Components of the cost of an anti-fraud system
The price of an anti-fraud system is not a constant. It depends on the vendor’s pricing model, the scale of the customer’s business, the chosen deployment format and many additional factors. To objectively assess the costs and compare them with the expected effect, it is important to understand what components make up the final amount.
The total cost of ownership of an anti-fraud system is formed from three key blocks: license payments, implementation expenses and operating costs. Each of them can vary within a wide range — from a few thousand to tens of millions of rubles — depending on the chosen solution and the specifics of the business.
License and payment model
Two fundamentally different pricing models have emerged in the anti-fraud solutions market: cloud (SaaS) and local (on-premise). The choice between them determines not only the initial investment but also the cost structure throughout the entire period of the system’s use.
| Category | Description |
|---|---|
| The cloud model: | Assumes payment based on actual consumption of the service. The customer pays for the volume of operations checked: the number of transactions, sessions or active users during the billing period. This approach does not require significant upfront investment and makes it possible to scale costs together with the growth of the business. The cost of a single check in the SaaS model is usually from a few kopecks to a few rubles — the exact figure depends on the complexity of the analysis and the volume of traffic. At small turnovers this is economically justified, but when processing millions of operations a month the total costs can turn out to be substantial. |
| Local deployment: | Involves purchasing a perpetual or fixed-term software license. A one-time license payment is usually measured in millions of rubles, but in the long term this model often turns out to be more advantageous for companies with a high volume of operations. At the same time, one must take into account the annual payments for technical support and updates — these usually amount to 15 to 25% of the license cost. |
In addition to the base model, the final price is affected by the system’s feature set. Additional modules — mobile application protection, advanced behavioral analytics, integration with external databases — increase the cost. Some vendors also offer customization of functionality to the customer’s specific requirements, which forms a separate expense item.
Integration and implementation
Purchasing a license is only the beginning. The real cost of launching an anti-fraud system depends substantially on the complexity of its integration with the customer’s existing infrastructure.
To minimize risks at the implementation stage, it is advisable to include detailed business requirements in the contract, fix the timeframes, and provide for a pilot period with the option to abandon the solution if the system does not demonstrate the stated effectiveness.
Operation and support
Putting the system into production does not mean the end of costs — rather, their transition to a regular mode.
Technical support and updates are needed to keep the system current. Fraud schemes are constantly evolving: according to the Bank of Russia, in 2024 banks’ anti-fraud systems rejected more than 72 million suspicious operations, while the number of attacks grew by 17% compared with the previous year. Without regular updating of rules and models, the effectiveness of protection inevitably declines.
The work of fraud analysts is another significant component of operating costs. Even the most advanced system cannot fully replace human expertise: complex and ambiguous cases require manual review, and detected incidents need investigation. Large companies form dedicated teams for these tasks; small ones may outsource the function to the provider of the anti-fraud solution.
Retraining machine learning models is necessary to adapt the system to changing user behavior and new fraud schemes. The frequency and labor intensity of this process depend on the architecture of the specific solution and the dynamics of change in the customer’s business.
Finally, it is worth taking hidden costs into account: employees’ time spent interacting with the system, handling false positives, and preparing reports for regulators. These costs are difficult to estimate in advance, but they inevitably arise and affect the total cost of ownership.
When planning a budget, it is important to consider not one-time payments but the total cost of ownership over a horizon of three to five years. This approach makes it possible to objectively compare different pricing models and choose a solution that is optimal not only in terms of initial investment but also in terms of long-term economic efficiency.
Calculating the ROI of an anti-fraud system
Implementing an anti-fraud solution is an investment, and it is reasonable to assess it by the same principles as any other business investment. The ROI (Return on Investment) figure makes it possible to understand how effectively the system recoups the funds invested, and helps justify the budget to management or investors.
The ROI formula
The classic formula for calculating return on investment applies to anti-fraud systems as well:
ROI = (Benefit from implementation − Total costs) / Total costs × 100%
The benefit from implementation is made up of two components: prevented fraud losses and reduced operating expenses. Total costs include the cost of the license, integration, maintenance and the labor of the employees involved in operating the system.
With a positive ROI, every ruble invested comes back with a gain. For example, an ROI of 300% means that for every ruble spent the company gets three rubles of savings or additional income. If the ROI is negative, the system is not yet paying off — but that is no reason to abandon it if it is a matter of protecting reputation or meeting regulatory requirements.
To assess long-term effectiveness, it is useful to calculate ROI over a period: one year, three years, five years. The longer the system is in operation, the lower the unit costs of integration and implementation, and therefore the higher the resulting profitability.
What to include in the calculation
The accuracy of the ROI calculation directly depends on the completeness of the factors taken into account. Here are the key parameters that should be included in the model.
| Category | Description |
|---|---|
| Prevented fraud losses: | This is the main benefit item. According to the Bank of Russia, in 2024 the anti-fraud solutions of credit institutions prevented thefts amounting to 13.5 trillion rubles — almost 2.5 times more than a year earlier. To calculate your own benefit, you need to assess the current level of fraud losses and the target reduction after implementing the system. Quality anti-fraud solutions reduce the fraud rate to 0.01% or less. |
| Reducing chargeback costs: | Every chargeback on a disputed transaction is not only a refund of the money but also fines from the payment systems and administrative costs of investigating the incident. Anti-fraud systems substantially reduce the number of chargebacks, which directly affects the financial result. |
| Savings on manual reviews: | Before implementing an automated solution, suspicious transactions are checked manually by analysts. The system reduces the review rate — the share of operations requiring manual processing. This frees up employees’ working time for other tasks or makes it possible to process a larger volume of transactions without expanding the staff. |
| Reducing the costs of two-factor authentication: | Every SMS confirmation or call from an operator costs money. An intelligent anti-fraud system applies additional authentication selectively — only to suspicious operations, rather than to all of them indiscriminately. |
| Impact on conversion: | Checks that are too strict cut off not only fraudsters but also honest customers. The False Positive Rate — the share of legitimate transactions mistakenly declined by the system — directly affects revenue. When assessing ROI, take into account how payment conversion will change after implementing a more accurate solution. |
| Reputational and regulatory factors: | They are hard to estimate in rubles, but they are significant. A data leak or a large-scale fraud incident damages customer trust and can lead to sanctions from regulators. Since July 2024, Russian banks have been obliged to reimburse customers for stolen funds if they did not suspend a transfer to a suspicious account from the Central Bank’s database. This makes quality anti-fraud not merely desirable but necessary. |
| Direct costs of the system: | These include license payments (or the subscription cost), integration and configuration expenses, technical maintenance, updates, and the salaries of the employees who work with the system. |
A worked example
Let’s consider a hypothetical example for a mid-sized company with online sales.
Input data:
- Monthly turnover: 50 million rubles
- Current level of fraud losses: 1.2% (600,000 rubles per month)
- Manual review costs: 150,000 rubles per month (the salaries of two analysts)
- Chargeback costs: 80,000 rubles per month
Anti-fraud system parameters:
- Subscription cost: 120,000 rubles per month
- One-time integration costs: 300,000 rubles
- Target fraud rate reduction: to 0.1%
Benefit calculation for the first year:
- Reduction in fraud losses: (1.2% − 0.1%) × 50 million × 12 = 6.6 million rubles
- Optimization of manual reviews (50% time savings): 150,000 × 0.5 × 12 = 900,000 rubles
- Reduction of chargeback costs by 70%: 80,000 × 0.7 × 12 = 672,000 rubles
- Total benefit: 8.17 million rubles
Costs for the first year:
- Subscription: 120,000 × 12 = 1.44 million rubles
- Integration: 300,000 rubles
- Total costs: 1.74 million rubles
ROI calculation: ROI = (8.17 − 1.74) / 1.74 × 100% = 369%
This means that every ruble invested in the anti-fraud system brings the company about 3.7 rubles of savings. The investment pays off in approximately 2.5 months.
It is important to understand that the example given is a reference point for building your own model. Real figures depend on the specifics of the business, the current level of fraud, the chosen solution and the quality of integration. Before implementation, it is worth requesting a pilot project or a trial period from the provider in order to gather actual data for a more accurate forecast.
The price of an anti-fraud system becomes clear when it is viewed as a total cost of ownership rather than a separate payment for a license or checks. Integration, configuration, infrastructure, support and the team’s regular labor costs are inevitably added to the final amount, so SaaS and on-premise should be compared over a horizon of several years, taking load and process requirements into account.
The ROI calculation helps translate this economics into a measurable effect: how much money is saved by prevented fraud losses and what share of costs can be reduced through automation without a loss in conversion. If the model includes all direct and hidden costs and takes into account the impact of accuracy on false positives, all that remains is to confirm the calculations in a pilot project or trial period and make a decision based on actual data.
