Chargebacks are the silent killer of sports betting profitability. For every dollar lost to a fraudulent chargeback, operators typically pay an additional $2.50-3.00 in fees, penalties, and operational costs. Worse, excessive chargeback ratios can trigger monitoring programs from card networks that restrict an operator's ability to accept payments entirely. At BetFlow, we built a machine learning system that reduces chargebacks by 65% while simultaneously lowering false decline rates by 23%. This article explains how.
The sports betting industry faces a unique fraud profile. Unlike e-commerce, where the primary concern is stolen card data used for unauthorized purchases, betting fraud encompasses a broader range of behaviors: bonus abuse, multi-accounting, friendly fraud (legitimate bettors disputing losses), and coordinated syndicate attacks. Each type requires different detection strategies, and a one-size-fits-all rules engine will either miss sophisticated fraud or block too many legitimate transactions.
The Chargeback Problem in Sports Betting
Sports betting operators face chargeback rates that are 3-5x higher than traditional e-commerce. The industry average hovers around 1.2-1.8%, compared to 0.3-0.5% for general online retail. This disparity exists for several reasons. The “digital goods” nature of betting means there is no physical delivery to prove the customer received value. The emotional nature of gambling losses motivates friendly fraud. And the high transaction volumes and rapid deposit cycles create opportunities for fraudsters to extract maximum value before detection.
The financial impact extends far beyond the chargebacks themselves. Visa's Dispute Monitoring Program (VDMP) flags merchants that exceed 0.9% chargeback ratios, and Mastercard's Excessive Chargeback Program (ECP) kicks in at 1.5%. Once an operator enters these programs, they face additional per-chargeback fees of $25-100, mandatory remediation plans, and the existential threat of having their merchant account terminated. For a sportsbook processing $100M monthly, a 1.5% chargeback rate translates to $1.5M in disputed volume, plus roughly $4.5M in associated costs. These numbers are unsustainable.
Traditional rules-based fraud detection systems struggle with this complexity. They rely on static thresholds: block transactions over $500, flag accounts with more than three deposits in an hour, reject cards from high-risk BIN ranges. These rules catch some fraud but generate enormous numbers of false declines, blocking legitimate bettors who happen to deposit frequently or make large wagers. Our analysis found that operators using rules-only systems were falsely declining 12-18% of legitimate transactions, each one a frustrated customer and lost revenue.
Our 200+ Signal Analysis Framework
Our fraud detection model ingests over 200 signals per transaction, organized into six categories. Device and browser signals (42 features) capture hardware fingerprints, browser characteristics, screen resolution, timezone, and language settings. Behavioral signals (38 features) track mouse movement patterns, typing cadence, time-on-page, and navigation sequences. Transaction history signals (51 features) analyze the account's deposit and withdrawal patterns over multiple time windows. Network signals (29 features) examine IP geolocation, VPN detection, proxy detection, and network characteristics. Account signals (24 features) consider account age, verification status, betting history, and customer service interactions. And cross-account signals (21 features) identify links between accounts through shared devices, payment methods, addresses, or behavioral patterns.
The cross-account signals deserve special attention because they are critical for detecting multi-accounting and syndicate activity. When a new account makes a deposit, we compute similarity scores against all accounts that have shared any attribute, including device fingerprint, IP address, payment card BIN, or even behavioral biometrics. If the similarity exceeds our threshold, the transaction is flagged for enhanced review. This layer alone catches 34% of the fraud that rules-based systems miss.
Key Insight: Behavioral biometrics, how a user moves their mouse, how fast they type, how they navigate between pages, turned out to be one of the most powerful fraud signals. Legitimate bettors have consistent behavioral patterns that are nearly impossible for fraudsters to replicate. Our behavioral scoring alone reduced false positives by 31% compared to device fingerprinting only.
Model Training and Architecture
Our fraud detection system uses an ensemble of three models, each optimized for a different fraud type. The first model targets third-party fraud (stolen credentials and payment methods) using a deep neural network trained on device and network signals. The second model targets friendly fraud (legitimate account holders disputing charges) using a gradient-boosted tree model trained primarily on transaction history and behavioral signals. The third model detects syndicate activity using a graph neural network that operates on the account relationship graph.
Training data is the most critical ingredient, and in fraud detection, it is inherently imbalanced. Fraudulent transactions typically represent less than 2% of total volume. We address this through a combination of techniques: SMOTE oversampling for the minority class, cost-sensitive learning that penalizes misclassification of fraud more heavily than misclassification of legitimate transactions, and temporal splitting that ensures the model is always evaluated on data that is newer than the training set. This last point is critical because fraud patterns evolve constantly, and a model that performs well on randomly split data may fail on truly future transactions.
We retrain models weekly using a rolling 180-day window of labeled data. The labeling pipeline combines three sources: confirmed chargebacks (the ground truth for third-party fraud), manual review outcomes from our fraud operations team, and automated labels from post-transaction analysis that identifies patterns only visible in retrospect. Each training cycle produces a candidate model that must beat the production model on a holdout set of the most recent two weeks before it is promoted.
Real-Time vs. Batch Scoring
Our fraud prevention system operates on two timescales. Real-time scoring happens during the deposit flow, adding less than 15ms to transaction processing time. Batch scoring runs continuously in the background, analyzing completed transactions and account activity to identify patterns that are only visible over longer time horizons.
Real-time scoring uses a streamlined feature set of approximately 80 signals that can be computed within the latency budget. These features are pre-computed and cached in Redis, so the inference step only needs to assemble the feature vector and run the model. The model produces a risk score from 0 to 100, which is compared against operator-configurable thresholds. Transactions scoring below the low threshold are approved instantly. Those above the high threshold are blocked. Those in between are routed to a secondary review flow, which may involve 3D Secure authentication, additional identity verification, or manual review by the operator's fraud team.
Batch scoring uses the full 200+ feature set and runs on a 5-minute cycle. It re-evaluates all transactions from the past 24 hours against updated features, particularly cross-account signals that may have changed as new accounts are created or new transactions are processed. If batch scoring identifies a previously approved transaction as high-risk, it can trigger a hold on the associated account, flag pending withdrawals for review, or alert the operator's fraud team for investigation.
Reducing False Declines
Reducing fraud is only half the equation. Every false decline represents a legitimate bettor who was blocked from depositing, and the cost of a false decline is often higher than the cost of a chargeback. Our data shows that 40% of bettors who experience a false decline never attempt another deposit on the same platform. At an average customer acquisition cost of $300, each false decline effectively wastes $120 in marketing spend.
Our ML approach reduces false declines through two mechanisms. First, the model's ability to combine 200+ signals means it can distinguish between genuinely suspicious behavior and behavior that merely looks suspicious in isolation. A large deposit from a new account using a mobile device on cellular data would trigger multiple rules in a traditional system. Our model considers the full context: is the device consistent with the registration device? Does the IP geolocate to the same region as the account address? Is the deposit amount consistent with the bettor's stated income during KYC? Is there an upcoming major sporting event that typically drives higher deposits?
Second, the tiered response system means we do not have to make a binary approve/block decision. Transactions in the uncertain middle range can be subjected to step-up authentication, such as a 3D Secure challenge or a one-time SMS verification, rather than being declined outright. This approach recovers 62% of transactions that would have been blocked by a rules-based system, with only a 0.3% increase in chargeback rate.
Measurable Results
After twelve months of production deployment across our operator base, the results are clear. Chargeback rates dropped from an average of 1.6% to 0.56%, a 65% reduction that brought every operator well below card network monitoring thresholds. False decline rates fell from 15.2% to 11.7%, a 23% improvement that translated directly to higher deposit volumes.
The combined financial impact is substantial. Across our platform, operators save an estimated $2.3 million per month in reduced chargeback losses, eliminated monitoring program fees, and recovered revenue from fewer false declines. Three operators who were previously in Visa's VDMP have exited the program entirely, removing the existential risk to their payment processing capabilities.
Looking ahead, we are investing in two areas that we believe will push these numbers even further. First, real-time collaboration across operators, where a fraud pattern detected at one operator can immediately protect all others on the platform. Second, generative AI for fraud pattern discovery, using large language models to analyze historical fraud cases and identify novel attack vectors before they scale. The fraud landscape in sports betting is constantly evolving, and our defenses must evolve faster.