Every declined transaction in the sports betting world is more than a lost deposit. It is a frustrated bettor who may never return. Across the industry, sportsbook operators face approval rates that are significantly lower than mainstream e-commerce, often hovering around 65-72% depending on the market and payment method. At BetFlow, we set out to fundamentally change this equation by applying machine learning to transaction routing, and the results exceeded even our most optimistic projections.
Over the past eighteen months, our Smart Routing Engine has processed more than 140 million transactions across 32 regulated markets. The outcome: a 27% average improvement in approval rates for operators using our dynamic routing compared to static acquirer assignments. This article walks through the problem, our approach, the technical implementation, and the measurable business impact.
The Problem: Why Sports Betting Transactions Get Declined
Sports betting occupies a uniquely challenging position in the payments landscape. Unlike standard e-commerce, where Merchant Category Codes (MCCs) are broadly accepted, sports betting transactions carry MCC 7995, which triggers heightened scrutiny from issuing banks, acquirers, and card networks alike. This classification alone can reduce baseline approval rates by 10-15 percentage points compared to a typical online retailer.
The challenges compound from there. Transaction patterns in sports betting are inherently irregular. A bettor might deposit $20 on a Tuesday afternoon and then attempt a $500 deposit minutes before a major NFL game on Sunday. Issuing banks see this volatility as risk and often decline transactions that would be perfectly legitimate. Add to this the regulatory patchwork across US states, European jurisdictions, and emerging markets, and you have an environment where a one-size-fits-all routing strategy leaves enormous amounts of revenue on the table.
We analyzed 18 months of historical transaction data across our platform and found that operators using static acquirer assignments were seeing average approval rates of 68.3%. More critically, the variance was enormous: certain BIN ranges from specific issuers had approval rates as low as 41%, while others exceeded 90%. The question was clear: could we build a system that learned which acquirer would yield the highest approval probability for each individual transaction?
The Solution: Dynamic Acquirer Selection with ML
Our Smart Routing Engine operates on a straightforward principle: for every incoming transaction, predict the approval probability across all available acquirers, then route to the one with the highest expected success rate, weighted by cost. In practice, implementing this at scale with sub-50ms latency requirements was anything but straightforward.
The model ingests over 150 signals per transaction. These range from obvious features like card BIN, issuing country, and transaction amount, to more nuanced ones: time since last successful transaction with the same acquirer, the acquirer's rolling approval rate for the specific BIN range over the past 4 hours, day-of-week patterns, and even signals derived from the current sports calendar. A Champions League final triggers very different deposit patterns than a mid-week MLS match, and the model has learned to account for this.
We group the feature set into four categories: card attributes (32 features), merchant context (28 features), temporal patterns (41 features), and acquirer performance history (53 features). Each category feeds into specialized sub-models whose outputs are then combined in an ensemble layer. This architecture allows us to update individual components independently, which is critical when an acquirer changes their risk appetite or a new regulatory requirement shifts approval patterns.
Key Insight: We discovered that temporal features, specifically the acquirer's rolling approval rate for a given BIN range over short time windows, were the single most predictive feature category. Acquirers adjust their risk models throughout the day, and capturing these micro-trends was responsible for roughly 40% of our improvement.
Implementation: Model Architecture and Feature Engineering
The routing model uses a gradient-boosted decision tree ensemble (LightGBM) for the core prediction, combined with a lightweight neural network for embedding categorical features with high cardinality. We evaluated several architectures, including deep neural networks and transformer-based models, but the LightGBM-plus-embeddings approach consistently delivered the best combination of prediction accuracy and inference speed.
Feature engineering proved to be the most impactful phase of the project. Raw transaction attributes are transformed into higher-order features through a pipeline that runs both in batch (for model training) and in real-time (for inference). Here is a simplified example of how we compute rolling acquirer performance features:
// Rolling acquirer approval rate computation
interface AcquirerPerformance {
acquirerId: string;
binRange: string;
window: '1h' | '4h' | '24h';
approvalRate: number;
totalTransactions: number;
lastUpdated: number;
}
async function getAcquirerPerformance(
redis: RedisClient,
acquirerId: string,
binRange: string
): Promise<AcquirerPerformance[]> {
const windows = ['1h', '4h', '24h'] as const;
const pipeline = redis.multi();
for (const window of windows) {
const key = `perf:${acquirerId}:${binRange}:${window}`;
pipeline.hGetAll(key);
}
const results = await pipeline.exec();
return windows.map((window, i) => ({
acquirerId,
binRange,
window,
approvalRate: parseFloat(results[i]?.approved ?? '0') /
Math.max(parseFloat(results[i]?.total ?? '1'), 1),
totalTransactions: parseInt(results[i]?.total ?? '0'),
lastUpdated: parseInt(results[i]?.ts ?? '0'),
}));
}The model is retrained daily on a rolling 90-day window of transaction data. We initially experimented with longer training windows but found that acquirer behavior shifts frequently enough that older data introduces noise rather than signal. The retraining pipeline runs on a scheduled Kubernetes job, and new models are promoted to production only after passing a battery of validation checks against a held-out test set from the most recent 48 hours.
For real-time inference, the model is compiled to ONNX format and served via a custom inference service written in Rust. This gives us p99 inference latency of 8ms, well within our 50ms budget for the entire routing decision. Feature values are pre-computed and cached in Redis, so the inference service only needs to assemble the feature vector and run the prediction.
A/B Testing and Rollout Strategy
Given the financial stakes, we took an extremely cautious approach to rolling out ML-based routing. The initial deployment used a shadow mode where the model made predictions for every transaction but routing decisions were still made by the existing rules-based system. This allowed us to measure theoretical improvement without any risk to live transactions.
After four weeks of shadow testing showed consistent predicted improvements of 18-25%, we moved to a controlled A/B test. Ten percent of transactions were randomly assigned to the ML router, with the remaining 90% continuing through the rules-based system. We monitored not just approval rates but also chargeback rates, processing costs, and settlement times to ensure the ML router was not inadvertently optimizing for approvals at the expense of fraud prevention.
The A/B test ran for six weeks across three operators in different market segments. The ML router consistently outperformed the baseline by 22-31% on approval rates, with no measurable increase in chargebacks. Processing costs actually decreased by 12-18% because the model naturally favored acquirers with lower interchange rates when approval probabilities were similar across multiple options.
Following the successful A/B test, we gradually increased the traffic allocation: 25% for two weeks, 50% for two weeks, 75% for one week, and finally 100%. At each stage, we validated that performance metrics remained stable. The full rollout across all operators was completed in early January 2026.
Results and Business Impact
After full deployment, the numbers speak for themselves. Across our entire platform, average approval rates increased from 68.3% to 86.7%, a 27% relative improvement. For certain operator segments, particularly those with high concentrations of US-issued debit cards, the improvement was even more dramatic at 34%.
The processing cost reduction of 15% was an unexpected bonus. By intelligently distributing transactions across acquirers based on both approval probability and cost, the model found a sweet spot that the rules-based system never could. For an operator processing $50M in monthly deposits, this translates to roughly $75,000 in monthly savings on interchange and processing fees alone.
Perhaps most importantly, operator-level customer support tickets related to failed deposits dropped by 43%. Bettors who previously faced repeated declines and called support or abandoned the platform entirely were now completing their deposits on the first attempt. Several operators reported measurable improvements in their Net Promoter Scores within weeks of the rollout.
What is Next: Continuous Learning and Real-Time Adaptation
We are currently working on the next generation of the Smart Routing Engine, which will move from daily retraining to continuous online learning. The new architecture will update model weights in near-real-time based on transaction outcomes, allowing the system to adapt within minutes when an acquirer changes their risk parameters or a card network adjusts its authorization rules.
We are also expanding the feature set to include cross-operator intelligence. With operator consent, anonymized approval patterns from one operator can inform routing decisions for another. Early experiments with this federated approach show an additional 5-8% improvement in approval rates, particularly for operators in newer markets with limited historical data.
The sports betting payments landscape continues to evolve rapidly, and static routing rules simply cannot keep up. Machine learning gives us the ability to adapt at the speed the market demands, and we are just getting started. If you are an operator struggling with approval rates, we would love to show you what dynamic routing can do for your business.