Remember when staging was where all the real testing happened? You’d write your code, merge it, and then wait to see what broke once everyone’s changes landed in the same place. Sometimes it was fine. Sometimes it was two days later and you’d already forgotten what that pull request even did.

A lot of teams don’t work that way anymore. More and more of them test each change in its own little isolated sandbox first, before it gets anywhere near a shared environment. Staging’s still around, sure. It’s just doing a different job now. I think it’s worth unpacking why that shift happened, because once you see it, it’s much easier to figure out how far your own team should go with sandbox environment testing.

What staging was built for

To be fair to staging, it made total sense when it showed up. Most apps were one big codebase back then, releases happened every few weeks, and the main question before shipping was pretty simple. Does everything work together in something that looks like production?

One shared environment was a great answer to that. Production-like servers, a copy of realistic data, every piece running side by side. QA could click through the whole product, and everybody got one last look before customers did.

And honestly, it’s still good at that. What changed is that teams now need a lot more answers, a lot more often.

What sandbox environment testing means

A sandbox is just an isolated environment that belongs to one thing. One change, one developer, or one test run. Whatever happens inside it stays inside it, and nothing outside can mess with what you’re testing. It might be on your laptop, inside a CI job, or spun up for a single pull request and thrown away after.

The way I think about it is ownership. Staging is shared by everyone. A sandbox exists for one purpose and then it’s gone. Sounds like a small difference, but it changes how testing feels day to day more than you’d expect.

Why teams are moving testing earlier

Too many people, one environment

Picture twenty engineers, a dozen microservices, and one staging environment they all deploy to. Someone pushes a half-finished change and checkout breaks. Someone else’s test data quietly overwrites yours. Your test fails, and now you’re wondering: was that me, or literally anyone else? Before long, people are asking in Slack whether staging is “stable right now” before they even try testing.

Sandboxes make that whole conversation go away. If every change gets its own space, a failure belongs to that change. Nobody’s coordinating who’s testing what, and folks can just get on with their work.

Finding bugs while the code is still fresh

Then there’s timing. A bug you find in staging shows up after merge, usually once the author has moved on to something else. A bug you find in a sandbox shows up in the pull request, while it’s all still in your head.

That’s really all “moving testing earlier” means. The same integration checks that used to wait for staging now happen before merge, when fixing them is quick and kind of painless.

Keeping sandbox environments realistic

This is the part that decides whether sandbox environment testing actually works for you. An isolated environment is only useful if it behaves like the real thing. And the real thing depends on a lot of stuff: other services, databases, caches, queues, plus third-party APIs you have zero control over.

Spinning all of that up for every sandbox? Gets heavy fast. What’s caught on instead is virtualizing those dependencies. You record how they really behave, the genuine traffic between your service and everything it talks to, and then replay that inside the sandbox. Your service gets realistic answers from its API calls, its database queries and its queue messages, and none of those real systems have to be there.

A few things make this click in practice. If the recording happens down at the network level, you don’t touch the app’s code at all, so the sandbox stays close to how the service really runs. If it captures every kind of dependency in one go, not just HTTP calls, nothing sneaks out to a shared system. And if the recordings sit as plain files right next to the code, refreshing them after a dependency changes is just another diff to review.

What you end up with is tests that give the same answer every single time. The sandbox doesn’t care if a partner API is having a slow afternoon, or if someone just wiped the shared database. When a test fails, it’s pointing at your change. Which is kind of the whole point.

Where staging still fits

None of this makes staging useless. It just gets to focus on what it’s actually best at.

Staging’s still the right spot for checks that need the full, real system. Things like making sure infrastructure and config are right, load and performance testing against production-like resources, or a final end-to-end walkthrough before a big launch. Those are slower, broader checks, and they don’t need to run on every pull request.

The teams I’ve seen get this right use sandboxes to catch most integration bugs, and treat staging more like a final confirmation step. Staging gets a lot quieter. And when something does break there, it’s usually something genuinely about the environment, not a bug someone could’ve caught days earlier.

Questions to ask before shifting testing earlier

Every team starts from a different place, so here’s what I’d talk through first:

• How often does shared staging hold your team up? If people are waiting on it, or puzzling over failures that aren’t theirs, that’s a pretty clear sign.
• What does each service actually lean on? Write down the APIs, databases, caches and queues. Each one needs a plan inside a sandbox.
• How will dependencies stay realistic? Real instances, recorded behavior or hand-written fakes all work differently, so decide who keeps them current.
• How much setup can each service handle? Anything that needs code changes in every service adds up quickly once you’ve got a lot of them.
• Where will the sandboxes run? Laptops, CI jobs and per-PR environments all come with different trade-offs on speed and resources.
• What stays in staging? Agree on which checks still need the full system, so nothing important slips through.

Conclusion

So no, I don’t think sandbox environment testing is out to kill staging. It’s more that most problems get caught way earlier now, back when they’re still small and easy to make sense of, and staging gets saved for the handful of checks that actually need the whole system. From what I’ve seen, teams that switch end up waiting around a lot less. Fewer of those “wait, whose change was that?” threads, too.

If you’re curious, honestly, just pick your noisiest service. You know the one. It’s always the reason staging is red. Set it up in its own sandbox with recorded dependencies and let its integration tests run there on every PR for a couple of weeks. Then count. How many bugs got caught before merge that would’ve shown up in staging before? I’d bet that number does the convincing for you.

Sandbox Environment Testing vs Staging: Why Teams Are Moving Testing Earlier was originally published in Women in Technology on Medium, where people are continuing the conversation by highlighting and responding to this story.

Can pricing help a business find better-fit customers, rather than simply more customers?

In a conversation with someone, one sentence really stuck in my mind: a product is made to either drive revenue or cut costs.

I started thinking about how this could be the simplest way to look at a product in front of you and understand its actual value. If a product can genuinely drive revenue or cut costs, shouldn’t we be careful not to underestimate what it’s worth?

But this made me think about something else.

What if a product isn’t actually failing? What if it’s reaching customers who don’t fully understand its value, or who simply aren’t the right fit for it in the first place?

What if we’re looking at the wrong problem?

We invest a lot in making products easy to use. We simplify the interface, improve the user experience, make onboarding smoother, and try to remove as many barriers as possible so more people can use the product.

And that makes sense. Nobody wants to build a product that people struggle to understand.

But what if, in some cases, we’re trying to solve the wrong problem?

What if the product isn’t failing because it’s too complicated or difficult to use, but because of its price?

Not necessarily because it’s too expensive, but because its pricing might be attracting customers who aren’t the right fit for what the product actually offers.

If a product helps a business drive revenue or cut costs, getting the most out of it might still require time, effort, and a willingness to change how things are done. Making the product user-friendly can help, but it can’t guarantee that customers will invest in learning how to use it properly.

And maybe making a product cheaper to attract more customers isn’t always the answer, either.

What if we’re so focused on making the product accessible to more people that we’re overlooking whether those people are actually the ones who can get the most out of it?

An interesting example: fewer free trials, on purpose

I came across an episode of Startups for the Rest of Us featuring Jordan Gal, co-founder of CartHook, an e-commerce software company. The episode is titled We Went from Hundreds of Free Trials to a Few Dozen…On Purpose.

What interested me was how Gal described the customers they were attracting.

The company had two different groups of merchants. One group was a good fit for the software, while the other included smaller merchants who didn’t fit the level of sophistication the software required or the business model CartHook was building.

According to Gal, many of these lower-end merchants weren’t staying. Some left before their free trials ended, while others paid for a month or two and then left.

So the company had incoming demand, but a significant part of that demand wasn’t translating into long-term customers.

This is the part that made me stop and think.

If a product is attracting hundreds of people to try it, that might initially look like a good sign. But what if many of those people aren’t staying long enough to get value from it? Is attracting more people actually helping the business?

CartHook decided to address the problem by changing two major things: its pricing and its process for bringing customers into the business.

From the outside, it might have looked like a pricing change. But Gal explained that the change was really part of a broader process change, involving how the company qualified and onboarded customers. Pricing served that larger change.

Source: Jordan Gal, Startups for the Rest of Us, Episode 476, December 24, 2019.

Listen to the full episode

I found this distinction more interesting than the price increase itself.

The company wasn’t simply trying to charge more. It was trying to take more control over which customers entered its system, reduce churn, and build a healthier business.

And that brings me back to my original question: what if a product isn’t necessarily failing, but the way we’re pricing and selling it is attracting customers who aren’t a good fit for it?

So, what does pricing actually change?

I don’t think this example proves that higher prices automatically attract better customers. CartHook changed its pricing and its customer qualification process together, so we can’t separate the effects of those changes based on this example alone.

But it raises an interesting question.

When we decide on a product’s price, are we thinking only about how many people can afford it, or are we also thinking about the customers we’re trying to attract?

A product can be affordable and still be the wrong fit for someone. And a higher price doesn’t automatically mean a customer will understand the product or use it properly.

But pricing can influence who considers a product, who is willing to pay for it, and what kind of customer the business ends up serving. Combined with a thoughtful sales and onboarding process, it might help a business become more selective about the customers it brings in.

This doesn’t mean we should deliberately make products expensive to exclude people. It means we should understand who the product is built for and whether our pricing and sales process are aligned with that audience.

If a product is designed to help a particular type of business drive revenue or cut costs, shouldn’t we think carefully about whether we’re reaching businesses that actually have that problem and are ready to invest in solving it?

And if customers aren’t getting value from the product, shouldn’t we understand why before assuming that the product itself needs to change?

I’m still thinking about this

I don’t think every product struggling with adoption or retention has a pricing problem. Sometimes the product is difficult to use. Sometimes its value isn’t clear. Sometimes it doesn’t solve the problem well enough.

And sometimes, perhaps, we’re attracting customers who don’t need it enough to justify the investment or who aren’t prepared for what using it properly requires.

These are different problems, and they probably shouldn’t all lead to the same solution.

What interests me is how easily we can focus on improving the product itself without questioning whether we’re reaching the right customers in the first place.

We can simplify the interface, improve onboarding, add features, and lower the price. But if we’re still attracting people who don’t need the product enough to use it properly, are we actually solving the problem?

I’m not sure there’s one answer, and I think the answer depends on the product, the customer, and the value being offered.

But I keep coming back to that original sentence: a product is made to either drive revenue or cut costs.

If that’s what makes a product valuable to a business, perhaps we should be just as careful about understanding who can benefit from that value as we are about making the product itself better.

Maybe the question isn’t always how to get more customers.

Maybe it’s whether we’re attracting the customers our product was actually built for.

Is the Product Failing, or Is It Reaching the Wrong Customers? was originally published in Women in Technology on Medium, where people are continuing the conversation by highlighting and responding to this story.

You’ve secured your SharePoint site. Permissions are in place. Access is controlled. Sensitive information is exactly where it should be. And then someone downloads a document and emails it to their personal email account. Suddenly, those SharePoint permissions aren’t the whole story. This is one of those scenarios that sounds simple until you start thinking about how information actually moves around an organisation.

In this article, I’m going to look at a few simple guardrails we can put around this specific scenario using SharePoint and Microsoft Purview Data Loss Prevention (DLP). Of course, DLP can do much more than what I’ll cover here, but sometimes starting with one very real problem is the easiest way to understand what these controls can do.

This question came up yesterday at the WiTCH Women in Cyber event, and it got me thinking. It’s a simple scenario, but probably one that quite a few organisations face, so I decided to put together a practical guide.

Scenario 1: What about sharing directly from SharePoint?

Email isn’t the only route we need to think about. Imagine the same document is sitting safely inside SharePoint. Instead of downloading it and attaching it to an email, somebody selects Share and enters their personal Gmail address. That’s where SharePoint’s external sharing controls become another important part of the picture. SharePoint and OneDrive let administrators use Limit external sharing by domain to control which external domains content can be shared with. The restriction can be configured using either an allow list or a block list.

Option 1: Block specific domains

A block list essentially says:

“External sharing is allowed, except to these domains.”

For example, an organisation could decide that particular domains shouldn’t be legitimate destinations for SharePoint content.

Option 2: Allow only approved domains

This flips the model around.

Instead of asking:

“Which domains don’t we trust?”

we ask:

“Which external organisations do we actually need to collaborate with?”

You can also apply controls to an individual SharePoint site

The domain restriction doesn’t have to be approached purely as an organisation-wide control. SharePoint also supports domain restrictions at the site level. Organisation-level settings take precedence, and if the organisation already uses an allow list, a site’s allow list has to be a subset of it.

For a particular sensitive site, the settings to configure in the SharePoint admin centre is:

• Open Active sites.
• Select the SharePoint site you want to configure.
• Select Settings, then More sharing settings under External sharing.
• Under Advanced settings for external sharing, enable Limit external sharing by domain.
• Choose either Allow only specific domains or Block specific domains.
• Add the domains you want to allow or block.

Scenario 2: But what if someone downloads the file and tries to send it in Outlook?

SharePoint domain restrictions help us control sharing directly from SharePoint. But once someone downloads the document, we need to think about another route the information could take. If that file is attached to an Outlook email, Microsoft Purview DLP gives us another opportunity to evaluate the content and its destination.

In this example, I’ve created a simple DLP policy to demonstrate how this could work.

The policy looks for three types of financial information: Credit Card Numbers, EU Debit Card Numbers and SWIFT Codes. I’ve configured confidence levels and occurrence requirements so that the policy isn’t triggered simply because a document contains any random number that happens to resemble financial information.

I’ve then added a second condition: the content must be shared with people outside my organisation. This is important because I’m not trying to prevent normal collaboration between colleagues. I’m interested in the point where sensitive information is about to leave the organisation.

Finally, I’ve added a recipient domain condition. This allows the DLP rule to consider where the email is being sent, so the organisation can apply different controls depending on the destination. (You can add any domains in here that you want)

In simple terms, the policy is asking:

Does this content contain the sensitive financial information I care about, is it being shared externally, and does the recipient meet my domain criteria?

If those conditions are met, the DLP rule can then apply the action I’ve configured, such as warning the user, requesting justification or blocking the activity.

Important: In this article, I use Gmail and Outlook.com as examples of personal email destinations. An external recipient isn’t necessarily a personal account, so DLP policies should be designed around your organisation’s approved and unapproved sharing destinations rather than treating all external recipients in the same way.

Just a reminder; I always test policies from DLP first in simulation mode or with a small number of test accounts to ensure everything is working as expected before rolling anything out.

If you like this article, you can find me on LinkedIn at: Jodie Lawrance on LinkedIn: www.linkedin.com/in/creativejodielawrance

The key takeaway for me is that we shouldn’t just think about protecting where the information lives. We also need to think about the different routes that information can take once someone starts sharing it.

The Data Was Secure in SharePoint… Until Someone Shared Or Emailed It. was originally published in Women in Technology on Medium, where people are continuing the conversation by highlighting and responding to this story.

Imagine receiving a call from someone who sounds exactly like your bank manager. They know your name, the bank you use, and even the details of a recent transaction. The caller warns you about suspicious activity and asks you to verify your account before your money disappears.

Everything sounds convincing. You follow the instructions, share a verification code, and end the call feeling relieved.

A few minutes later, you discover that your money is gone.

This is the uncomfortable reality of financial fraud today. Criminals no longer need to break into a bank or steal a physical card to access someone’s money. With artificial intelligence (AI), they can create convincing messages, imitate voices, manipulate identities, and target victims on a much larger scale.

Banks are fighting back with the same technology. AI systems can analyse millions of transactions, recognise unusual behaviour, and flag suspicious payments in seconds.

But this raises an important question: If banks are becoming smarter, why is financial fraud still such a serious problem?

The answer lies in a simple truth. Technology is changing the way fraud happens, but it is also changing how criminals operate. Staying ahead requires more than sophisticated software. It requires good judgment, reliable security systems, and a better understanding of human behaviour.

How AI Is Changing the Face of Financial Fraud

Financial fraud has always existed, from forged cheques to stolen credit cards. What has changed is the speed, scale, and sophistication of modern attacks.

In the past, a scammer might send poorly written emails to thousands of people and hope a few would respond. Today, AI tools can produce convincing messages in different languages, personalise them using publicly available information, and help criminals reach potential victims more effectively.

1. Deepfakes and Identity Impersonation

One of the most concerning developments is the use of AI-generated voices, images, and videos.

A fraudster can imitate a business executive, a family member, or a trusted financial professional. In a corporate setting, an employee might receive what appears to be a genuine video call from a senior executive requesting an urgent transfer.

The request may look completely legitimate. The voice sounds familiar, the face appears real, and the situation seems believable.

This makes traditional trust-based verification less reliable. Recognising someone’s voice or seeing their face on a screen is no longer enough to establish their identity.

Banks and businesses need stronger verification procedures, particularly for high-value transfers and changes to account details.

2. Phishing That Feels Personal

Most people have learned to be cautious about suspicious emails. However, AI is making phishing messages harder to recognise.

Criminals can use AI to write messages that sound professional, imitate the communication style of real organisations, and create believable explanations for why someone needs to click a link or provide sensitive information.

A message claiming that a bank account will be suspended may create just enough urgency to make a customer act without thinking.

The danger is not simply that these messages look more convincing. It is that they can be produced and adapted quickly, allowing fraudsters to test different approaches and target people more precisely.

3. Synthetic Identities and Account Fraud

Another growing challenge is synthetic identity fraud, in which criminals combine real and fabricated information to create an identity that appears genuine.

For example, a fraudster might use a legitimate person’s details alongside a false name, address, or other invented information to build a financial profile.

AI can help criminals generate convincing documents, alter images, and identify weaknesses in digital onboarding processes.

For banks offering online account opening, digital wallets, and instant payments, this creates a difficult balancing act: customers expect a quick and convenient experience, but identity checks must remain reliable.

How Banks Are Using AI to Fight Back

While criminals are exploiting new technology, banks are not standing still. Many financial institutions already use machine learning, behavioural analytics, and automated monitoring to detect suspicious activity.

The advantage of AI is its ability to examine patterns that would be difficult for a human team to identify manually.

1. Detecting Suspicious Transactions in Real Time

Traditional fraud detection often relies on predefined rules. A transaction above a certain amount, for example, might trigger an alert.

Rules are useful, but they have limitations. A legitimate customer might make an unusually large payment, while a criminal could deliberately keep transactions below a threshold.

AI can examine several factors together, including transaction size, payment frequency, location, device information, account history, and the recipient’s behaviour.

Suppose a customer normally makes small domestic payments but suddenly initiates several transfers to unfamiliar accounts within minutes. The system can identify this pattern as unusual and request additional verification.

The goal is not to block every unfamiliar transaction. It is to recognise when several warning signs appear together.

2. Understanding Customer Behaviour

Every customer develops certain financial habits. Some regularly pay bills at the end of the month, while others make frequent small purchases or transfer money between their own accounts.

AI-powered behavioural analysis can learn these patterns and identify activity that differs significantly from normal behaviour.

For instance, a transaction might be suspicious not because of its amount, but because it follows an unusual sequence of account access, device changes, and payment requests.

This approach can help banks identify fraud earlier, sometimes before a customer realises anything is wrong.

However, behavioural patterns should be treated as signals rather than proof. People travel, change jobs, buy new devices, and experience unexpected expenses. Effective systems must account for these ordinary changes.

3. Strengthening Digital Identity Verification

Digital banking has made opening accounts and accessing financial services easier than ever. Customers can complete many processes without visiting a branch.

AI can support identity verification by analysing identity documents, detecting possible alterations, checking facial consistency, and identifying unusual onboarding patterns.

Combined with document verification, liveness detection, and other security controls, these systems can make it more difficult for criminals to use stolen or fabricated identities.

Still, no single verification method is foolproof. Banks need layered checks that can adapt to the level of risk involved.

A low-risk activity may require little friction, while a suspicious account application or high-value transfer may justify additional verification.

4. Connecting the Dots Across Financial Activity

Fraud rarely follows one predictable pattern. Criminals may use several accounts, devices, payment channels, or financial institutions to move money.

AI can help investigators identify connections between seemingly unrelated events.

For example, multiple accounts may share device characteristics, receive funds from the same suspicious source, or transfer money through a similar sequence of transactions.

When these signals are analysed together, a bank may uncover a wider fraud network rather than treating each transaction as an isolated incident.

This is especially valuable in a financial environment where payments can move across platforms and borders within seconds.

Why Banks Still Struggle to Stay Ahead

If AI can process enormous amounts of data and identify suspicious patterns, why can’t banks eliminate fraud altogether?

Because detecting fraud is not simply a technical problem. It is also a problem of uncertainty, speed, and human behaviour.

1. Criminals Adapt Quickly

Fraudsters do not need to defeat every security system. They only need to find a weakness they can exploit.

When banks introduce new detection methods, criminals may change transaction patterns, use different devices, or focus on social engineering instead of directly attacking banking systems.

This creates an ongoing cycle of adaptation. Banks improve their controls, criminals change their tactics, and financial institutions must adjust again.

AI gives both sides new capabilities, so its presence alone does not guarantee an advantage for defenders.

2. False Alarms Can Frustrate Customers

Imagine being on holiday and having your card declined because your bank detects an unfamiliar location. Or imagine a legitimate transfer being delayed while you wait for additional verification.

These situations can be frustrating, especially when customers need access to their money urgently.

If a bank flags too many genuine transactions, customers may lose confidence in its security systems. If it flags too few, suspicious activity may go unnoticed.

Finding the right balance is one of the hardest parts of fraud prevention.

Banks must consider not only how accurately a model identifies suspicious activity, but also how it affects customers in everyday situations.

3. Poor Data Can Lead to Poor Decisions

AI systems depend on the information they receive. Incomplete transaction records, inconsistent customer data, and outdated fraud patterns can weaken their performance.

A model trained on historical examples may also struggle when criminals introduce a completely new technique.

Financial institutions need reliable data, regular model testing, and human oversight to identify weaknesses before they cause significant harm.

AI should support better decisions, not become an unquestioned authority.

4. The Human Factor Remains Difficult to Predict

Some of the most damaging scams do not involve breaking through a bank’s technical defences.

Instead, criminals persuade customers to approve payments themselves.

A victim may believe they are transferring money to a safe account, paying a legitimate supplier, or helping a relative in an emergency. From the bank’s perspective, the transaction may appear authorised.

This is where fraud prevention becomes more complicated. A technically valid payment can still be the result of deception.

Banks need to consider the circumstances surrounding a transaction, provide timely warnings, and make it easier for customers to question unusual requests before sending money.

The Importance of Human Judgment in an AI-Driven Banking System

There is a temptation to treat AI as a complete solution. After all, machines can analyse data faster than people and operate continuously without fatigue.

But speed does not automatically produce good judgment.

A fraud detection system may identify a suspicious pattern, yet a trained investigator may need to understand the wider context. A customer might be making an unusual payment because of a medical emergency, a property purchase, or a legitimate business transaction.

Human oversight helps distinguish between genuine risk and unusual but reasonable behaviour.

This does not mean every alert should be reviewed manually. That would be inefficient and impractical for large banks. Instead, institutions should combine automation with carefully designed escalation processes.

AI can handle routine monitoring, prioritise alerts, and identify relationships between transactions. Human specialists can focus on complicated cases, disputed decisions, and emerging fraud patterns.

The strongest approach is not AI replacing people. It is AI helping people make faster, better-informed decisions.

What Banks Must Do to Stay One Step Ahead

There is no single tool that can prevent every type of financial crime. Banks need a broader strategy that combines technology, operational discipline, and customer awareness.

Invest in Adaptive Fraud Detection

Fraud models must be monitored and updated as criminal tactics change. Banks should test their systems against emerging attack scenarios rather than relying entirely on historical data.

Strengthen Security Beyond Passwords

Multi-factor authentication, device intelligence, transaction confirmation, and risk-based verification can provide additional protection. Sensitive actions should receive stronger checks, especially when account details or payment destinations change.

Make Fraud Prevention Easier for Customers

Security measures should be clear and practical. Customers need straightforward ways to verify suspicious requests, report scams, freeze compromised cards, and contact their bank quickly.

Warnings are most effective when they explain the specific risk rather than simply telling people to be careful.

Improve Collaboration Across the Financial Industry

Fraud often crosses institutional and national boundaries. Where permitted by law, banks, payment providers, regulators, and law enforcement agencies should share relevant threat intelligence and develop faster ways to respond to emerging attacks.

Better coordination can help institutions recognise patterns that would remain invisible when each organisation examines its own data in isolation.

Build Responsible AI Systems

Fraud prevention models should be tested for accuracy, bias, privacy risks, and unintended consequences. Banks should understand why their systems generate alerts and ensure customers have appropriate ways to challenge significant decisions.

A system that catches more fraud but unfairly blocks legitimate customers is not a complete success.

What Customers Can Do to Protect Themselves

Banks carry significant responsibility for securing financial systems, but customers also have a role to play.

A few practical habits can reduce exposure to common scams:

• Never share passwords, PINs, or one-time verification codes with someone who contacts you unexpectedly.
• Verify urgent payment requests through a separate, trusted communication channel.
• Avoid clicking links in unexpected messages claiming to come from your bank.
• Enable transaction notifications and review account activity regularly.
• Contact your bank immediately if you notice an unfamiliar transaction or suspect that your credentials have been compromised.

Most importantly, pause when a message creates intense urgency. Scammers often try to make people act before they have time to think.

Even sophisticated technology cannot fully protect someone who is deliberately pressured into authorising a payment without understanding what they are doing.

The Future of AI and Financial Fraud

The relationship between AI and financial fraud will continue to evolve. Banks will develop better ways to identify suspicious transactions, verify identities, and investigate complex networks. Criminals will continue looking for opportunities to exploit new technologies and human weaknesses.

The next stage of fraud prevention will likely involve more integrated systems that connect transaction monitoring, identity verification, behavioural analysis, and real-time risk assessment.

But progress should not be measured only by the number of suspicious transactions a bank detects. It should also be measured by how quickly victims receive help, how often legitimate customers experience unnecessary disruption, and how effectively financial institutions prevent losses before they happen.

Trust remains the foundation of banking. Customers expect their money to be safe, their information to be protected, and their access to financial services to remain reliable.

AI can help banks protect that trust, but it cannot maintain it on its own.

Final Thoughts: Staying Ahead Is a Continuous Process

Can banks stay one step ahead of financial fraud?

The realistic answer is that they can improve their chances, but they cannot assume the battle will ever be completely won.

AI gives financial institutions powerful tools to recognise patterns, investigate suspicious activity, and respond at a speed that would be impossible through manual processes alone. At the same time, fraudsters are using technology to make their attacks more convincing and easier to scale.

The difference will come from how banks use these capabilities.

Institutions that combine adaptive technology, strong verification, responsible data practices, skilled investigators, and customer education will be better positioned to respond to emerging threats.

Ultimately, the goal is not to build a banking system that never encounters fraud. It is to build one that can recognise danger sooner, respond more effectively, and recover quickly when something goes wrong.

In a world where criminals are becoming smarter, the banks most likely to succeed will be those that never stop learning.

AI vs. Financial Fraud: Can Banks Stay One Step Ahead? was originally published in Women in Technology on Medium, where people are continuing the conversation by highlighting and responding to this story.