; a lawyer has cases with clients who pay and receive monies, possibly in escrow.
Each industry has its own variation, and our systems are able to adapt to each of these contexts with a very simple set up. But, embedded banking goes further than this. With contextual banking, a landlord can open a virtual account for each property or each client, as they choose and this same autonomy can be extended to the other industries as well. But there is no reason for this to remain an explicit act.
There is no reason the system the landlord commonly uses, or the insurer, or the lawyer, whether a generic ERP system like SAP cannot simply, though APIs, automatically open and show the virtual account for the user. Embedded banking essentially ensures that there is never an explicit need for the user to directly interact with the bank.