Modernize with control.Prove every number.Collect earlier.Put AI to work, safely.
Data Semantics moves banking data off legacy warehouses onto a modern, governed platform, so every figure traces back to source. Then analytics and AI agents put it to work across customers, collections, service and operations, inside your own environment.
For retail, corporate and Islamic banks, finance companies and the data, finance and risk teams behind them.
Data Semantics provides data modernization, analytics and governed AI for banks and financial services. We move warehouses and data marts from platforms such as Teradata, Netezza and Oracle Exadata onto Microsoft Fabric, Databricks or Snowflake, migrate data with full reconciliation when a bank changes core, and make every number traceable from source transaction to regulatory report. AI agents then work on collections, customer service and finance operations, in the bank's own cloud tenant, in-country or on-premises.
A customer is onboarded in one system, financed in another, collected from in a third and reported on in a fourth. Every handoff between them is where trust in the numbers, cash and customer experience leak.
Four audiences, one foundation: connect the data, model the bank, then automate the decisions that matter, under the controls banking depends on.
High-volume customers, products and channels, where service, collections and cost-to-serve decide margin.
Groups, facilities and relationship managers, with Islamic financing contracts modeled as they are, not forced into conventional loan structures.
Auto, personal and installment lenders where collections speed and portfolio quality drive results.
The functions that own the warehouse, the ledger, the reports and the AI program.
A trusted data foundation first, then analytics and AI agents on top of it. We combine banking understanding with the technical know-how to build what you need; the last card in each row covers everything else.
Data warehouse modernization moves banking warehouses, data marts and reporting off legacy platforms onto Microsoft Fabric, Databricks or Snowflake, on an industry-aligned banking model, and proves the new numbers match the old.
Core banking data migration moves customer, account, financing and ledger data when a bank changes its core system, with every rule decided by the business and every balance reconciled.
Regulatory data intelligence makes the numbers behind finance, risk and regulatory reports traceable from source transaction to final figure, with the evidence to show it.
Customer and portfolio intelligence connects customers, groups, accounts, financing and transactions into one view of relationships, profitability and portfolio risk.
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Collections automation predicts which accounts are about to fall behind on installments, cards and financing, and uses AI agents to reach out, track promises-to-pay and escalate, within the bank's conduct rules.
Customer service automation resolves routine customer requests by voice and chat, prepares cases for staff, and shows where service time goes.
AP and procurement automation captures, matches, approves and posts the bank's own vendor, facilities and technology invoices into the ERP, with a full audit trail.
Tell us where your teams lose the most time today. We’ll show you what we would fix first, on the systems you already run.
Private AI means AI assistants and agents that run in an environment the bank controls, on the bank's own data and entitlements, with nothing sent to a public service unless you approve it.
Available on engagement
Scoped with your technology, security and risk teams. The deployment pattern, model choice and controls are agreed during assessment, before anything touches customer data.
A banking ontology is a shared model of your bank: customers, groups, accounts, financing contracts, transactions, collateral and ledger entries, how they relate, and which system each lives in. We align it with established banking data models such as FSLDM, so your definitions follow industry structure instead of being reinvented project by project.
Two workflows banks often start with.
A portfolio balance in the reporting layer doesn't match core banking. Here's what happens next.
Installment, card and financing arrears handled consistently, for every account, every month.
We don’t borrow results from other industries. Every banking engagement starts by measuring today’s baseline, and success is proven against it. These are the measures we track most often.
Core banking data mapped from iMAL to T24 and modeled in an FSLDM-based warehouse, with analytical APIs and downstream integrations feeding reporting across the bank.
Receivables brought into one engine with automated outreach, promise-to-pay tracking and reconciliation, needing over 80% fewer collectors. The same approach now applies to installment, card and financing arrears.
Each is a fixed-scope engagement on your own data, measured against today’s baseline.
Your warehouse, pipelines and reports assessed, with a plan, timeline and parity approach for moving to Fabric, Databricks or Snowflake.
Data Platform Modernization →AnalyticsTrace one finance or regulatory report from source transaction to final figure, and close the reconciliation gaps.
Data Governance & Quality →AutomationAI agents work one early-arrears segment within your contact rules, measured against today’s roll rates.
Zebo →Every engagement follows the same path: measure today’s baseline, connect and model the data, prove it on a real workflow, then scale.
About banking AI
A banking ontology is a shared model of your bank: customers, groups, accounts, financing contracts, transactions, collateral and ledger entries, how they relate and which system each lives in. Data Semantics aligns it with established banking data models such as Teradata FSLDM and IBM’s banking data model, so definitions follow industry structure instead of being reinvented project by project.
Core banking migration moves live customer, account and ledger data when a bank replaces its core system, for example moving to Temenos T24. Data warehouse modernization moves the reporting and analytics layer, such as a Teradata or Oracle warehouse, onto Microsoft Fabric, Databricks or Snowflake while the core stays in place. Data Semantics does both, often in the same program.
Data modernization
Data Semantics moves banking warehouses and data marts from Teradata, Netezza, Oracle and Exadata, SQL Server, SAS and Hadoop, and pipelines from Informatica, ODI, DataStage and SSIS, onto Microsoft Fabric, Databricks or Snowflake, with parity reconciliation before each report is switched.
Yes. Data Semantics migrates and modernize warehouses and data marts, including Teradata environments, while your core banking system stays in place. Business rules are carried across with a decision record, and the new platform is reconciled against the old so you can show the numbers match.
Data Semantics builds the data foundations behind it: data quality, lineage from source transaction to report line, reconciliation controls and audit-ready evidence. Regulatory interpretation and compliance ownership stay with the bank and its specializts.
AI and automation
Collections intelligence predicts which accounts are about to fall behind on installments, cards and financing, then uses AI agents to send reminders, make calls, track promises-to-pay and escalate. Outreach runs within the bank's conduct rules, including approved contact windows and scripts, and hardship cases and disputes go to a person.
AI agents trace issues, score risk, reach customers, prepare cases and recommend next steps within guardrails you set. Business-critical decisions stay with accountable people, and every action is logged for audit.
Security and residency
Yes, on engagement. AI assistants and agents can run in your own cloud tenant in an in-country region, in a private cloud, or fully on-premises, including air-gapped environments where required. The deployment pattern, model choice and controls are agreed with your technology, security and risk teams during assessment.
No. Where your policies require it, we use open-weight models hosted inside your environment, with access tied to your existing entitlements and every prompt, answer and action logged. Nothing goes to a public AI service unless you approve it.
Getting started
Yes, and we recommend it. Common starting points are a reconciliation that never closes, a warehouse migration, early-arrears collections, contact-center automation and invoice processing. Each new use case reuses the same ontology and controls, so it goes live faster.
A reconciliation that never closes, a warehouse migration, early arrears, contact-center volume or invoices. We'll show you what it looks like solved, on your own data, in your own environment.
