How a finance modernization platform built for banks and insurance enables AI

In banking and insurance, AI is only as effective as the data it uses.

Financial institutions operate on complex, interrelated financial processes that evolve daily. This means they need AI that understands current realities, adapts to changes and produces reliable outputs regulators and executives can trust. They don’t need AI that simply generates static forecasts.

There are two key roles for AI in a finance modernization context:

1. Supporting data production by identifying and correcting errors during ingestion and reconciliation
2. Enabling advanced analysis once the data is accurate and aligned

Both require consistent, high-quality data as a starting point. A finance modernization platform purpose-built for banks and insurance enables this by producing trusted, contextual and interconnected data at scale.

Equally important, the platform enforces standardized processes and embedded controls. This combination of better data, stronger controls and more consistent processes ensures that AI runs efficiently and produces compliant and future-ready outputs.

Here is how industry-specific finance platforms like SAP Fioneer are designed to support AI roles in bank and insurance modernization directly.

1. Providing high-quality, daily operative data that AI needs

Many banks and insurers still rely on static, point-in-time data snapshots, typically used for monthly closes, quarterly reporting or audit preparation. These are disconnected from real-time operations and are quickly outdated. They do not reflect changes in risk exposure, asset valuation or liquidity positions as they occur.

AI models built on this data lack context. They can only describe past states and do not understand evolving conditions or why key indicators have shifted. Errors in snapshot creation become embedded and invisible to the model, which can lead to incorrect conclusions.

This is especially critical for Tier 1 banks that need a complete, up-to-date picture of the business every day, and for large insurers managing dynamic claim reserves, policy valuations and solvency capital positions that shift daily. Data must be gathered from hundreds of systems and recalculated across domains. KPIs in accounting, risk and treasury—or actuarial metrics such as claim reserves, unearned premiums and loss ratios—are interconnected and recalculated daily.

A purpose-built platform like SAP Fioneer supports this by ingesting daily changes, recalculating every KPI and producing a fresh snapshot of the organization each day with an AI Agent.

The agent operates in real time and is embedded in user workflows, surfacing suggestions and anomalies as the data is processed.

AI models trained on this data see the actual state of the business, not a stale approximation. This ensures AI is learning from valid, relevant and timely inputs—a prerequisite for trustworthy outputs.

With this foundation, AI supports efficiency by reducing manual data handling, strengthens compliance by ensuring every calculation is based on validated data and prepares institutions for future use cases where regulators will demand explainable, daily AI-driven insight.

2. Integrating AI into the data assembly line rather than treating it as an add-on

In many banks and insurance organizations, AI is implemented as an afterthought. It takes the form of dashboards, stand-alone models or third-party plug-ins applied to data exported from a warehouse. These tools sit outside the data production process and lack transparency. They cannot see how data was generated or why it changed.

AI applied post-process cannot account for domain-specific adjustments, such as IFRS valuation rules, policyholder behavior models or risk reclassifications. It also limits explainability, which is a regulatory requirement.

SAP Fioneer embeds AI into the core data production process. AI agents learn from past reconciliation patterns, and reduce the time needed to validate financial results.

The platform also maintains a human-in-the-loop design, ensuring key decisions remain explainable and overseen, critical for audit, financial regulation and compliance frameworks like Solvency II in insurance.

Rather than analyzing outputs, AI participates in creation. Errors are addressed during production, not after the fact, which means cleaner data and faster cycles. Embedding AI into the process creates better-controlled workflows and standardized practices, ensuring efficiency gains are sustainable and outputs remain compliant.

3. Enabling AI to operate on fully contextualized, domain-aware data

Banking and insurance data are not generic.

• For an insurer, a policy liability includes claim history, expected loss development and associated capital charges
• For a bank, a loan balance includes repayment status, future risk and associated capital charges
• Risk metrics like probability of default rely on credit behavior and market inputs
• Accounting and risk systems use different definitions, assumptions and timelines

AI trained on siloed or inconsistent data cannot generate meaningful outputs. For example, in insurance, AI may identify a spike in claims without aligning it to changes in policy wording or reinsurance coverage. For banks, it may flag a deteriorating loan in risk but fail to align that insight with accounting treatment. This results in conflicting reports and undermines trust in the system.

SAP Fioneer solves this through its multi-domain architecture. Data is linked across finance, risk and operations. Each KPI is traceable to its source assumptions and events. AI models work within this structure, accessing contextual data that preserves the relationships between financial domains.

As a result, AI can answer more than just “what happened”. It can attribute changes in impairment, identify causes of increased credit losses and simulate how market shifts affect asset valuations. Outputs are consistent across reporting systems and defensible in regulatory reviews. This consistency transforms AI from an experimental tool into a reliable, future-ready capability for finance, risk and actuarial leaders.

4. Embedding AI into a modular, scalable architecture for continuous learning

Many AI solutions are packaged into front-end tools with no access to upstream data logic. Banks and insurers are forced to choose between black-box models or complex, expensive integrations that connect AI to legacy systems. This creates fragile architectures that are hard to govern or scale.

SAP Fioneer’s Finance Platform offers a layered approach:

• The data foundation is managed through the Financial Product Subledger (FPSL) and Financial Services Data Management (FSDM – for banks only).
• Reconciliation and quality processes are handled through Financial Control (FC) and Financial Services Data Quality (FSDQ).
• Integration occurs via the Finance Open Integration Framework (FOF).

This structure allows banks and insurers to embed AI at any point in the workflow. AI can be used early to improve data ingestion and validation, or later to support forecasting and root-cause analysis, such as predicting claim frequency trends or identifying drivers of reserve changes in insurance portfolios. Each model operates on trusted data, aligned to standardized definitions and audit trails.

SAP Fioneer supports a progressive AI maturity model, starting with validation and assistance, then evolving toward embedded intelligence and automation.

With it, banks and insurers gain flexibility. They introduce AI gradually, without overhauling core systems, because the architecture is designed to support AI maturity over time, and is not limited to short-term deployment.

5. Providing data governance and standardization to enhance AI performance 

“AI can only work on already finalized and qualitative good data. It can’t create data — it can only analyze what’s already been produced correctly.” — Daniel Pehnec, SAP Fioneer Senior Solutions Manager

Without data governance and validation, AI models underperform, generate misleading results and introduce compliance risk.

Yet many solutions lack governance by design. They do not validate data during ingestion, track lineage or enforce domain-specific rules. When auditors or regulators ask where a number came from, banks and insurers cannot provide a defensible answer, risking fines, increased scrutiny and closures.

SAP Fioneer addresses this through standardization. Its data model captures lineage and consistency, and validation logic is embedded in ingestion flows via its data quality layer (FSDQ). When data is corrected or overridden, feedback loops capture the logic, making the system smarter over time. The AI Agent then captures and reuses logic from past corrections, continuously improving its ability to suggest accurate resolutions.

This governance structure makes AI outputs explainable and compliant. Insurers and banks can trace every result back to a validated source, meeting banking audit standards and insurance requirements, such as actuarial control frameworks. AI operates within a controlled environment, reducing risk and increasing trust.

By enforcing standardized processes and capturing every correction as reusable logic, the platform builds institutional knowledge over time, driving efficiency, compliance and adaptability as AI matures.

The outcome is efficient, compliant and future-ready AI

AI in banking and insurance goes beyond trend analysis or prediction to power faster, safer and more accurate decisions, which depend on clean, contextual and interconnected data.

SAP Fioneer enables AI by aligning data across accounting, risk, treasury and actuarial domains, embedding domain-specific logic into every layer, and governing the entire process from ingestion to insight. No longer an add-on, AI becomes an integral part of the finance modernization journey.

A finance platform built for banks and insurance also gives AI a foundation to be credible, explainable and effective for the entire organization:

• CFOs gain trusted numbers
• CIOs get orchestration across systems
• CDOs have governed definitions, lineage and data quality
• CROs and actuaries work with explainable models they can defend with regulators
• COOs gain AI embedded into streamlined processes

While 92% of financial services executives see AI as essential, only 25% have implemented it at scale. SAP Fioneer’s platform architecture helps close this gap by embedding AI into the data and process foundations banks and insurers already rely on.

See how SAP Fioneer embeds AI into daily finance data production. Book a demo or contact a specialist today.