Industries / Payments

Issuers, acquirers, networks, processors, fintech, fraud, merchant, dispute, risk, product, operations, and engineering leaders

Payments data + AI transformation

Payments organizations operate at transaction speed. AI has to improve authorization, fraud, disputes, merchant performance, and resilience without adding latency, control gaps, or opaque decisions. Infinitive connects the systems you already run to trusted data and AI so teams can improve outcomes without separating innovation from governance.

Discuss Payments priorities Explore use cases

Speak the language of the business

Start from the operating model—not the model.

AuthorizationFraudChargebacksMerchant riskTokenizationSettlementRoutingNetwork performanceDisputesPCI

We use the language, measures, controls, and workflows your teams already operate, then connect them to modern data and AI capabilities.

Trusted Enterprise AI

Five capabilities. One business outcome.

Every use case can draw from the same Infinitive capability model: Data Foundations to enable AI; Analytics + Decision Support powered by AI; Governance + Risk to trust AI; Agents + Automation driven by AI; and Adoption + Workforce Enablement to realize value.

01Data FoundationsTo enable AI 02Analytics + Decision SupportPowered by AI 03Governance + RiskTo trust your AI 04Agents + AutomationDriven by AI 05Adoption + WorkforceTo realize the value of AI

Customer / constituent journey

Use AI where it improves the experience and the economics.

AI becomes more valuable when the same governed data and decision logic follows the journey instead of being rebuilt channel by channel.

Onboard

Accelerate merchant, account, device, token, and identity setup with governed risk checks.

Authorize

Combine transaction, identity, device, merchant, and behavior context for real-time decisions.

Clear / settle

Monitor exceptions, reconciliation, fees, interchange, and settlement across participants.

Dispute

Summarize evidence, classify disputes, assemble representment, and route exceptions.

Protect

Detect fraud, account takeover, merchant abuse, collusion, and operational anomalies.

Grow

Use merchant and customer intelligence to optimize acceptance, routing, offers, and product strategy.

Employee journey

Give people better context, not another disconnected tool.

Infinitive redesigns the workflow so AI prepares evidence, finds patterns, and removes low-value effort while people stay accountable for judgment, exceptions, empathy, and business tradeoffs.

Fraud analyst

Receives prioritized alerts, entity context, similar cases, and reasoned evidence.

Merchant / product team

Uses performance, acceptance, risk, and segment intelligence to improve products and relationships.

Operations

Investigates settlement, reconciliation, dispute, and network exceptions with AI-prepared context.

Engineering / risk

Owns low-latency architecture, feature quality, controls, monitoring, and decision traceability.

In-depth use cases

Where data, Databricks, and AI can change the work.

These are not generic AI demos. Each use case starts with a recognizable operating problem, the data required to solve it, the role of AI, the human decision point, and the measures that determine whether value is real.

Use case 01

Real-time authorization and fraud intelligence

Payment decisions must balance approval rate, fraud loss, customer friction, latency, and explainability at massive scale.

Trusted data on Databricks

Stream transaction, merchant, device, token, account, behavior, geolocation, network, and historical decision data into governed features.

AI at work

Score anomalous behavior, detect emerging patterns, generate explainable analyst context, and support adaptive strategies.

How Infinitive helps

Design streaming pipelines, governed feature and model operations, investigation workspaces, and monitoring on Databricks.

Human accountability

Fraud and risk leaders approve strategy, thresholds, overrides, and high-impact policy changes.

Measures to improveApproval rateFraud lossFalse declinesReview rateDecision latency

Use case 02

Dispute and chargeback automation

Dispute teams spend time classifying cases, gathering transaction evidence, applying network rules, and preparing representment packages.

Trusted data on Databricks

Connect transaction, authorization, merchant, device, communication, receipt, delivery, network-rule, and prior dispute data.

AI at work

Classify disputes, retrieve rule context, summarize evidence, identify missing documentation, and draft representment for review.

How Infinitive helps

Implement document and transaction data products, controlled generation, workflow integration, and quality monitoring.

Human accountability

Dispute specialists approve classifications, credits, representment, and exception decisions.

Measures to improveCase cycle timeWin rateManual effortEvidence completenessCustomer resolution time

Use case 03

Merchant onboarding and risk intelligence

Merchant onboarding requires KYB, ownership, business model, expected volume, prohibited activity, fraud history, and ongoing monitoring.

Trusted data on Databricks

Combine application, entity, beneficial owner, web, transaction, chargeback, fraud, sanctions, industry, and external-risk data.

AI at work

Summarize merchant risk, detect inconsistencies, flag abnormal behavior, and prioritize enhanced due diligence or monitoring.

How Infinitive helps

Build merchant 360 data products, entity resolution, monitoring, reviewer workspaces, and governance.

Human accountability

Risk and compliance teams decide onboarding, reserves, restrictions, and termination.

Measures to improveOnboarding timeManual reviewRisk detectionChargeback exposureMonitoring productivity

Use case 04

Payment network and routing optimization

Organizations need to improve authorization, cost, latency, reliability, and routing across processors, networks, regions, and methods.

Trusted data on Databricks

Unify authorization responses, latency, fees, interchange, routing, network events, merchant segments, and operational incidents.

AI at work

Identify routing patterns, explain approval degradation, predict operational issues, and recommend experiments or routing adjustments.

How Infinitive helps

Build the telemetry data foundation, performance analytics, experimentation layer, and governed decision support.

Human accountability

Product, network, and risk leaders approve routing or commercial strategy changes.

Measures to improveAuthorization rateTransaction costLatencyDowntimeRouting effectiveness

Use case 05

Settlement and reconciliation exception intelligence

Settlement, fee, funding, and reconciliation breaks span multiple networks, files, currencies, participants, and accounting systems.

Trusted data on Databricks

Normalize settlement, clearing, authorization, fee, ledger, funding, and reference data in Databricks.

AI at work

Classify breaks, group root causes, summarize impacted transactions, and suggest investigation paths based on prior resolved cases.

How Infinitive helps

Modernize ingestion, reconciliation data models, exception analytics, and operations workflows.

Human accountability

Operations and finance teams approve accounting or settlement corrections.

Measures to improveBreak agingManual investigationRepeat errorsFinancial exposureClose efficiency

Use case 06

Merchant and customer growth intelligence

Payments data contains rich signals about merchant performance, customer behavior, acceptance, churn, and product opportunities but is often difficult to activate.

Trusted data on Databricks

Create governed merchant and customer data products with transactions, product usage, acceptance, service, risk, and commercial context.

AI at work

Generate segment insights, churn or growth signals, account briefs, and explainable next-best product or service actions.

How Infinitive helps

Build the semantic layer, AI/BI experience, account workflows, and value measurement.

Human accountability

Commercial teams decide pricing, offers, relationship actions, and strategic changes.

Measures to improveMerchant retentionProduct penetrationRevenue per accountSales productivityAcceptance improvement

How Infinitive engages

Land with a focused need. Expand through demonstrated value.

Clients can enter at any stage. We do not require a multi-year transformation before value is visible; we connect the right capabilities to the immediate problem and scale only where the evidence supports it.

01Activate

Define a priority problem, establish trusted data, and demonstrate value through a focused engagement or POC.

02Productionize

Make the capability secure, reliable, governed, measurable, and useful in everyday operations.

03Scale

Extend proven value across teams, workflows, data domains, and business areas—only where evidence supports it.

Why Infinitive

Trust is part of the architecture and the operating model.

Infinitive combines data and AI delivery with governance, risk, transformation, and adoption so the same team can address the technical path and the organizational conditions that determine whether AI actually works in practice.

Build on enterprise reality

Connect core systems, controls, teams, and existing investments rather than designing a disconnected AI island.

Govern at design time

Data access, lineage, security, policy, auditability, cost, and human decision rights are defined before production.

Prove value before scale

Use a focused entry point with business measures so the next decision is based on evidence—not enthusiasm.

Keep people accountable

AI can automate preparation and pattern finding; people remain at judgment, exception, empathy, and accountability.

Payments next step

Choose one workflow where better data and AI could change the outcome.

Bring the business owner, data/platform lead, and operating or risk leader. Infinitive will help frame the use case, required data, governance needs, human decision points, and the right Activate, Productionize, or Scale path.

Request a working session Why Infinitive