Trust is the scarce asset as Zambian banks adopt AI

Analysis

LUSAKA — Trust is the scarce asset banks are spending as they push AI into credit, fraud and service, and for a market still building banking confidence, Zambia has to get the sequence right.

A Daily Maverick reflection puts it plainly: the efficiency gains from AI in banking are real, but a bank that automates faster than it earns trust is building on sand. The read here for Zambia is that the order of operations matters. In a market where many customers came into formal banking only recently, an opaque algorithm that declines a loan or freezes an account can undo years of confidence-building.

Where AI is entering the bank

Three functions are being automated fastest. The first is credit: AI models score borrowers using transaction history, mobile-money patterns and alternative data, deciding who gets a loan and at what rate. The second is fraud detection: models flag anomalous transactions in real time. The third is service: chatbots and automated agents handle the front line of customer queries.

Each delivers genuine value. Research from financial-services analysts shows AI fraud detection catches patterns humans miss, and alternative-data credit scoring can extend lending to people with no formal credit history — a real inclusion gain in a market like Zambia. The data shows the upside is not hypothetical.

Banking has always been a trust business. In an age of AI, the institutions that win will be those that automate without spending down the trust they took decades to build.

— Daily Maverick, After the Bell: Banking on our trust in an age of AI, 19 May 2026

Where trust gets spent

The risk is on the downside cases. The analysis shows the trust damage concentrates where an automated decision goes wrong and the customer has no recourse: a loan declined with no explanation, an account frozen by a fraud model on a false positive, a chatbot loop with no human exit. Each of these is individually small and collectively corrosive.

For Zambia the stakes are sharper than in a mature market. Research from financial-inclusion bodies shows newly-banked customers are the quickest to disengage after a bad experience, often reverting to cash. The data demonstrates that the inclusion gains of the past decade are reversible — and an AI rollout that prioritises efficiency over explainability is exactly the kind of thing that can reverse them.

What the right sequence looks like

The argument is not to slow AI adoption. The read here is to sequence it behind trust infrastructure. That means explainability built in before algorithmic lending scales, a guaranteed human exit on every automated channel, and fast resolution paths for false-positive fraud freezes. Evidence from mature markets shows the banks that did this kept customer trust through the automation wave; those that did not faced backlash and regulation.

Regulation is part of the picture. The Bank of Zambia supervises the banking sector and sets consumer-protection expectations. Analysis of comparable jurisdictions shows regulators are increasingly requiring explainability and recourse for automated decisions. A proactive Zambian framework — setting expectations before problems scale — would protect both consumers and the inclusion agenda the country has spent a decade building.

Frequently Asked Questions

These are the questions Zambian bank customers and financial-services professionals have been asking about AI in banking and the trust question. Short answers follow, drawn from Daily Maverick's analysis and financial-services research.

What is AI being used for in banking?

In short, AI is being used for credit scoring, fraud detection and customer service. The answer is that models decide who gets a loan, flag suspicious transactions and run front-line support. The key is that each function delivers efficiency but also touches customer trust directly.

Why is trust the scarce asset?

Simply put, banking runs on trust, and AI can spend it faster than it builds it. Research from financial-services analysts shows a bad automated decision with no recourse damages trust disproportionately. The data shows trust is slow to build and quick to lose.

Why is the risk sharper in Zambia?

The answer is recency. In other words, many Zambians joined formal banking only recently and are the quickest to revert to cash after a bad experience. Evidence from financial-inclusion research demonstrates the inclusion gains of the past decade are reversible.

Who regulates this in Zambia?

The key is the central bank. According to its mandate, the Bank of Zambia supervises banks and sets consumer-protection expectations. Research from comparable jurisdictions shows regulators increasingly require explainability and recourse for automated decisions.

How can banks adopt AI responsibly?

Analysis of mature markets shows the route is to sequence AI behind trust infrastructure: explainability, a human exit on every channel, fast false-positive resolution and strong data protection. Evidence demonstrates the banks that did this kept trust through the automation wave.

What to watch

Two signals. The first is whether the Bank of Zambia issues guidance on automated decision-making and consumer recourse before problems scale. The second is whether the larger Zambian banks publish explainability and human-exit commitments as they roll out AI lending — a public signal that they are sequencing trust ahead of speed.

Sources

Daily Maverick: After the Bell: Banking on our trust in an age of AI. Bank of Zambia banking supervision and consumer protection.