AI Is Rewriting Finance. And Our Digital Footprint Is Part of the Story.

Today, that question is changing.

We already generate mountains of data. The real question is whether we know how to understand it.

AI is moving faster than many financial institutions can adapt. Banking, CFDs, payments and crypto are feeling the pressure. One major change is being overlooked.

The information we create through our digital activity is becoming more useful in financial decisions.

And that changes the game for businesses, customers and all of us.

For decades, institutions relied on income, credit histories, bank statements and declared information.

Alternative data broadens that picture.

It can include cash-flow patterns, digital payment histories, utility bills, online merchant sales, platform transactions and, in some cases, other information that a provider is legally allowed to use.

What is new is the ability to analyse varied information at a practical cost.

The International Finance Corporation's 2026 report on alternative data and AI explains how these tools can help lenders evaluate people and small businesses that traditional credit systems often miss.

Think about a small business with limited borrowing history but years of consistent digital sales and payments. Those records may tell a lender something that an empty credit file cannot.

Not every signal is reliable. But ignoring alternative data entirely makes little sense.

Banking: beyond the credit score

Imagine two people applying for financing.

One has a long conventional credit history. The other has little formal borrowing history but a stable, verifiable pattern of business income.

Historically, the second person might have struggled to show their financial reliability.

Alternative data can help some lenders understand that person better. AI can assist by finding patterns, identifying inconsistencies and assessing information that would take people much longer to examine.

This is already happening.

In a 2024 survey of 118 UK financial firms, the Bank of England and Financial Conduct Authority found that 75% were using AI in some form. That does not mean 75% were using AI for lending. Adoption spans many applications.

CFDs: the blind spot is often inside the broker

Now look at our own industry.

I've spent years working with brokers, and I keep seeing the same problem. A broker spends money attracting visitors, gets them onto a website, asks them to register, and then wonders why conversion is disappointing.

But what happened between that first visit and the moment they left?

Which page did they read? Where did they hesitate? Which question was never answered? Did they struggle during onboarding? Did they abandon the deposit process?

Those are questions a broker can investigate using appropriately collected website and customer-journey data.

AI can help analyse patterns across those journeys, spot friction and help teams test improvements.

That is not permission to manipulate customers into trading more. Investment firms still have conduct, privacy and investor-protection obligations.

The opportunity is to understand behaviour well enough to make onboarding clearer, communication more relevant, fraud controls stronger and decisions less dependent on someone's gut feeling.

A CRM cannot rescue a company that ignores its data.

Crypto: a different kind of digital trail

Crypto adds another dimension.

Public blockchains record transaction histories that analysts can inspect. The Bank for International Settlements has explored how transaction provenance can support anti-money-laundering controls.

AI and other analytical methods can help detect patterns across large networks.

But here is the catch: an on-chain transaction does not automatically tell you who owns a wallet or why a transfer happened. Context matters.

Analysis still needs human judgement.

Your digital presence now has three different meanings

This is where I think people underestimate the shift.

First, there is the data you create through economic activity: payments, sales and other records that may, where legally permitted, help a provider understand your business.

Second, there is the journey you create as a customer: what you view, where you stop, which problems you encounter. Companies can use properly collected data to improve their services.

Third, there is your public digital presence: your website, professional profile, published work, company information and the reputation you build.

That last category matters for a different reason.

It shapes how people, partners, clients and increasingly AI-assisted research tools can discover and evaluate you. It is not the same thing as a regulated credit score.

A business with clear, accurate information gives others more to work with.

The point is not to post on LinkedIn ten times a day.

It is to make sure your digital identity reflects what you actually do.

More data does not automatically mean better decisions

There is a dangerous side to this story.

Wrong information can produce wrong conclusions at extraordinary speed. Biased datasets can exclude people. AI can make decisions look scientific even when the assumptions behind them are weak.

The EU AI Act prohibits certain harmful social-scoring practices and classifies AI systems used for personal creditworthiness assessments as high-risk, subject to specified exceptions. ESMA has also reminded investment firms that using AI does not remove their responsibility to act in clients' best interests.

So no, the future should not be a world where an algorithm judges your financial future from a few social posts.

Financial institutions need a lawful basis for data processing, quality controls, explainability and human accountability.

The real competitive advantage

Here's where I think this is going.

Over the next few years, the companies that win will not necessarily be those buying the most AI tools.

They will be the companies that understand what information they already have, what they are legally allowed to use, and which decisions that information should improve.

AI is accelerating the shift from making decisions on what people declare to also learning from what verifiable data shows.

The uncomfortable question is not whether AI is coming for finance.

It is whether the finance industry is ready to understand the data sitting right in front of it.