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Trusted Enterprise AI for data-intensive organizationsAWS · Databricks · Azure · Human judgment by design
Retail, commercial, card, deposit, lending, branch, digital, risk, fraud, AML, and operations leaders
Banking data + AI transformation
Banks are under pressure to grow relationships, lower cost to serve, modernize data, control risk, and move AI from isolated pilots into governed everyday work. Infinitive connects the systems you already run to trusted data and AI so teams can improve outcomes without separating innovation from governance.
Speak the language of the business
Start from the operating model—not the model.
DepositsLendingCardsKYC / AMLFraudCollectionsCustomer 360Branch + digital servicingRegulatory reporting
We use the language, measures, controls, and workflows your teams already operate, then connect them to modern data and AI capabilities.
DiscoverPersonalized product discovery, financial guidance, and banker prompts grounded in customer context and eligibility.
ApplyAI-assisted application completion, document extraction, KYC checks, fraud indicators, and exception routing.
OnboardAccount activation, funding, card setup, digital enrollment, and proactive recovery of abandoned journeys.
TransactReal-time anomaly detection, cash-flow insight, dispute support, and contextual customer alerts.
ServiceContext-rich servicing, complaint triage, next-best resolution, and human escalation for sensitive decisions.
GrowRelationship expansion across deposits, lending, cards, treasury, and advisory based on explainable needs and signals.
Banker / relationship managerReceives a governed customer brief, next-best conversation prompts, and policy-aware recommendations before the interaction.
Operations analystUses AI to summarize cases, assemble evidence, and prioritize queues while retaining exception and approval authority.
Risk / compliance leaderDefines policy, thresholds, fairness, model risk, AML, privacy, and audit requirements before production.
Data / platform ownerConnects core banking, CRM, digital, transaction, fraud, document, and third-party data into reusable governed products.
Use case 01
AML investigation and case intelligence
Investigators often move across alerts, customer files, transactions, typologies, adverse media, prior cases, and narrative templates. The friction is fragmented evidence and repeated manual assembly.
Trusted data on Databricks
Unify alert, transaction, KYC, customer, entity, case, document, and external-risk data in governed Databricks data products with lineage and access controls.
AI at work
Use AI to summarize case context, identify related entities and patterns, retrieve relevant typologies and policies, draft investigative narratives, and surface what evidence is still missing.
How Infinitive helps
Map the investigation workflow, connect current AML systems to the lakehouse, configure governed AI workspaces, define analyst handoffs, and establish monitoring and value measures.
Human accountability
Investigators remain accountable for disposition, escalation, SAR decisions, and narrative approval.
Measures to improveAlert review timeInvestigator throughputEvidence completenessQA reworkFalse-positive reduction
Use case 02
Customer 360 and next-best relationship action
Deposits, cards, lending, servicing, digital, and CRM histories are often separated by product and channel, making it hard to understand customer needs or coordinate treatment.
Trusted data on Databricks
Create reusable customer and household data products with identity resolution, product relationships, interaction history, eligibility, consent, and quality controls.
AI at work
Generate explainable customer summaries, propensity signals, banker prompts, and next-best actions that respect eligibility, privacy, and channel context.
How Infinitive helps
Connect source systems, build the semantic layer, configure AI/BI experiences, and redesign banker and digital workflows around a consistent customer view.
Human accountability
Bankers and product owners retain judgment for recommendations, sensitive conversations, and exception handling.
Measures to improveRelationship depthOffer conversionDigital engagementService resolutionCustomer retention
Use case 03
Fraud and credit exception decision support
Fraud and credit teams balance speed with loss prevention, policy compliance, fair treatment, and explainability across high-volume decisions.
Trusted data on Databricks
Bring together application, identity, device, transaction, bureau, behavior, policy, and prior decision data with governed feature and lineage management.
AI at work
Surface anomalies, reason codes, policy conflicts, document inconsistencies, and similar historical cases to help reviewers prioritize and decide faster.
How Infinitive helps
Design the data pipelines, model and feature governance, review experience, human decision rights, and production monitoring.
Human accountability
Fraud, credit, and risk owners retain authority for adverse actions, exceptions, overrides, and escalation.
Measures to improveFraud lossDecision cycle timeManual review rateOverride qualityAuditability
Use case 04
Collections and hardship intelligence
Collections teams need to distinguish temporary hardship, chronic risk, contact preference, regulatory constraints, and the treatment most likely to help the customer and the bank.
Trusted data on Databricks
Combine payment history, product exposure, contact outcomes, hardship indicators, servicing notes, digital behavior, and policy rules.
AI at work
Prioritize accounts, summarize history, recommend compliant contact strategies, detect hardship signals, and draft customer-specific options for review.
How Infinitive helps
Build the governed decision layer, integrate servicing systems, define treatment logic and controls, and measure outcome by segment.
Human accountability
Agents and hardship specialists approve treatment, exercise empathy, and manage exceptions.
Measures to improveCure rateRight-party contactRoll ratesPromise-to-pay performanceComplaint rate
Use case 05
Branch and contact-center service intelligence
Customer service teams lose time searching across products, knowledge bases, prior interactions, policies, and back-office systems.
Trusted data on Databricks
Connect customer history, interaction transcripts, knowledge content, product rules, complaints, staffing, and service outcomes.
AI at work
Prepare a customer brief, retrieve approved answers, summarize calls, recommend next steps, route complex cases, and identify recurring service friction.
How Infinitive helps
Implement governed knowledge retrieval, Databricks analytics, workflow integration, and frontline adoption with clear escalation patterns.
Human accountability
Frontline staff remain accountable for empathy, disclosure, exceptions, complaints, and final customer communication.
Measures to improveHandle timeFirst-contact resolutionEscalation rateCustomer satisfactionKnowledge reuse
Use case 06
Regulatory reporting and data control modernization
Banks spend significant effort reconciling data, tracing definitions, documenting transformations, and responding to control or audit questions across reporting processes.
Trusted data on Databricks
Establish governed data products, lineage, quality rules, business definitions, ownership, and repeatable transformations in Databricks.
AI at work
Assist analysts with lineage exploration, control evidence, reconciliation explanations, policy retrieval, and issue triage without changing accountable sign-off.
How Infinitive helps
Map critical data elements, modernize pipelines, implement Unity Catalog patterns, define controls, and improve issue-management workflows.
Human accountability
Finance, risk, data owners, and control functions retain certification and regulatory accountability.
Measures to improveReconciliation effortData issue agingControl evidence timeLineage coverageReporting cycle time
01ActivateDefine a priority problem, establish trusted data, and demonstrate value through a focused engagement or POC.
02ProductionizeMake the capability secure, reliable, governed, measurable, and useful in everyday operations.
03ScaleExtend 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 realityConnect core systems, controls, teams, and existing investments rather than designing a disconnected AI island.
Govern at design timeData access, lineage, security, policy, auditability, cost, and human decision rights are defined before production.
Prove value before scaleUse a focused entry point with business measures so the next decision is based on evidence—not enthusiasm.
Keep people accountableAI can automate preparation and pattern finding; people remain at judgment, exception, empathy, and accountability.
Banking 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.
