The questions banks ask us have changed. Two years ago, almost every first meeting opened with "can you build us a chatbot?". Today it's a transformation lead with a longlist, asking which use cases should go live first, what the ones nobody demos actually deliver, and how other banks sequenced it.
The shift matches the numbers. McKinsey estimates that generative AI could add $200 billion to $340 billion of value to global banking every year. Accenture went further and concluded that banking is likely to be more profoundly impacted by gen AI than any other industry, with 73 percent of the time spent by US bank employees having high potential to be impacted by it. Nobody gets to those numbers with a widget in the corner of a website. A bank runs hundreds of processes, and an uncomfortable number of them still depend on copy paste, PDF attachments and hold music.
We're 21 use cases in, and counting, live or in delivery for banks and card networks across the Middle East, Africa and Europe, from the front door to the plumbing. This post is the map we draw on the whiteboard when that longlist question comes up.
A chatbot is a channel. A use case is a job. And banking has more jobs to give than any industry we work in.
Customer conversations are the natural first step, and the public numbers show how far ahead the leaders already are. Bank of America's assistant Erica has handled more than 3 billion client interactions since 2018 and now averages more than 58 million per month. NatWest's Cora went from just over 5 million customer queries in 2019 to 10.8 million in 2023. DBS now runs a gen AI assistant for all of its corporate clients, and Klarna famously reported that its AI assistant handled two thirds of all customer service chats in its first month, doing the equivalent work of 700 full time agents while cutting the time customers needed to resolve an errand from 11 minutes to under 2.
What those headline numbers don't show is how unforgiving the work is. A banking assistant that is 95 percent right is a liability, and "the front door" is rarely a single door. In our own projects it looks like this:
Voice in the customer's actual dialect. We run telephone assistants for banks that answer in Egyptian Arabic, Gulf dialects and the Saudi "White dialect", not textbook Arabic. And voice has rules of its own: one of our assistants formats every number phonetically, because a text to speech engine that reads "1500" as a digit string will say something a customer on a phone line simply doesn't parse. We've written before about why dialects and voice quality make or break these projects.
Digital humans as the face of the bank. For several banks we run avatar assistants that answer strictly from an approved knowledge base and emit emotion tags with every response, so the avatar's face matches the content: a nod when a customer says thanks, compassion when someone reports a problem. It sounds cosmetic until you watch a customer talk to one, and then you realize tone is a big part of trust.
Card servicing. Lost card, stolen card, activation. An identity check before any sensitive action, and a handover to a human for the edge cases. Not glamorous, extremely high volume, and with ContactBabel putting the typical UK contact centre call at around £6.26, the math writes itself.
Offers, loyalty and redemption. This is my favorite underrated use case. Bond's 2017 loyalty report, run with Visa across the US and Canada, put unredeemed loyalty points at $100 billion, and found that more than one fifth of program members had never redeemed anything at all. We build assistants that filter a bank's offer catalog by card, category and location in the middle of a conversation, and render the results as native carousels in the app. I'd file that under engagement revenue rather than support, and for years it sat in a queue nobody was answering.
Financial wellbeing. Spending insights, budgets, anomaly alerts, always showing the underlying math. The assistant that helps a customer understand their money is the one they keep talking to.
Everything above shares one assumption: the customer starts the conversation. The newest requests on our whiteboard flip that. The bank has a list of customers, an offer each of them already qualifies for, and a contact center that will never work through the list. Three new jobs joined the queue while this post was in draft, all of them outbound, all of them voice, and all of them from banks in the Gulf:
Sales lead generation for existing customers. The bank already knows which products each customer is eligible for; that knowledge mostly sits unused in a CRM. The agent calls proactively, recommends only the products the customer actually qualifies for, and captures and qualifies the lead for a human to close.
Card and spend activation. A newly approved card that never leaves the drawer is pure cost: issuance, risk, zero interchange. The agent contacts new cardholders, walks them through activation on the call, and nudges the first transaction.
Credit line increase. An increase offer sent as an app notification mostly dies unread. A short call that explains what the offer means, answers the customer's questions and records the acceptance turns an ignored push message into a signed yes.
The agent is the caller, not the campaign brain.
We treat that as a design decision, and I think it's why these projects land. The bank keeps ownership of contact lists, targeting criteria, campaign rules and eligibility data; all of that comes from its own systems. The agent plugs into the telephony environment the bank already runs (Cisco, in the most recent case) instead of bringing a dialer of its own. And its job stays deliberately narrow: conduct the conversation in the customer's own dialect, capture the outcome, and hand the lead or the acceptance to the workflow that owns it. No campaign management suite, no segmentation engine, no analytics platform pretending to be a marketing department. Note that everything the middle office section below insists on (guardrails, identity checks, an audit trail) applies unchanged here, because an outbound call that touches credit is still a regulated conversation.
The KPI flips along with the direction. Inbound assistants are judged on cost avoided. These three are judged on revenue started: leads qualified, cards activated, credit accepted.
Put everything we deliver for banks side by side and it stops looking like a chatbot project. It looks like this:
A caveat before we walk through it: this map is a snapshot, not a catalog. It gains rows every few months (the outbound trio is the newest addition), and the newest rows rarely appear anywhere near the chat window. The first column is what gets demoed. In our experience, the other two are where much of the value sits. Let me take you down to those floors, because most longlists never reach them.
KYC and onboarding are the clearest example of a process everyone tolerates until they see the price tag. Fenergo put the average cost of a single corporate KYC review in 2023 at $2,598, up 17 percent in a year, with banks globally taking 95 days on average to complete one. A later Fenergo survey found that 67 percent of corporate, institutional and commercial banks have lost clients because of slow and inefficient onboarding and KYC. And on the retail side, Signicat found that 68 percent of European consumers abandoned a financial application partway through.
Look at what onboarding actually is: collecting documents, checking them, chasing people who went quiet, escalating the doubtful cases, keeping an audit trail. That is a conversation problem stapled to a document problem, which is precisely what a bot plus agent combination is good at. We've built onboarding flows where a bot handles the predictable rails, a validation agent checks every uploaded document (is the full passport visible, is the signature there, are all fields filled), reminders go out on a schedule, and a human gets a structured task the moment confidence drops. We never bend on one design rule. The validation agent returns decisions and never passport data, so it answers "approved" or "rejected, because the signature is missing," and the personal data stays where it belongs.
Fraud is the other middle office giant. Card fraud still cost the world $33.4 billion in 2024. AI here is nothing new: an Economist Intelligence Unit report for Temenos found back in 2022 that 91 percent of US banks used AI for fraud detection. What's new is what the generative layer promises on top: when Mastercard announced its gen AI enhancements, its initial modelling showed fraud detection rates rising by 20 percent on average and as high as 300 percent in some instances, and its own analysis showed false positives falling by more than 85 percent. Those are modelling figures, not audited production results, but the direction is clear: fewer blocked holidays, more caught criminals.
In banking, I find what an AI reliably refuses to do more impressive than anything it can do, and that sums up the middle office pretty well. Guardrails, identity checks before sensitive actions, human review when confidence drops, an audit trail on everything. When we demo this, the compliance team leans in further than the innovation team. That's when you know a project will survive contact with production.
Here's a pattern I see constantly: the AI budget goes to the front office because that's what the board can see, while the clearest business case in the building sits in accounts payable. Ardent Partners benchmarks the average cost of processing a single invoice at $9.84, with the top performing teams paying 79 percent less than their peers. Processing time shows the same spread: 8.2 days on average against 2.9 days with advanced automation.
So the demo lives in the front office, and the business case usually lives in the back. This is agentic workflow territory, and it's where the platform earns its keep without anyone noticing. Invoices come in by email, an agent extracts and validates the line items, matches them against purchase orders, posts the result into the ERP and routes only the exceptions to a human. We run tender document pipelines that read an entire government tender package and produce Excel files ready for submission. We convert offer catalog spreadsheets into clean SQL databases that a retrieval agent can query mid conversation. We transcribe podcast audio into Arabic and English transcripts, consolidate data scattered across decentralized databases, and turn log files into categorized error reports. None of this will ever be on stage at a conference. All of it compounds, every single day.
There's a question we hear more and more from banks in Latin America, the Middle East and Asia, and it has nothing to do with cost savings: how do we get customers to open our app when nothing is wrong? Nobody opens a banking app for fun. The ambition behind that question has a name, the super app: one app where the customer books a flight, orders dinner, arranges insurance and pays for all of it, with the bank underneath every transaction.
That's an engagement question more than a technology question, and the ambition stopped being exotic a while ago. Careem's own site calls it building the everything app, serving over 48 million customers across ten countries. WeChat sits at 1.43 billion monthly users. Brazil's Inter runs banking and digital commerce, shopping included, in one app for 44 million clients, logging more than 20 million logins a day. Revolut, 65 million customers and aiming for 100 million, will book your hotel with cashback and sell you an eSIM data plan for over 100 countries right next to your current account. Gartner expects that by 2027 more than half the world's population will be daily users of multiple super apps.
US customers, at least, seem open to it. A 2024 Chase survey found that 86 percent of consumers prefer one app for all their banking, and 38 percent prefer to book their travel from one place. And the engagement math is what makes boards lean in: Inter reports that clients in its loyalty program use 2.3 times as many products as other clients, and at KakaoBank noninterest income, increasingly fee and platform income, has grown to 37 percent of operating revenue. Time in app turns into share of wallet, which is why boards care about it.
Our field observation, having built inside one: a super app without a conversational layer is a shopping mall without a concierge, twelve tabs and a search bar. This is where several rows of our map converge. We've built the travel concierge that knows visa rules, flight routes and hotel tiers inside a regional super app. The food ordering agent that remembers dietary preferences mid conversation and renders the menu as a native carousel. The card intelligence that surfaces the right card at the right moment and redeems loyalty points at checkout. For a bank taking this route, the AI layer is what makes twelve services feel like one app. It shouldn't be treated as the tenth workstream on the program, and it runs on the same guardrails, offer pipelines and payment confirmation layer that were already on the map.
Most banks hit a strategic fork around use case number three. 21 use cases as 21 separate vendor projects means 21 security reviews, 21 integrations, 21 ways of logging, and no shared learning between any of them. That road ends with a pile of AI tools nobody can govern.
The alternative is treating use cases the way you treat employees: different jobs, one building, one set of house rules. On Blits.ai, the voice assistant, the KYC workflow and the invoice pipeline share the same knowledge bases, the same guardrails, the same audit trail and the same test suite. When a new model comes out, we swap the engine per job and prove nothing broke by replaying real conversations against it. When an agent is allowed to touch money, it goes through the same confirmation layer we described when AI was first allowed to spend money by itself. And every use case reports into the same KPIs, because a use case without a number attached is a hobby.
The market is moving this way fast. Temenos found that 75 percent of banks are exploring gen AI deployment, with 36 percent already deploying or in the process. Most banks have stopped asking whether and started asking in what order.
After watching this go right and wrong at many banks, my advice is to pick one use case per floor.
- One from the front door, because visible wins buy you organizational patience. Card servicing, an offers assistant or an outbound activation campaign, with deflection, satisfaction or conversion as the number.
- One from the middle office, because that's where risk and regulation live and you want to learn governance early. Onboarding is ideal, because the KPI is days and everyone understands days.
- One from the back office, because it pays for the other two. Invoice automation has a cost per invoice benchmark waiting to be beaten.
Then resist the urge to celebrate. Measure, publish the numbers internally, and let the second wave ride on the infrastructure the first wave already paid for: the knowledge bases are built, the integrations exist, the governance is in place. That's how 21 use cases stop being 21 projects and start being one capability.
The flashiest chatbot rarely belongs to the bank that's winning with AI. The winners are the ones that noticed the queue of jobs behind the chat window, answered the governance question early, and simply started working through the queue.
We'll happily draw this map on your whiteboard, including the floors your customers never see. By the time we do, the number probably won't be 21 anymore. Let's talk.
Updated on 12 October 2026.
