Margins turn with the rate cycle, deposits and the primary relationship are contested, scams and financial crime industrialise, and regulators ask how every number and every AI decision was reached. The answer runs across every line of business — from how a customer is onboarded to how the ledger closes. SCIKIQ is the governed data and AI platform that helps banks make that shift, one value-chain domain at a time.
Banks organised around products — a deposit system, a card platform, a lending book — now compete on decisions made across all of them. The banks pulling ahead treat data as a product and AI as an operating capability, not a set of pilots. These are the pressures they are responding to.
As the rate cycle turns, asset yields reprice faster than deposit costs fall. Net interest income stops doing the heavy lifting — productivity and cost-to-income have to.
Data & AI: deposit pricing and ALM analytics, and agents that take cost out of finance and operations.
Read the insightDigital banks, embedded finance and higher-yield accounts make balances mobile. Customers expect instant onboarding, relevant offers and service that resolves rather than deflects.
Data & AI: customer 360, attrition signals, next-best-action and AI-assisted service.
Read the insightReal-time rails leave seconds to act, and reimbursement rules push more of the cost of authorised push-payment scams onto the paying and receiving bank.
Data & AI: real-time fraud and scam scoring, and dispute agents that assemble the evidence.
Read the insightAlert volumes and periodic reviews grow faster than investigation teams, and most analyst time goes on gathering evidence rather than deciding.
Data & AI: investigator agent squads that prepare the case; the MLRO still decides.
Read the insightBasel III endgame capital, CECL and IFRS 9 provisioning, DORA resilience and consumer-duty-style conduct rules all ask for traceable data, delivered faster.
Data & AI: BCBS 239 lineage, expected-credit-loss model support and AI-assisted regulatory reporting.
Read the insightProducts live on separate cores, card platforms and ledgers with different customer IDs. Every new report, model or agent starts with another extract.
Data & AI: one governed data fabric, so the core can be modernised one capability at a time.
Read the insightModel-risk management was built for scorecards, not autonomous agents. Supervisors expect an inventory, evaluation, human oversight and an audit trail behind every AI decision.
Data & AI: agent governance — autonomy levels, approvals, evaluations and a kill switch.
Read the insightThe response isn't one platform or one model — it is data and AI applied line of business by line of business, on a shared, governed foundation.
See where, domain by domain ↓Six customer-facing lines of business and the two group functions that run underneath them. Select a domain to see the data it runs on, the AI opportunities, and what SCIKIQ does there.
Deposits reprice as rates move, digital banks compete for the primary relationship, and customers expect onboarding, lending and service in minutes — with the bank still accountable for every decision.
Data it runs on
Data & AI opportunities
What SCIKIQ does here
Instant rails leave seconds to stop a payment, scam-reimbursement rules move liability onto banks, and declines, disputes and scheme fees quietly erode card economics.
Data it runs on
Data & AI opportunities
What SCIKIQ does here
Relationship managers and credit teams spend days assembling credit memos, tracking covenants and refreshing KYC files, while corporate treasurers expect real-time cash visibility.
Data it runs on
Data & AI opportunities
What SCIKIQ does here
Trade is still paper-heavy and high-risk: 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 it runs on
Data & AI opportunities
What SCIKIQ does here
Respondent banks rely on their correspondent for payments, investigations and liquidity — and increasingly for risk support such as CECL — while ISO 20022 messages and nostro breaks keep operations busy.
Data it runs on
Data & AI opportunities
What SCIKIQ does here
Advisers and bankers spend too much time preparing and checking rather than advising: suitability, rebalancing, source-of-wealth reviews, conflicts clearance and settlement exceptions compete for the same people.
Data it runs on
Data & AI opportunities
What SCIKIQ does here
AML queues, KYC reviews and model-risk work grow faster than teams, while Basel III endgame, CECL and IFRS 9, DORA and conduct rules ask how every number — and every AI decision — was reached.
Data it runs on
Data & AI opportunities
What SCIKIQ does here
Cost-to-income has barely moved. The close still depends on manual matching, spreadsheet journals and hand-written commentary, and back-office queues run on hand-offs and scattered bots.
Data it runs on
Data & AI opportunities
What SCIKIQ does here
Opportunities and approaches are described qualitatively. Shaded chips are SCIKIQ service lines; the others open working demos and agents. See every service line mapped to these eight domains
Nine service lines that advise, build, transform and run — delivered on three pieces of SCIKIQ IP, so banks start from working components rather than a blank page.
In our AI & Agentic Engineering and Managed Services work, agents do the routine work end to end — in every line of business above. Policy decides what goes straight through; people approve everything else. Nothing happens off the record.
Fetching live figures from the agent control room…
Open the digital workforceAgents plan, call tools and gather evidence on the governed Data Fabric — the same lineage-tracked data your teams use.
Each agent has an autonomy level. A case goes straight through only if confidence, amount limits and evidence checks pass.
Everything else lands in a named owner's queue with the agent's draft, rationale and evidence — approve, edit or reject.
Every plan, tool call, guardrail check and decision is logged; overrides feed evaluation, and a kill switch halts all agents.
Pre-built, configurable solutions on the SCIKIQ Data Fabric — many staffed by an agent squad. Our teams tailor rules, data and autonomy to your controls, so delivery starts well ahead of a from-scratch build.
Five flagship accelerators plus enterprise reconciliation and SOX controls for the office of the CFO.
AI/ML reconciliation engine achieving 90%+ auto-match rates across eWallet, Instant Rail, Card Switch, and GL systems with intelligent tolerance matching and exception handling.
GenAI-powered natural language generation for automated Balance Sheet & P&L commentary, variance analysis, and executive briefings with audit-ready documentation.
AI-powered multi-GAAP accounting hub with automated journal entry generation, sub-ledger aggregation, and real-time GL reconciliation for accelerated financial close.
AI-powered intercompany transaction matching, automated eliminations, and multi-entity consolidation with real-time dispute resolution and transfer pricing compliance.
Ledger to landing in one workspace: close with a predicted landing, explain the month with AI commentary, rolling forecast, driver-based planning, scenarios and the board pack.
Finance Transformation engagements combine these with the flagship accelerators — close, recon, accounting hub, intercompany, FP&A and SOX.
About Finance TransformationFigures reported by banks, vendors and analysts about agentic AI in operations — not SCIKIQ results. Follow each link for the source.
Less reconciliation task time with AI agents across 12 banks; break investigation more than 73% faster.
Faster client onboarding in tests, with AI agents for trade accounting, reconciliation and onboarding/KYC under human oversight.
“Digital employees” with their own system access; 20,000 staff building agents on its internal AI platform.
Agents one practitioner can supervise in financial-crime agent squads; productivity gains of 200–2,000%.
Fewer false AML alerts reported by one large bank; another reports 2–4x more true positives.
Enter your own volumes. The estimate compares today's manual handling with agents working the cases and people reviewing only the exceptions.
Estimate only, not a quote or a SCIKIQ result. Manual hours = cases × minutes ÷ 60. Supervised hours = cases × (1 − STP share) × review minutes ÷ 60. Hours saved = manual − supervised. FTE = hours saved ÷ 150. Cost saved = hours saved × cost per hour (× 12 for a year). Supervisor capacity = 150 h × 60 ÷ ((1 − STP share) × review minutes). Excludes platform and run costs.
Tell us the problem — a slow close, an AML backlog, a governance finding, a platform to modernise. We'll propose an assessment or a 30-45 day pilot, delivered by SCIKIQ teams on our framework and accelerators.