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.

Discuss Banking priorities Explore use cases

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.

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.

Discover

Personalized product discovery, financial guidance, and banker prompts grounded in customer context and eligibility.

Apply

AI-assisted application completion, document extraction, KYC checks, fraud indicators, and exception routing.

Onboard

Account activation, funding, card setup, digital enrollment, and proactive recovery of abandoned journeys.

Transact

Real-time anomaly detection, cash-flow insight, dispute support, and contextual customer alerts.

Service

Context-rich servicing, complaint triage, next-best resolution, and human escalation for sensitive decisions.

Grow

Relationship expansion across deposits, lending, cards, treasury, and advisory based on explainable needs and signals.

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.

Banker / relationship manager

Receives a governed customer brief, next-best conversation prompts, and policy-aware recommendations before the interaction.

Operations analyst

Uses AI to summarize cases, assemble evidence, and prioritize queues while retaining exception and approval authority.

Risk / compliance leader

Defines policy, thresholds, fairness, model risk, AML, privacy, and audit requirements before production.

Data / platform owner

Connects core banking, CRM, digital, transaction, fraud, document, and third-party data into reusable governed products.

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

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

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.

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.

Request a working session Why Infinitive