How Finance Teams Strengthen Fraud Controls Without Slowing the Business

1. Don't treat all transactions equally. In most companies, the rules for fraud prevention are equally applied to all transactions. They may use a rule such as 3D Secure for all transactions or manual approval above a certain amount. The result is that conversions fall and finance is bogged down in dealing with transactions that go wrong. In reality, fraud occurs in uneven and unpredictable ways. For us at TradingFXVPS, the majority of bad payments come from a few areas: new accounts, payments made with prepaid cards or newly issued cards, card country not matching IP, and a pattern of attempts made within a short space of time. Add friction in these areas and nowhere else. A repeat customer renewing a server again and again should never have to go through extra verification steps.

2. To provision a server (to create it) and to process a payment for an order now are two separate steps. Before, a payment for an order would provision a server immediately upon successful processing of the payment for the order. In that type of system, a stolen card can be used to provision a server in 60 seconds (abuse, then chargeback several weeks later). In our current system of risk-based provisioning, every order now gets a risk score at the time of checkout. Low risk orders are provisioned immediately. Medium risk orders are processed for payment via 3D Secure, and high risk orders are held for a short manual verification (approximately 15 minutes or less). About 90% of orders in our system fall into the low or medium risk categories and thus never even know that any special verification was conducted. Outgoing payments have one rule: Verify any change to vendor bank account information by telephone to a number already in our system's database for that customer. (This one verifies invoice fraud for most cases and costs only two minutes.)

We introduced a daily exception digest instead of relying only on individual alerts. It groups unusual payment activity into patterns, such as changed account details, invoices submitted near cut-off, or repeated payments just below approval limits. This helps the finance team see the bigger picture. A single payment may seem reasonable alone but raise concerns when linked to similar requests.

We review the digest at a set time each day to avoid disrupting normal payment work unless a clear pattern emerges. This process also improves team discussions by separating genuine urgency from behavior that needs attention. Fraud controls work better when they connect related events rather than flagging isolated transactions. This helps protect cash while keeping legitimate work moving.

Last year, our payment volume grew 60%, and a single person could both add a vendor and release money to them. A review showed this was our biggest weak spot. Adding approvals to every payment would have slowed everything, so I changed just one thing: no one could edit a vendor's bank details alone. A second person had to approve the change, and the new details could not be used for 48 hours. Routine payments to unchanged vendors under Rs 1 lakh stayed automatic. This covered about 88% of our 1,900 monthly payments. In the next six months, we blocked 3 suspicious bank-detail requests, worth Rs 6.2 lakh, while average payment time rose by only 0.3 days. Vendor complaints stayed flat. The one lesson I would pass on is to place your strongest check where fraud actually enters, which is usually a change in details, and leave ordinary payments alone.

What protected us the most without slowing anything down was separating who can approve a payment from who can change payment details. Those used to be the same person and that's mostly how scams happen.

Nowadays fraud isn't a fake invoice, it's someone changing the bank details on a real one. A legitimate vendor you've paid for years sends a routine update to their account number and because it's an existing relationship nobody is cautious so I split the two functions. One person can approve payments but changing any banking detail on file requires a second person to verify it through a channel we already have, not the email or message that requested the change. Approvals still move at normal speed because the flow is untouched.

Most people slow down everything equally which just trains employees to see security as a tax and rush through it. You don't need friction everywhere, you just need it on the one step the fraud depends on.

The control change that gave the most protection without slowing anything down was separating who records work from who approves payment. In my NBN telecom contracting company, about 30 staff were paid largely on performance, so pay depended on work captured in the field in our own software. The crew entered the job, and an office admin reviewed and signed it off before any money moved. Nobody could both create and approve a payment, and that one split stopped errors and padded claims before they cost us, while crews still got paid on schedule.

I now set up the same principle for the owners I coach, adapted for payment fraud. Any change to a supplier's bank details, any first payment to a new payee, and anything marked urgent by email needs a callback to a phone number already on file and a second approver. Everything routine flows as normal, which keeps the team fast. Daily bank alerts on outgoing transfers mean anything odd is caught the same day instead of at month end. The lesson is to put the friction only on the few moments fraudsters target, and make sure no single person controls a payment from start to finish.

I strengthened financial controls by adding AI-assisted fraud detection to our website payments so suspicious transactions are flagged for review. In one case the system flagged a bulk order where the credit card country and name did not match the order and stopped the transaction. We contacted the customer to explain our payment policy and resolve the issue. While the change initially caused some customer satisfaction problems, the automated flagging has protected the company from many financial losses while reducing routine manual checks.

We run adaptive MFA and device fingerprinting on high-risk billing only, like when new payment details get added or someone logs in from an unfamiliar location. Fraud attempts dropped, and routine renewals stayed fast. Targeted controls beat blanket rules if you're worried about slowing things down.

I run Netsurit, an MSP supporting 300+ client organizations, so I see payment fraud less as a "finance only" issue and more as an identity, email, and process issue.

The biggest control change is step-up control at the point of risk: vendor banking changes, unusual payment requests, and new payees get stronger verification, while normal approved workflows keep moving. That preserves speed because you are not adding friction to every transaction.

Practically, that means MFA, least-privilege access, account takeover monitoring, anti-phishing protection, and staff training around payment-related email threats. We use the same thinking in our email security and cybersecurity work: block the deceptive request before it becomes a finance problem.

During growth and acquisitions like Vital I/O, iTeam, Avaunt Technologies, and US Computer Connection, the useful lesson was to standardize controls without burying teams in process. Regular audits, clear access rules, and transparent reporting gave better protection while letting people do their day-to-day work.

Set your fraud controls from your own payment history, so they land where the fraud is, and almost nowhere your real customers are.

We run the website for a Virginia nonprofit that takes invoice payments through an online form. One morning that form was hit by card testers, people running stolen card numbers in small amounts to see which ones work. It took 183 submissions in under two hours, each from a different US residential IP address, and 78 small charges went through. The form already had reCAPTCHA and a honeypot field. Both were bypassed.

The tempting response is more friction for everyone: a harder challenge, extra verification steps, manual review. Every one of those slows down the people paying legitimate invoices, and the attack had already shown that a challenge alone wouldn't stop it.

Instead, we added two controls that real payers almost never touch. The first is a limit on attempts: the form shuts itself off after 5 tries in 15 minutes or 15 in 24 hours. Someone paying an invoice doesn't submit five payments in a quarter of an hour. A testing script does. The second is a $15 minimum payment. We didn't guess at that number. We pulled 12 months of the organization's actual payments, and only 3 of 176 were under $15, so the minimum affects a handful of real payers while removing the cheap test charges these attacks depend on.

I'd treat the minimum as a second layer, not the fix. Attackers adapt, and the last attempt that day was for exactly $15. The attempt limit does the heavy lifting.

The broader lesson for any finance team: before adding a control, look at what your legitimate activity actually looks like. A threshold built from your own data protects you and stays out of the way.

At Acquire.com, we're running verified escrow as our standard for larger transactions. We've instituted further buyer verification procedures for high-value purchases, and observed that fraudulent transactions dropped. Small, pre-vetted deals have experienced no slowdown-they close as quickly as always. The industry-standard ways of making buying safer without harming the buyer experience simply weren't up to the task.

We kept catching fraud too late when all we did was run everything as a single zone. Separating out our billing, treasury, and payments APIs into separate zones solved this. We added device fingerprinting and MFA for high-risk zones and opportunities to reduce signal, so regular work is quick while we catch truly suspicious behavior. Don't slow everything down to account for every possible risk. Secure what matters.

We added AI agents at Wonderchat to auto-check invoices and contracts. Cut manual review way down without slowing payments. The hard part was getting it to flag actual problems instead of false positives. Took a few tries to dial it in. But once we did, approvals moved faster and payment errors dropped. If I did it again, I'd start with a small dataset to see what the AI actually catches before rolling out wide.

A while back, a supplier invoice arrived with new bank details and a polite note saying the old account was closed. It looked genuine, and our team nearly paid it. A quick phone call to the number we already had on file showed the supplier knew nothing about it. That near miss made us rethink our controls. We did not add more approvals for everything, since that would have slowed every payment. Instead, we added one rule: any change to bank details must be confirmed by a call to a saved number, and only then does payment go ahead. Everything else kept its normal speed. In the following year, we caught 7 suspicious requests, and 93% of regular payments were still processed in a single day. The lesson is to protect the moments where fraudsters actually strike, not every step. Find the one point where money can be redirected, and put a single, simple check there.

The control change that paid off for me was a callback rule on one event: any change to payout details. New bank account, new payee, new routing number. That change gets confirmed on a phone number already on file before a dollar moves. Everything else on the payment run goes through the same day it always did.

I like that rule because payment fraud is getting harder to spot by looking at the payment itself. The invoice looks right and the email sounds right. The tell is that something about where the money goes just changed. So I put the friction on the change and left the recurring payments alone.

In my business, cleaning teams work inside hundreds of homes and we pay a lot of small recurring amounts to the same people. A callback on a payee change takes a few minutes and happens rarely. Re-approving every routine payout would eat hours every week and teach everyone to click through it.

The other piece is that the person who requests a payee change can't be the person who approves it. Two people, two steps, only on the rare change. Nobody waits on the weekly run.

If you're deciding where to add a control, find the one moment a stranger would have to touch to steal from you, and start there.

The most efficient fraud control is often a short pause built into the highest-risk moments. I added a cooling-off period for first-time payment destinations and significant banking changes, allowing verification to occur before funds were released. The pause was brief, clearly defined, and limited to transactions where reversal would be difficult.

Legitimate activity remained efficient because established payment relationships were unaffected, and requestors knew exactly what documentation would clear the review. The policy also reduced pressure on employees who received urgent instructions from apparent leaders or vendors. A defined waiting period gives people permission to verify rather than react. In payment security, a modest delay at the right point can prevent a much larger disruption later.

We introduced a cooling-off period for changes to payment instructions rather than delaying all payments. This small operational change had a major effect because fraudulent requests often rely on immediate action. When banking details changed, the update could be recorded but not used until independent confirmation was complete. Meanwhile, previously approved details remained available for legitimate recurring payments.

This distinction helped prevent a changed email or compromised vendor account from immediately redirecting funds while keeping regular payments moving. It showed why controls should focus on the actions that carry the greatest risk. A brief pause before using a new payment destination is far less disruptive than delaying every invoice in the queue.

Speed is preserved when verification happens through a channel fraudsters cannot control. Email is useful for initiating a request, but it is a poor place to authenticate a financial instruction, especially when a familiar name and urgent narrative are easy to imitate.

Our control was a callback protocol for changes involving destination accounts or payment timing. The callback used contact details held in the supplier file, never information included in the request. The person making the call records the conversation and selects a reason code, allowing finance to spot pressure tactics or relationship records. Routine payments continue untouched, while the set of changes carrying disproportionate risk receives human confirmation.

Working with Patron Accounting LLP, I've found the sweet spot is controls that match how you actually operate. For a few SaaS clients, we set up transaction rules and tighter user permissions in Zoho Books. It caught odd payment patterns but didn't slow down normal recurring transactions. Start with controls that run in the background, then adjust based on what you actually see. Your workflow stays intact, but fraud risk drops.

There are times where payments must be made immediately prior to the event itself. A simple rule works well: no payment can be approved until we have a clear record of why the payment was made and WHO AUTHORIZED the payment. Link each vendor payment to an approved purchase order/contract. This will help with processing the routine payments as quickly as possible and provide a method of catching unauthorized charges.

If an invoice is received for additional rental equipment, it should correspond to an approved request prior to the release of funds. If no corresponding request exists, the charges need to be verified prior to fund disbursement. This allows the business to protect itself against unauthorized payments. The best payment controls provide greater DIFFICULTY for an unauthorized payment TO APPEAR AS A LEGITIMATE ONE.

The control change that worked best for the ecommerce brands we support was moving fraud review out of the checkout and into the window between payment and dispatch, and judging each order against the customer's actual history rather than a blanket rule.

Blanket rules are where legitimate business gets slowed down. If every order over a threshold gets held for manual review, your best repeat customers wait alongside the genuinely risky ones, and your team spends its day clearing false positives. The better approach is to let order history do the sorting. A customer with 40 past orders and a high lifetime value is a trusted relationship, and that order should flow straight through. An account with 10 payment disputes behind it is a different story, and that order gets held before the goods leave the warehouse. The good customer never notices any of it.

The second change is making the financial exposure visible every day, not at month end. Tracking authorisation success rate, chargebacks and the customer lifetime value at risk on one dashboard means a finance lead can see, on a given day, that something like $16,000 and 32 customers are exposed, and act on it that afternoon. Controls get tighter because the attention goes to the small number of orders that deserve it, not because everyone is checking everything.

We started running daily bank reconciliations with automated alerts, and fraud detection got way better without slowing down construction draws or title disbursements. One wire request looked off and got flagged in minutes. Stopped it cold. Would've been a real loss. The old manual reviews were too slow for that. If you're upgrading controls, let automation catch the weird stuff so your team can focus on routine transactions.

We tokenized all stored card data at VpnGenix and kept those tokens separate from VPN logs. If something got breached, attackers would find tokens, not real card numbers. Customers never noticed a difference in payment speed. Test tokenization alongside your current flow first. It took some tweaking, but once it clicked, we had real protection without slowing down anyone's checkout.

Chargebacks are often treated as a payment problem, but many originate in preventable fulfillment confusion. Delivery dates, carrier handoffs, partial shipments, damage claims, and unclear return expectations can all cause a legitimate customer to dispute a charge. I would strengthen controls by matching financial records more closely to operational milestones, including shipment confirmation, delivery evidence, and any post-delivery claim.

That reduces fraud exposure without creating checkout friction because the work happens behind the scenes. It also gives finance a stronger position when a dispute occurs, since the transaction history is complete and time-stamped. The best controls are not always barriers placed before a sale, they are reliable records that make the truth easier to establish afterward.

I run operations for a Las Vegas security company, so financial control for us starts with proving the work actually happened: posts staffed, patrols completed, incidents documented, and coverage matching the assignment.

The biggest control improvement was tying approvals to operational records we already produce. Digital shift reports, patrol logs, timestamps, incident notes, and client communication create a clean record before payroll or billing is reviewed.

For example, on property patrols, the approved post orders define the route, frequency, checkpoints, and reporting expectations. That keeps financial review from becoming a separate slowdown because the proof is captured during the officer's normal work.

My rule is: don't add friction at the payment stage if you can build verification into the workflow earlier. The stronger your daily documentation is, the less you have to rely on memory, email chains, or last-minute judgment when money is involved.

At Domus Lending, quarterly fraud drills changed everything. We focus on title and escrow since that's where attackers target us most. Software helps, but when people actually practice spotting fake wire instructions or shady emails, they catch things faster. Didn't disrupt our routine at all. If you haven't tried it, run drills on whatever your highest-risk process is. People freeze less when they've seen the scenario before.