Services

Nine services, mapped to the banking value chain.

Banks don't buy "data and AI" — they buy stickier deposits, faster credit decisions, fewer scam losses, cleared AML queues, a faster close and audit-ready reporting. Below, each SCIKIQ service line is mapped to the lines of business and group functions where it does that work, with the use cases, the KPIs it moves and the demos you can open today.

See the value-chain map How we add value, by domain Find your service by role
Chapter 2 · Services on the value chain

Where each service line works on the banking value chain

Read across a row to see where a service line leads and where it supports. Read down a column to see the team a bank gets in that domain. Select any domain to see use cases, the KPIs we help move and the working demos.

9
Service lines
8
Value-chain domains
19
Lead roles across the map
34
Supporting roles across the map
Leads the work Supports Hover a dot for detail · select a domain to explore it
01 · Advise
Data & AI StrategyStrategy
Data Governance & Regulatory DataGovernance
02 · Build
Data Engineering & Platform ModernisationData platform
AI & Agentic EngineeringAI & agents
03 · Transform
Finance TransformationFinance
Risk, Compliance & Financial CrimeRisk & fin. crime
Customer & GrowthCustomer
Operations & AutomationOperations
04 · Run
Managed Services: DataOps, MLOps & AgentOpsManaged
Service lines engaged 7 7 5 5 9 6 7 7

The mapping shows where each service line typically leads or supports; every engagement is scoped to the bank. The value chain itself is explained on the overview.

How we add value

Domain by domain: from data to a measurable outcome

Each domain follows the same path — source data, a governed data product, AI and agents, an outcome the business measures. KPIs are the measures we help you move and track; we agree targets with you, we don't promise them in advance.

Domain 1 of 8

Retail banking & deposits

Deposits reprice with the rate cycle, digital banks compete for the primary relationship, and onboarding, lending and complaints still run on hand-offs.

Data
Core, digital journeys, KYC & bureau
Data product
Customer 360 with consent
AI & agents
Pricing, onboarding & collections agents
Outcome
Faster decisions, stickier balances

Banking use cases

  • Deposit pricing and balance-attrition signals by segment
  • Digital onboarding with document AI and KYC checks
  • Personal-loan underwriting assistance with explainable reasons
  • Collections and complaints agents that draft the next action

KPIs we help you move

Cost of depositsOnboarding completion timeTime to loan decisionComplaint resolution time

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working CATALYST Retail agents
Domain 2 of 8

Cards & payments

Instant rails leave seconds to stop a payment, scam-reimbursement rules move liability onto banks, and declines, disputes and scheme fees erode card economics.

Data
Authorisations, instant payments & disputes
Data product
Real-time payments data product
AI & agents
Fraud & scam scoring, dispute agents
Outcome
Losses down, good payments through

Banking use cases

  • Real-time fraud and APP-scam scoring before the money leaves
  • Authorisation-decline analysis to let good transactions through
  • Chargeback and dispute resolution with the evidence assembled
  • Scheme-fee audit and merchant-risk monitoring

KPIs we help you move

Fraud and scam lossesFalse-positive decline rateDispute cycle timeScheme fees per transaction

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Fraud & Authorization Disputes & Chargebacks Run the chargeback agent Cards & payments agents
Domain 3 of 8

Corporate & commercial banking

Credit memos, covenant tracking and corporate KYC refresh take days of assembly work, while treasurers expect real-time cash visibility.

Data
Financials, limits, covenants & cash flows
Data product
Corporate client & credit data product
AI & agents
Credit-memo, covenant & KYC agents
Outcome
Faster credit, earlier warnings

Banking use cases

  • Credit-memo drafting from statements and the credit file
  • Covenant monitoring with early-warning signals
  • Client cash and liquidity forecasting
  • Corporate KYC refresh with ownership checks

KPIs we help you move

Credit-memo turnaroundCovenant breaches flagged earlyKYC refresh backlogRelationship-manager time with clients

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Corporate agents CATALYST
Domain 4 of 8

Trade finance

Every presentation is examined by hand against the credit terms, while sanctions, dual-use and trade-based money-laundering checks sit in separate queues.

Data
Presentations, credit terms, vessels & lists
Data product
Trade transaction data product
AI & agents
Examiner, screening & red-flag agents
Outcome
Faster, safer document checks

Banking use cases

  • Document examination against UCP 600 and ISBP, with discrepancies classed
  • Sanctions, dual-use and vessel screening
  • Trade-based money-laundering red-flag analysis
  • Limits and exposure monitoring across the book

KPIs we help you move

Examination time per presentationDiscrepancy accuracyScreening hits cleared per analystLimit and exposure breaches

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Run the document examiner Trade finance agents
Domain 5 of 8

Correspondent & transaction banking

Respondent banks rely on their correspondent for payments, investigations, liquidity and risk support such as CECL, while message repair and nostro breaks keep operations busy.

Data
ISO 20022 messages, nostros & call reports
Data product
Payments & respondent data product
AI & agents
Investigation, reconciliation & CECL agents
Outcome
Respondents served faster

Banking use cases

  • Payment investigations and ISO 20022 message repair
  • Nostro/vostro reconciliation with breaks explained
  • CECL allowance modelling, Q-factors and documentation for respondents
  • Respondent analytics: deposits, profitability and peer benchmarks

KPIs we help you move

Investigation turnaroundOpen nostro breaksMessage repair rateAllowance cycle time

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Run a payment investigation Run the CECL allowance Correspondent agents
Domain 6 of 8

Wealth management & investment banking

Advisers and bankers spend their time preparing and checking: suitability, rebalancing, source-of-wealth reviews, conflicts clearance and settlement exceptions.

Data
Portfolios, mandates, deals & trades
Data product
Client & portfolio data product
AI & agents
Suitability, preparation & conflicts agents
Outcome
More time advising clients

Banking use cases

  • Suitability checks and rebalancing proposals within mandate
  • Adviser meeting preparation from the full client picture
  • Source-of-wealth review with evidence gaps flagged
  • Book building, M&A screening, conflicts clearance and settlement exceptions

KPIs we help you move

Adviser preparation timeSuitability exceptionsSource-of-wealth review timeSettlement fails

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Wealth agents Investment banking agents
Domain 7 of 8

Risk, compliance & financial crime

AML queues, KYC reviews and model-risk work outgrow teams, while Basel III endgame, CECL and IFRS 9, DORA and conduct rules ask how every number and AI decision was reached.

Data
Alerts, KYC, credit losses & models
Data product
Critical data elements with lineage
AI & agents
Investigator agents & model governance
Outcome
Audit-ready and explainable

Banking use cases

  • AML alert investigation with evidence gathered and a draft decision
  • Periodic KYC review and customer-risk refresh
  • Expected-credit-loss (CECL / IFRS 9) model support and documentation
  • BCBS 239 lineage and model and agent governance

KPIs we help you move

Alerts closed per investigatorFalse-positive alert rateRegulatory report preparation timeModels with documented evaluation

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Compliance & AML Run the AML investigator Data Governance Hub Agent governance
Domain 8 of 8

Finance, treasury & operations

Cost-to-income has barely moved: the close depends on manual matching, spreadsheet journals and hand-written commentary, and the back office runs on hand-offs.

Data
GL, sub-ledgers, recon feeds & plans
Data product
Governed finance data product
AI & agents
Matching, journal & commentary agents
Outcome
A faster, cleaner close

Banking use cases

  • Auto-matching and break investigation in reconciliation
  • Journal generation, intercompany matching and eliminations
  • AI commentary for the balance sheet, P&L and board pack
  • Revenue-leakage detection and back-office automation

KPIs we help you move

Days to closeAuto-match rateManual journalsCost-to-income ratio

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working COMPASS LEDGER360 NEXUS Run the break investigator Revenue Assurance
Find your service

Start from your role

Each service line has a clear owner on the client side. Pick yours to jump to the services most teams like yours start with.

CFO
Chief Financial Officer
CRO
Chief Risk Officer
COO
Chief Operating Officer
CDO
Chief Data Officer
CIO
Chief Information Officer
Business
Heads of retail, cards, corporate, trade & wealth
01

Advise

Set direction, make the business case and put the rules in place that data and AI must meet.

Advise · Service line

Data & AI Strategy

ForCDOCIOCFO
On the value chainRetail & depositsCards & paymentsCorporate & commercialTrade financeCorrespondent bankingWealth & marketsRisk & financial crimeFinance & operations

The client problem

AI pilots multiply but few reach production. There is no shared, evidence-based view of where the bank stands on data and AI, which use cases pay back, or who owns them.

Outcomes

  • A maturity baseline across 12 capability layers and five stages, scored on evidence rather than opinion
  • A prioritised roadmap of use cases with a business case for each
  • An AI operating model: ownership, funding, delivery and controls

What we do

  • Data & AI maturity assessment
  • AI strategy & use-case prioritisation
  • AI operating model & centre of excellence design
  • Business case & value tracking

Typical engagements

  • AssessmentMaturity assessment and roadmap
  • Pilot · 30-45 daysProve the top-ranked use case on your data
  • BuildRoadmap delivered through our Build and Transform services
  • Managed runValue tracking and roadmap refresh

Delivered with

Maturity assessment Value calculator SCIKIQ Data Fabric
Advise · Service line

Data Governance & Regulatory Data

ForCDOCRO
On the value chainRetail & depositsCorrespondent bankingWealth & marketsRisk & financial crimeFinance & operations

The client problem

BCBS 239, SOX and model-risk reviews want proof of where a number came from. Ownership, lineage and data quality checks are often undocumented or manual, and AI adds a new layer to govern.

Outcomes

  • A working CDO function and data operating model
  • Critical data elements with owners, lineage and data quality controls for regulatory reports
  • AI and agent governance: policy, approvals, evaluation and a kill switch

What we do

  • CDO set-up & operating model
  • BCBS 239 & regulatory data lineage
  • Metadata catalogue & critical data elements
  • Data quality rules & controls
  • AI & agent governance

Typical engagements

  • AssessmentGovernance and lineage gap review against regulatory reports
  • Pilot · 30-45 daysCatalogue, lineage and DQ for one regulatory report
  • BuildCDO function, metadata repository and controls, bank-wide
  • Managed runOngoing DQ monitoring and catalogue stewardship

Delivered with

Data Governance Hub SCIKIQ Data Fabric Agent governance
02

Build

Engineer the data platforms and the AI that runs on them, with governance built in.

Build · Service line

Data Engineering & Platform Modernisation

ForCIOCDO
On the value chainRetail & depositsCards & paymentsCorporate & commercialTrade financeCorrespondent bankingWealth & marketsRisk & financial crimeFinance & operations

The client problem

Data sits across core banking, card switches, SAP, Oracle and legacy stores. Every new report or model starts with another bespoke extract, and nothing is traceable end to end.

Outcomes

  • One governed, cloud-agnostic data platform on Azure, AWS or hybrid
  • Batch and streaming pipelines with lineage from source
  • Repeatable environments deployed with Infrastructure as Code

What we do

  • Pipelines, batch & streaming
  • Lakehouse / warehouse on Azure or AWS
  • Banking data models & source-to-target mapping
  • Infrastructure as Code (Terraform)
  • Legacy platform modernisation

Typical engagements

  • AssessmentData estate and target-architecture review
  • Pilot · 30-45 daysConnect and curate priority sources end to end
  • BuildPlatform build in sprints, tested with real data
  • Managed runDataOps under agreed SLAs

Delivered with

SCIKIQ Data Fabric 268 data sources
Build · Service line

AI & Agentic Engineering

ForCIOCOO
On the value chainRetail & depositsCards & paymentsCorporate & commercialTrade financeCorrespondent bankingWealth & marketsRisk & financial crimeFinance & operations

The client problem

GenAI and agent pilots stall at the controls review: no autonomy limits, no audit trail and no named owner for the exceptions.

Outcomes

  • Supervised agent squads in production, with autonomy and guardrails set per agent
  • Every plan, tool call, guardrail check and decision logged
  • People review only the exceptions, with the agent's draft and evidence in front of them

What we do

  • Agent design: roles, squads & autonomy levels
  • Tool integration with core, GL, switch & case systems
  • Evaluation & guardrails: policy, limits, kill switch
  • GenAI: auto-commentary / NLG
  • Document AI: KYC, US tax forms, AP invoices

Typical engagements

  • AssessmentAgent opportunity and controls review
  • Pilot · 30-45 daysOne agent squad working real cases under your controls
  • BuildSquads integrated with your systems and scaled
  • Managed runAgentOps: monitoring, overrides, drift

Delivered with

Digital workforce Run an agent live Governance & controls NARRATOR
03

Transform

Domain practices that change how a business function works, end to end, with our accelerators as the starting point.

Transform · Service line

Finance Transformation

ForCFOControllersFP&A
On the value chainCorrespondent bankingRisk & financial crimeFinance & operations

The client problem

The close depends on manual matching, spreadsheet journals and commentary written by hand. Intercompany breaks and SOX evidence pile up at month-end.

Outcomes

  • A faster, cleaner close with automated matching and journal flows
  • Management commentary drafted by AI and signed off by people
  • An efficient, transparent intercompany process

What we do

  • Financial close & reconciliation
  • Accounting hub (multi-GAAP)
  • Intercompany matching & eliminations
  • FP&A, forecasting & board pack
  • SOX controls & regulatory reporting

Typical engagements

  • AssessmentClose and reconciliation diagnostic
  • Pilot · 30-45 daysOne accelerator on a live recon or reporting stream
  • BuildAccounting hub, intercompany or FP&A rollout
  • Managed runRecon and close squads run under SLA

Delivered with

CLARION LEDGER360 NEXUS COMPASS NARRATOR Enterprise Reconciliation SOX Compliance Break Investigator agent
Transform · Service line

Risk, Compliance & Financial Crime

ForCROChief Compliance Officer
On the value chainRetail & depositsCards & paymentsCorporate & commercialTrade financeCorrespondent bankingWealth & marketsRisk & financial crime

The client problem

Alert and dispute queues grow faster than investigation teams, and most analyst time goes on gathering evidence rather than deciding.

Outcomes

  • Cases arrive with evidence gathered and a draft decision ready for the analyst
  • Straight-through handling only within policy limits; everything else goes to a named owner
  • An audit-ready record for every decision

What we do

  • AML alert investigation & monitoring
  • Fraud & authorisation analytics
  • Disputes & chargebacks
  • KYC & account-form validation

Typical engagements

  • AssessmentAlert, fraud or dispute operations review
  • Pilot · 30-45 daysAn investigator agent on one queue, under your policy
  • BuildSquads across AML, fraud and disputes
  • Managed runAgentOps with evaluation and override review

Delivered with

Compliance & AML Fraud & Authorization Disputes & Chargebacks AML Alert Investigator Chargeback Resolution Agent
Transform · Service line

Customer & Growth

ForBusiness headsCMO
On the value chainRetail & depositsCards & paymentsCorporate & commercialCorrespondent bankingWealth & markets

The client problem

Customer data is split across products and channels, so offers are generic and churn shows up too late to act on.

Outcomes

  • A single customer view shared by sales, service and marketing
  • Next-best-action and churn signals in the relationship manager's workflow
  • Campaigns targeted from the same governed data

What we do

  • Customer 360
  • Cross-sell & next-best-action
  • Churn prediction
  • Integrated marketing & sales platform

Typical engagements

  • AssessmentCustomer data and growth-use-case review
  • Pilot · 30-45 daysCustomer 360 and one NBA use case
  • BuildIntegrated marketing & sales platform
  • Managed runModel monitoring and MLOps

Delivered with

CATALYST RM Assistant agent
Transform · Service line

Operations & Automation

ForCOO
On the value chainRetail & depositsCards & paymentsTrade financeCorrespondent bankingFinance & operations

The client problem

Back-office work runs on manual hand-offs and scattered bots, and revenue leaks through fees and charges nobody checks.

Outcomes

  • Automation run as one portfolio from a control tower
  • Revenue leakage detected and recovered
  • Bots and agents working back-office queues, with people on the exceptions

What we do

  • RPA & reconciliation bots
  • Automation control tower & foundry (CoE)
  • Revenue assurance
  • Document automation: AP invoices (OCR), US tax forms

Typical engagements

  • AssessmentAutomation portfolio and leakage review
  • Pilot · 30-45 daysOne process automated end to end
  • BuildControl tower and automation foundry
  • Managed runBot and agent operations under SLA

Delivered with

RPA Automation Control Tower Revenue Assurance Value calculator
04

Run

Keep platforms, models and agents healthy and improving after go-live.

Run · Service line

Managed Services: DataOps, MLOps & AgentOps

ForCIOCOO
On the value chainCards & paymentsCorrespondent bankingRisk & financial crimeFinance & operations

The client problem

After go-live, pipelines break, models drift and agents need someone watching overrides, limits and evaluation results.

Outcomes

  • Platforms, pipelines, models and agents run under agreed SLAs
  • Continuous improvement driven by override and evaluation data
  • Your teams freed from L2/L3 support

What we do

  • Run & L2/L3 support
  • DataOps
  • MLOps
  • AgentOps: logs, overrides, drift, kill switch
  • Continuous improvement

Typical engagements

  • AssessmentRun-readiness and support model review
  • Pilot · 30-45 daysHypercare for a newly live capability
  • BuildMonitoring, runbooks and SLAs
  • Managed runOngoing service under agreed SLAs

Delivered with

Digital workforce Governance & controls SCIKIQ Data Fabric
Our assets

What makes our services faster

Every engagement starts from SCIKIQ IP rather than a blank page. These assets are how we deliver — they come with the service.

1
SCIKIQ Data Fabric

The governed foundation every engagement runs on: the 4C method (Connect, Curate, Contextualize, Consume), 268 data sources, and governance, metadata, lineage, data quality, an AI/agent layer and security built in — cloud-agnostic on Azure, AWS or hybrid.

Explore the framework
2
Accelerators

Six flagship accelerators — CLARION, NARRATOR, LEDGER360, NEXUS, CATALYST and COMPASS — and nine solution accelerators. Pre-built, configurable starting points that we tailor to your rules, data and controls.

Accelerators by service line
3
Supervised digital workforce

AI agents that plan, call tools and gather evidence on the governed data. Policy decides what goes straight through; people approve everything else; every step is logged and a kill switch halts all agents.

Meet the workforce Watch one work
How we engage

From a business outcome to measured value

Every engagement starts from the business outcome, not the technology. We frame it through the same business lens each time, then deliver in five phase-gated stages. Most banks start with a discovery and value case for one value-chain domain, then scale to the next on the same foundation.

How we frame an engagement

  1. 1Business outcome & KPI
  2. 2Value-chain domain
  3. 3Decisions
  4. 4Data
  5. 5AI & agents
  6. 6Governance & adoption
  7. 7Measured value

Delivery phases

Phase 1
Discover & value case

Outcome, domain and KPIs agreed; data and process assessment; baseline and business case.

Phase 2
Design

Decisions, data products, models, agents and controls designed for the chosen domain.

Phase 3
Build & integrate

Sprint delivery on the SCIKIQ Data Fabric, integrated with core banking, card, payment, GL and case systems; tested on real data.

Phase 4
Deploy & adopt

Go-live, people and process change, agent autonomy limits agreed with risk; value tracked against the baseline.

Phase 5
Run & scale

Managed service — DataOps, MLOps and AgentOps under SLA — and the next domain on the same foundation.

Each phase ends with a gate signed off by your steering group; a pilot in one domain typically reaches a production-ready capability in 30-45 days. The SCIKIQ Data Fabric we build on

Engagement models

Staff Augmentation

Data engineers, architects, analysts and AI specialists embedded in your teams, under your delivery lead.

Project Delivery

Outcome-based delivery of an accelerator or platform build, owned end-to-end by SCIKIQ with phase gates.

Managed Services

We run and improve your data platforms, models and agents — DataOps, MLOps, AgentOps and support under agreed SLAs.

Weekly status Bi-weekly steering Phase-gated sign-off 30-45 day pilot → scale

Start with the outcome you need

Pick a domain and a KPI. We'll propose a discovery and value case or a 30-45 day pilot and show you what the first weeks look like.

Talk to us Take the self-assessment View pitch deck
Next chapter · 3 of 6
The foundation

Every domain above runs on the same governed data fabric — core banking, cards, payments and the ledger connected, curated, contextualised and consumed, with lineage from source to regulatory report.

Next chapter: The foundation
AI
AI Analystagentic

I'm the SCIKIQ AI Analyst, working with tools rather than from memory. I can:

  • Query the live platform APIs (disputes, fraud, AML, recon, revenue…)
  • Report what the digital workforce is doing: runs, approvals, overrides
  • Start an agent run on a real case and hand you the link to watch it

Every answer shows the tools it used.