Online fraud used to feel like “someone else’s problem” for finance teams. Security owned the logs, product owned the user behavior, and finance just cleaned up the mess as chargebacks, refunds, and a painful fraud line on the P&L.
Look a little closer at those losses, though, and you start to see familiar patterns. High-ticket orders from unfamiliar locations. New devices are making big purchases minutes after account creation. VPN or proxy traffic on transactions that later turned into disputes. Most of the clues were already visible in your IP and geolocation data.
The problem isn’t a lack of signal. It’s that the signal rarely makes it into the places where decisions about forecasting, provisioning, and risk appetite actually happen. If you want to move from “fraud is just a cost of doing business” to “we can show exactly what we saved,” you need to pull IP intelligence out of security silos and into the finance workflow.
Why card-not-present fraud keeps hitting your P&L
For e-commerce, fintech, and subscription companies, card-not-present (CNP) fraud is where IP and geolocation really matter. You’re approving transactions without a physical card, so you rely on patterns: device, behavior, and location.
That risk is not theoretical. Recent research from the Federal Reserve Bank of Kansas City shows that CNP fraud rates on debit cards in the U.S. have continued to rise even as chip technology has reduced some types of card-present fraud. Merchants shifted more volume online; fraudsters followed.
Across Europe, a European Central Bank review of card fraud found that CNP transactions made up the vast majority of total card fraud value—over 80% in 2021. When more payments move to remote channels, the attack surface expands.
For a finance team, all of this shows up as:
- Direct losses from fraudulent transactions
- Chargeback fees and network penalties
- Operational cost of handling disputes
- Knock-on effects on processing fees and risk ratings
And the true cost is usually higher than it first appears. Aggregated industry data from chargeback statistics compiled by Chargebacks911 suggests that every dollar in fraud can cost merchants multiple dollars once you factor in fees, lost goods, and overhead.
When you add those multipliers, even modest reductions in fraud loss move the needle on margin. That’s why the early hints inside an IP address matter so much.
If you work with IP data every day, you already know how much a single address can reveal. IPLocation’s guide on what someone can do with your IP address breaks down how IPs expose approximate location, ISP, and potential attack paths. For finance, the same signals can highlight where losses are more likely to originate and which customer segments need tighter controls.
From IP alerts to finance fields: building the bridge
Turning IP alerts into fraud savings isn’t about collecting more logs. It’s about choosing a few key attributes and making sure they follow the money.
1. Decide which IP attributes travel with each transaction
Fraud tools, gateways, and CDPs all record IP details slightly differently. From a finance perspective, you want a minimal set of fields that can live alongside order and invoice data, for example:
transaction_ipandlogin_ipip_country,ip_region,ip_cityip_risk_flag(VPN/proxy/Tor, data-center IP, known bad range)ip_velocity_score(how many cards or accounts have used this IP recently)
2. Attach the IP context to the objects that finance actually sees
Security teams think in terms of sessions and devices. Finance lives in invoices, payouts, and journal entries. You get value from IP data only when it’s tied to:
- Payment transactions and authorizations
- Subscription renewals and invoices
- Refunds, disputes, and chargeback events
If a transaction is later written off as fraud, you want the record to show that it came from a new device, a high-risk country, or a data-center IP with a history of abuse. IPLocation’s guide on how to prevent IP address abuse goes into the technical defenses; your job in finance is to make sure those same signals sit next to the transaction IDs your accounting team uses.
3. Give finance a home for IP-rich data
Most finance teams won’t query a SIEM or dig through raw JSON. They need IP-enriched data inside tools that are designed for budgeting, forecasting, and reporting. One practical pattern is to push transactions into an AI-driven finance layer—an environment such as the AI finance platform omniga.ai, which connects operational data with cash-flow, revenue, and margin metrics.
When IP risk flags and geolocation fields are available there, finance leaders can slice fraud losses by region, IP type, or risk score without changing how they work. “Suspicious login from X country” becomes “this risk segment drove Y dollars in fraud last quarter.”
Designing IP-driven rules that finance can measure
Once IP data travels with each transaction, you can start turning alerts into controls with measurable outcomes.
1. Start with where you’re already bleeding
Begin with historical fraud-attributed losses from the last 6–12 months. Group them by simple IP-related characteristics:
- Source country or region
- Proxy/VPN vs. residential
- First-seen vs. long-standing IPs
- Distance between billing address and IP location
Patterns usually appear quickly: certain high-risk regions, repeated use of the same data-center ranges, or spikes when IP distance crosses a threshold. Those patterns suggest candidate rules: step-up authentication, extra verification, manual review, or even automatic declines.
2. Borrow from security’s playbook—minus the noise
On the security side, IP data is already part of machine-learning models that weigh behavior, device, and location. IPLocation’s article on AI and IP addresses for security and privacy explains how those models can balance threat detection with user experience and privacy concerns.
Finance doesn’t need every detail of the model. What helps is a small set of interpretable, stable signals: a “high-risk IP score” threshold, a flag for known anonymization services, or a rule like “new device + VPN + high order value.” When those signals are easy to explain, they’re easy to defend in risk committees and audit conversations.
3. Define a fraud cost bucket that uses the same IDs
Work with accounting to ensure fraud-related losses are consistently coded. That might include:
- Chargebacks coded as fraud
- Manual write-offs after confirmed fraud cases
- Direct dispute fees
Crucially, every one of those entries should carry the same transaction or order ID that your IP-enriched data uses. That’s what lets you simulate questions like:
- “If we had challenged every transaction from this group of IP ranges, how much fraud loss would we have prevented?”
- “What’s the difference in loss rates between residential IPs and traffic from a specific hosting provider’s ranges?”
At that point, IP rules stop being abstract and start looking like levers with a clear financial impact.
Turning geolocation into a recurring “fraud savings” metric
Once you’ve connected the dots, you can start measuring IP-based controls the way you measure marketing or sales initiatives.
1. Run retro simulations before you deploy rules
Take historical transaction data, apply a proposed IP-based rule, and calculate:
- Fraud losses that would have been blocked
- Legitimate revenue that would have been challenged or declined
- Additional manual review cost
Feed that through the multipliers you already know from your chargeback economics. Given how costly each dollar of fraud can be, even a modest reduction in volume can justify investments in better IP data, tooling, or review capacity.
2. Track prevented loss alongside revenue and margin
After a rule goes live, standardize a straightforward metric such as:
Estimated fraud savings = historical fraud loss rate × value of transactions impacted by the rule
It won’t be perfect, but if your assumptions are conservative and consistent, you’ll see trends. Over time, “fraud savings” can sit next to revenue, refunds, and CAC in your regular reporting pack.
3. Use IP data to tell a story that stakeholders actually understand
Fraud risk is increasingly treated as a structural feature of digital payments rather than a temporary anomaly. When central banks and regulators highlight the dominance of CNP fraud, they’re effectively telling merchants: “You need layered defenses, and you need to know if they work.”
If you can say, “We used IP and geolocation signals to reduce fraud losses from X basis points of revenue to Y, with an estimated Z dollars saved,” you’re no longer just describing good security hygiene—you’re explaining risk-adjusted returns on invested capital.
The takeaway for finance teams
For years, “suspicious IP” alerts have lived in security dashboards while finance teams dealt with the fallout months later. That separation is no longer sustainable if a growing share of your revenue is digital and global.
When you standardize a handful of IP attributes, attach them to transactions, and surface them inside your finance tools, you turn geolocation from background noise into a lever you can pull on purpose. You may not stop every fraudulent transaction, but you’ll finally be able to show, in real numbers, how IP-driven controls translate into fewer chargebacks, lower write-offs, and cleaner books—and that’s a story every CFO is ready to tell.
Featured Image by Freepik.
Share this post
Leave a comment
All comments are moderated. Spammy and bot submitted comments are deleted. Please submit the comments that are helpful to others, and we'll approve your comments. A comment that includes outbound link will only be approved if the content is relevant to the topic, and has some value to our readers.
Comments (0)
No comment