Approval rate optimisation
Payment approval rate optimisation requires orchestrating first-time transaction payloads to match exact issuer expectations. By leveraging BIN-based routing logic and data enrichment, merchants can submit perfectly formatted authorisation requests to the most appropriate acquirer partner before any decline occurs.
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Boosting authorisation approvals is a continuous process requiring precise measurement and iterative refinement. Cardflo supports merchants in defining baselines, tracking key metrics, and implementing strategic adjustments to improve payment success rates at each stage of the transaction lifecycle.
Driving improvements involves understanding the nuanced interplay between issuer preferences, payment scheme mandates, and the quality of data within every authorisation request. This programmatic approach ensures that all changes are quantifiable and contribute positively to overall performance.
This tool dynamically routes transactions through the optimal acquirer partner from our network of 50+ integrations, ensuring the highest approval ratios. It improves transaction success rates and maximises revenue capture for merchants.
Approval rate optimisation overview
Cardflo defines authorisation rate as the ratio of approved authorisations to total authorisations initiated, encompassing both successful first attempts and subsequent retries. This metric is further segmented by variables such as card scheme, issuing bank, BIN, currency, and transaction type to enable granular analysis.
Establishing a baseline involves collecting historical transaction data for a defined period, identifying current approval rates across these segments, and pinpointing areas with the most significant potential for improvement.
Moving the approval number genuinely requires a multi-faceted approach, starting with strategic acquirer selection given issuer preference and local acquiring capabilities. The quality of data embedded within the authorisation message is paramount; accurate AVS data, correct CVV submission, and consistent cardholder details directly influence issuer decisions.
Network tokens and an adaptive 3D Secure strategy also play critical roles in reducing fraud false positives while maintaining customer convenience.
Cardflo facilitates controlled testing by allowing merchants to A/B test different payment configurations or routing strategies. This involves segmenting traffic, applying a new strategy to a control group, and meticulously comparing approval rates over a statistically significant period.
Lift attribution is achieved by isolating the impact of the tested changes from other operational variables, ensuring accurate performance assessment and demonstrating return on optimisation efforts.
How approval rate optimisation works
Baseline establishment
Cardflo ingests your historical transaction data over a specified period, typically three to six months. We segment this data by issuer, card scheme, BIN, and currency to calculate a precise baseline authorisation rate for each category. This granular view highlights underperforming segments and identifies areas where optimisation efforts will yield the greatest impact on overall approval rates.
Optimisation strategy development
Based on baseline analysis, Cardflo proposes specific strategies, such as adjustments to acquirer routing, modifications to 3D Secure thresholds, or enrichment of authorisation message fields. We identify specific issuer decline codes that consistently occur, mapping them to potential solutions like alternative acquirer routes or data quality improvements within the transaction request.
Controlled experimentation
Your optimisation strategies are deployed as controlled experiments, directing a predefined percentage of traffic to a test group while maintaining a control group. For instance, half of all transactions from a specific BIN might be routed through a new acquirer. Cardflo monitors key metrics like authorisation rate, decline codes, and latency for both groups, collecting statistically significant data.
Performance attribution and scaling
Following the test period, Cardflo analyses the experimental data, comparing approval rates and decline reasons between the control and test groups. A clear lift attributed to the new strategy is identified. Successful optimisations are then scaled across the entire transaction volume, continuously monitored for sustained performance, and refined as new data emerges.
Why approval rate optimisation matters
Reduce operational costs associated with declines
Frequent payment declines lead to increased customer service inquiries, manual order reviews, and re-engagement efforts, all of which incur operational costs. By proactively optimising approval rates, merchants reduce the volume of declined transactions, thereby lowering the need for support resources and manual interventions. This efficiency gain streamlines operations and reduces overheads related to managing payment failures.
Improve customer experience and retention
Smooth and successful payment processing is fundamental to a positive customer experience. Repeated payment declines frustrate customers, leading to abandoned carts and a negative perception of your brand, potentially driving them to competitors. A high approval rate ensures a seamless checkout, fostering trust and encouraging repeat purchases, contributing significantly to long-term customer retention and loyalty.
Approval rate optimisation use cases
Grocery basket authorisations
Online grocers submit initial authorisations before weighted produce, substitutions and bag charges establish the final basket amount, creating issuer sensitivity to estimated totals. Cardflo enriches authorisation payloads with appropriate estimated-amount indicators and routes by card BIN to acquirer partners whose configuration matches the merchant’s grocery transaction pattern.
Petrol forecourt pre-authorisations
Unattended petrol terminals request pre-authorisation before the final fuel volume is known, and incomplete terminal, MCC or verification data can prompt issuer declines. Cardflo applies routing rules for domestic and commercial card BINs, while acquirer partners configure correctly identified pre-authorisation requests for first-time issuer assessment.
Low-value transit taps
Transport operators submit high-velocity, low-value contactless fares where aggregated ticketing, transit indicators and SCA treatment must be represented correctly at first authorisation. Cardflo routes eligible BIN ranges towards suitably configured acquirer partners and supplies the exemption and transaction-context data issuers use to assess genuine passenger journeys.
Corporate card invoice payments
B2B suppliers accepting corporate cards against invoices can see first-time declines when enhanced commercial data, merchant descriptors or card-product routing are incomplete. Cardflo identifies commercial BINs, enriches the initial request with available invoice and tax fields, and directs it to acquirer partners aligned with Visa and Mastercard corporate card acceptance.
Approval rate optimisation by the numbers
This represents a typical industry range for merchants moving from a static, single-acquirer setup to a multi-acquirer environment with active decline management.
Industry benchmarks suggest that a significant portion of temporary declines can be recovered through intelligent retry logic and proper timing of re-submissions.
By refining fraud rules and using secondary authentication selectively, businesses often see this level of reduction in legitimate transactions being incorrectly blocked.
Methodology: these figures are illustrative ranges drawn from published industry data and observed merchant cohorts, not guarantees. Actual results depend on your risk profile, card mix, geography and acquiring setup, and are confirmed only in your own pricing and approval terms.
Related terms
Talk to our team about a live rollout across our acquirer partners' rails.
What you get with Approval rate optimisation
- Define authorisation rate benchmarks using segmented transaction data across various parameters.
- Improve approval rates through strategic partner selection aligned with issuer preferences.
- Enhance authorisation message data quality, including AVS, CVV, and cardholder details.
- Leverage network tokens to reduce friction and improve issuer approval rates for recurring transactions.
- Implement an adaptive 3D Secure strategy to balance fraud prevention with authorisation success.
- Cardflo supports A/B testing of payment configurations, comparing approval rates for different strategies.
- Attribute uplift directly to specific changes by isolating the impact of new optimisation efforts.
- Monitor performance across card schemes, issuing banks, and transaction types for granular insights.
- Identify specific BIN ranges and currency combinations with lower approval rates for targeted action.
- Adjust retry logic based on issuer decline codes to maximise the chances of subsequent approval.
A short scoping call, then a written plan for your MIDs.
Questions about Approval rate optimisation
What is the difference between a soft decline and a hard decline in this context?
A soft decline, such as a temporary technical failure or insufficient funds, suggests that a subsequent attempt might succeed. A hard decline, often caused by a stolen card or a closed account, indicates a permanent failure where retries should be avoided to prevent scheme penalties.
Optimisation systems distinguish between these codes to ensure that retry logic is only applied when there is a statistical probability of success, thereby protecting the merchant's reputation with issuers and card schemes.
Which payload fields influence first-time payment authorisation decisions?
Relevant fields include billing address elements, cardholder name, transaction type, recurring or stored credential indicators, merchant-initiated transaction references and 3DS exemption data. Cardflo standardises and validates these fields against each acquirer partner’s API requirements before submission.
This reduces avoidable mismatches or omissions that may affect an issuer’s initial decision, and still keeping the transaction’s genuine commercial context and required scheme indicators.
Can approval rate optimisation help with PSD2 and SCA compliance?
Yes, it is a core component. The system evaluates each transaction to see if it qualifies for an exemption under SCA, such as a low-value payment or a transaction going to a trusted beneficiary.
By correctly flagging these exemptions in the authorisation request, the merchant can avoid the 3DS challenge, reducing friction for the user while remaining fully compliant with the regulatory requirements of PSD2 or the upcoming PSD3 framework.
What role does BIN lookup play in transaction success?
Bank Identification Number (BIN) lookup provides data on the issuing bank, card type, and country of origin. Optimisation tools use this data to predict how an issuer will react to certain variables.
If a specific issuer is known to decline transactions lacking certain data fields, the system ensures those fields are populated or routes the transaction to an acquirer with a better historical relationship with that specific bank.
Why is retry logic timed rather than immediate?
Immediate retries for an insufficient funds decline are rarely successful and can be flagged as fraudulent or abusive by the card schemes. Strategic retries are timed to coincide with typical cycles, such as paydays or specific hours when issuer systems are most stable.
This disciplined approach minimises the risk of being blocked by the network while maximising the chance that the cardholder has replenished their balance.
How does the use of network tokens impact approval rates?
Network tokens are issued by the card schemes and are specific to the merchant. Unlike standard card numbers, they are kept up to date by the schemes even if the physical card is replaced.
Issuers generally view network tokens as more secure, which often leads to a measurable increase in approval rates and a reduction in false positives compared to transactions using plain Primary Account Numbers.
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