Enterprise leaders in banking and finance are embracing artificial intelligence to automate transactions, invoices, compliance checks, and more. Yet the success of these AI initiatives hinges on a often-overlooked factor: the quality of the data and domain knowledge feeding the models. From our vantage point at Itemize – a company focused on financial transaction processing and business banking automation – we have seen that high-quality data and robust domain-specific knowledge are essential for AI accuracy and reliability. In this post, we examine why data and knowledge quality matter so profoundly for AI in financial operations, backed by recent insights from Deloitte, Gartner, McKinsey, PwC, and others. We also explore real-world examples where poor data quality derails AI outcomes, the business risks of inaccurate AI, and best practices to ensure your data foundation is solid.
Data Quality: The Foundation of Accurate AI
“Garbage in, garbage out” may sound cliché, but it perfectly captures the reality of AI in finance. The performance and reliability of an AI model hinge on the quality and accuracy of the data you feed into it. Deloitte’s 2025 analysis emphasizes that no matter how advanced the algorithm, flawed or incomplete data will undermine an AI’s output. Gartner experts likewise note that data is an essential asset for training algorithms – and inadequate data quality remains a top barrier to successful AI adoption in finance. In a 2024 Gartner survey of CFOs, finance leaders cited “inadequate data quality/availability” as the number one challenge in implementing AI, even ahead of talent shortages.
The impact of poor data quality is not abstract – it has been quantified. According to Gartner research, organizations in the financial sector incur around $15 million in average annual losses due to poor data quality. This figure represents the cost of operational rework, error correction, and missed opportunities caused by bad data. More broadly across industries, Gartner estimates that bad data costs enterprises roughly $12.9 million each year in lost revenue and inefficiency. It’s no wonder that McKinsey warns 70% of AI projects fail to meet their goals due to issues with data quality and integration. In fact, Gartner projects that by 2025, 30% of all generative AI initiatives will fail specifically due to poor data quality. The message is clear: without a solid data foundation, even the most promising AI finance project is likely to fall short.
Equally important is the quality of knowledge and context built around the data. Domain-specific knowledge acts as the lens through which AI interprets data. In finance, context is everything – whether it’s understanding an invoice line-item description or the significance of a suspicious transaction pattern. AI models augmented with rich domain knowledge and context consistently outperform generic ones. Deloitte recommends developing fine-tuned AI models for specific vertical domains (like finance) to greatly enhance their accuracy and relevance. Training algorithms on domain-specific data and taxonomies equips them to recognize subtle patterns that a one-size-fits-all model might miss. Furthermore, structuring domain knowledge in forms like ontologies or knowledge graphs can reduce ambiguity. By implementing well-defined taxonomies and ontologies to tag and organize financial data, organizations improve the precision of AI context retrieval and inferencing. In other words, a curated knowledge model of financial concepts helps the AI draw the right conclusions, whereas a lack of industry context can lead to misclassification or “hallucinations” in AI outputs. As PwC observed, the expanded use of diverse and complex datasets in AI makes robust data governance and validation practices even more critical to manage these risks. Strong data quality controls and domain context are not just IT concerns – they are strategic assets for ensuring AI delivers trustworthy results.
Real-World Consequences of Low-Quality Data in Finance AI
Poor data quality is not a theoretical problem; it directly causes AI systems to make mistakes that can hurt the business. Nowhere is this more evident than in financial operations, where even a small data error can cascade into a costly issue or compliance violation. Let’s consider a few core areas in financial operations and how bad data or lacking knowledge can derail AI decision-making:
- Invoice Processing and Accounts Payable: AI-driven invoice automation relies on accurate data capture (from OCR or integrations) and correct classification of invoice details. If the underlying data is wrong – say an OCR system misreads a vendor name or a decimal point – the AI could misclassify expenses or approve incorrect payment amounts. A wrong tax code or missing PO reference can result in significant underpayment, overpayment, or delays. Duplicate or inconsistent invoice records are another common data quality issue that leads to duplicate payments. Such errors directly translate into financial losses or the need for manual intervention. High-quality, well-structured invoice data (complete with purchase order references, vendor details, etc.) is crucial for AI to match invoices correctly and prevent costly mistakes.
- Regulatory Compliance and Reporting: Financial institutions operate under strict regulations (e.g. AML, KYC, SOX, Basel accords), and data errors can mean compliance violations. AI models are increasingly used to generate compliance reports or flag risky transactions, but if they are fed incomplete or inaccurate data, the outcomes won’t hold up to regulatory scrutiny. Missing or incorrect data in regulatory filings can lead to fines or sanctions. For example, if an AI system compiling a regulatory report is pulling from siloed systems with inconsistent records, it might omit key transactions or customer information. This not only undermines decision-making but can also put the company in legal jeopardy. As one industry analysis notes, maintaining high data quality is essential for regulatory compliance – errors or gaps can quickly trigger regulatory penalties. In the compliance domain, there is no tolerance for “roughly right” data; it must be precise. Poor data quality in AI-driven compliance checks could mean failing to detect a money-laundering red flag or misreporting capital levels – risks no enterprise can afford.
- Reconciliation and Financial Close: Financial operations involve constant reconciliation of accounts – matching payments with invoices, trades with ledger entries, and so on. When data is inconsistent across systems or departments, AI matching algorithms struggle. A slight difference in how a customer or account is named in two systems can cause an automated reconciliation tool to flag a mismatch (or worse, to falsely assume things match when they don’t). Data discrepancies hinder decision-making and create major inefficiencies in reconciliation. Teams end up spending time manually investigating and cleaning data rather than closing the books. In a fragmented data environment, the promise of a faster financial close via AI may not materialize. Gartner has highlighted that data silos and poorly defined metadata directly reduce AI model accuracy and scalability. In practical terms, if your ERP and banking systems don’t speak the same data language, an AI tool cannot reliably reconcile transactions, leading to delays in financial reporting. Consistent, standardized data (with clear metadata) is necessary for AI to truly automate reconciliation processes.
- Fraud Detection and Risk Management: Many banks and enterprises deploy AI to detect fraud or assess credit and operational risks. The effectiveness of these systems is highly dependent on data quality. As Mastercard’s risk experts point out, the efficacy of AI predictions hinges on the quality of the underlying data. Poor data quality can lead to false positives or false negatives in fraud detection, compromising the system’s effectiveness. False positives (flagging legitimate transactions as fraud) inconvenience customers and waste investigation resources, while false negatives (failing to catch actual fraud) result in direct financial losses. Both outcomes are bad for business and reputation. In the fraud domain, incomplete or outdated data is especially dangerous – for instance, if an AI model hasn’t been updated with the latest known fraud patterns or if it’s missing pieces of transaction context, it may overlook subtle signs of fraud. Conversely, extraneous or noisy data can confuse the model into seeing a problem where none exists. High-quality data (accurate, timely, and rich in relevant features) helps the AI correctly distinguish fraud from normal behavior. The benefits of quality data in fraud detection include faster response times, better identification of evolving fraud tactics, and fewer false alarms that cause friction with customers. In sum, garbage data in means garbage fraud predictions out, which can translate into either fraud losses or customer attrition due to unnecessary transaction blocks.
Across all these examples, a common theme emerges: when AI misfires due to bad data, the business feels the pain in the form of lost dollars, inefficiencies, and risks. Poor data quality can disrupt daily operations and even strategic outcomes. Financial leaders may start to lose trust in AI systems if they observe frequent errors, undermining adoption. The U.S. Government Accountability Office (GAO) cautions that introducing bad data will make an AI model less reliable, and a system that produces errors will also erode trust in the use of AI. This erosion of trust can be fatal for AI initiatives – if end-users or executives don’t believe the AI’s outputs, they will revert to manual processes, negating the ROI of the technology. Clearly, ensuring data (and knowledge) quality is not just an IT task but a business imperative to avoid these pitfalls.
Business Risks and Inefficiencies of Inaccurate AI
The stakes for data quality in AI go beyond individual error incidents. Systemically low-quality data or knowledge will introduce compounding business risks and operational inefficiencies:
- Financial Losses and Error Costs: As noted above, poor data leads to direct monetary losses (e.g. duplicate vendor payments, fraud write-offs). It also incurs significant hidden costs in the form of error correction, customer refunds, and regulatory fines. The average $12–15 million annual loss per firm due to data quality problems underscores how these small frictions add up. Inaccurate AI predictions can cause companies to miss revenue (for example, not collecting the full amount due because of misapplied tax data) or incur avoidable expenses. Bad data essentially acts as a drag on the bottom line, siphoning off value that automation was supposed to create.
- Operational Inefficiency and Rework: Low-quality data can dramatically reduce the efficiency gains that AI promises. Instead of straight-through processing, teams find themselves spending extra time validating AI outputs, reconciling discrepancies, and manually fixing errors. This is the classic scenario of having to “clean up” after the AI when the root cause was dirty input data. Studies have long found that knowledge workers and data scientists spend a majority of their time (up to 80%) on data cleansing and preparation, rather than on analysis or strategic work. In the finance function, if controllers and analysts must devote time to manually correcting AI-generated reports or chasing down data inconsistencies, the whole point of automation is defeated. Poor data quality thus creates a cycle of inefficiency, where humans must double-check AI results at great effort, slowing down processes like monthly close or audit preparation.
- Decision-Making and Strategy Risks: Enterprise decision-makers rely on AI-driven analytics for insights into financial performance, customer behavior, risk exposure, etc. If the underlying data is unreliable, the AI’s analytical insights can misguide strategic decisions. For instance, an automated cash flow forecasting tool fed with erroneous transaction data could lead a CFO to make a poor investment or liquidity decision. Gartner’s concept of pursuing “sufficient versions of the truth” instead of a single absolute source acknowledges that striving for perfection in data is hard, but data must at least be accurate enough to inform decisions usefully. Low-quality data that yields fundamentally wrong conclusions represents a serious business risk – it’s essentially flying blind or, worse, flying with faulty instruments. Ensuring data accuracy and completeness is part of the due diligence for any AI-driven strategy or decision support system.
- Compliance and Reputational Risk: Inaccurate AI outputs in finance don’t just cost money – they can cost reputation and legal standing. An AI that produces an incorrect financial report or fails to flag a compliance issue can put a company at risk of restatements, audit findings, or regulatory penalties. These events erode stakeholder trust and can damage a firm’s reputation in the market. For banks and financial services, reputation is everything; customers need to trust that their bank manages risk prudently. AI misclassifications due to bad data – such as falsely denying a legitimate transaction or approving a risky loan – chip away at that trust. Moreover, regulators are now scrutinizing AI models themselves. Demonstrating data lineage and data quality controls for AI outputs is becoming part of regulatory compliance. If your AI can’t explain itself because the training data was messy or biased, it opens the door to compliance challenges and potential legal liabilities (for example, questions of unfair bias in lending decisions due to skewed data). In short, bad data can make your AI not only wrong but also non-compliant.
Collectively, these risks highlight why enterprise finance leaders must treat data quality and governance as first-class priorities in any AI project. As one Gartner report urged, CFOs should take a direct role in data and AI governance to ensure alignment with business goals. The cost of inaction is not just project failure but tangible business harm. On the flip side, organizations that invest in high-quality data and robust knowledge frameworks stand to gain a competitive edge – their AI systems are more accurate, more trusted, and ultimately more effective at driving value.
Ensuring High-Quality Data and Knowledge: Best Practices
Given the clear link between data quality and AI success, what can enterprises do to ensure their data and domain knowledge are up to the task? Here are some best practices and frameworks for curating, structuring, and cleaning financial data to improve AI outcomes:
- Establish Strong Data Governance: Effective AI starts with good data governance. This means having policies, ownership, and oversight for data quality. Finance leaders should maintain clear oversight of data sources – understanding their origins, ensuring completeness, and validating their reliability. Procedures must be in place to routinely address inconsistencies, duplicate entries, and stale data. Leading organizations set up data governance councils (often led by a Chief Data Officer or CFO) to define data standards and monitor quality metrics. For AI, governance also entails an audit trail: Deloitte recommends implementing data quality controls and archiving inputs/outputs so that every AI decision can be traced back to source data. This not only improves transparency but also helps in identifying and fixing data issues proactively.
- Invest in Data Preparation and Cleaning: There is no shortcut around the hard work of data cleaning. Before feeding data to an AI model – whether it’s invoice images for an OCR engine or transaction logs for a fraud algorithm – invest in preprocessing. This could involve standardizing formats (dates, currencies, account codes), removing or correcting erroneous records, and filling gaps (e.g. using enrichment data for missing fields). Automation can assist here: for example, machine learning can detect anomalies or outliers in data that likely indicate errors. Some enterprises employ data preparation platforms that apply rules and machine learning to continuously cleanse data streams feeding AI systems. The goal is to present the AI with data that is as close to reality as possible. According to American Banker research, “the efficacy of AI predictions hinges on data quality”, so this step dramatically boosts model performance. It’s far more efficient to fix data issues upstream than to troubleshoot AI errors downstream.
- Leverage Domain Knowledge and Contextual Data Models: To improve AI accuracy, incorporate financial domain knowledge into your solutions. This can be done by fine-tuning AI models on industry-specific datasets, as mentioned earlier, which helps the AI learn the nuances of financial language and processes. Additionally, use ontologies and taxonomies to structure your knowledge. For instance, create a financial data ontology that defines relationships (customers, accounts, transactions, invoices, GL codes, etc.) in a consistent way. Gartner and Deloitte have noted that ontologies act as a structured language, standardizing concepts and reducing ambiguities in data interpretation. By tagging data with standardized labels and hierarchies (e.g., expense categories, risk categories), your AI can more precisely contextualize information. This practice reduces context overlap and confusion, enabling more accurate retrieval of relevant data for AI models. In practical terms, a curated knowledge base might help an AI distinguish that “AP” means “Accounts Payable” in one context and “Accounts Policy” in another, based on source or surrounding data. Domain experts should work with data scientists to embed such business logic or reference data (for example, lists of known vendor names, blacklisted entities, regulatory watchlists) into the AI pipeline. This ensures the AI is “speaking the language” of finance from day one.
- Implement Continuous Monitoring and Feedback Loops: Ensuring data quality is not a one-off task – it requires ongoing monitoring. AI systems in production should have metrics and checks to detect data drift or quality degradation over time. For example, if an invoice processing model suddenly sees an uptick in unreadable fields, that’s a red flag to investigate whether new invoice formats are coming in that weren’t accounted for. Regular data audits are a must. Some organizations have instituted “data quality dashboards” that track completeness, accuracy, and timeliness of key data feeds feeding AI models. When anomalies are detected, a data team can intervene to correct the dataset or adjust the model. Moreover, establishing a feedback loop from AI outputs to data inputs is powerful: when an AI prediction is found wrong, analyze whether bad data contributed, and then fix that at the source. Human oversight remains a vital control – as Deloitte stresses, having human review at key points can catch issues and also continually improve the AI. In finance, a common practice is to have a human validate a sample of AI-generated outputs (say, a batch of automated expense categorizations or fraud alerts) to ensure they make sense. This not only catches errors but also yields insights into where the model might need more training data or rule adjustments. Over time, this feedback loop greatly enhances both data quality and model accuracy.
- Adopt an AI Accountability Framework: Frameworks and guidelines are emerging to help enterprises maintain high standards for AI data quality and governance. For example, the GAO’s AI Accountability Framework outlines key practices for ensuring data used in AI is high-quality, reliable, and appropriate for its purpose. Similarly, leading firms like Deloitte have developed “Trustworthy AI” frameworks that include robust data management as a core pillar. Enterprise decision-makers should consider adopting or tailoring such frameworks to their organization. The framework should cover data acquisition (e.g. vetting external data sources for reliability), data privacy and security (especially important for sensitive financial data), as well as bias detection in data (to avoid skewed AI outcomes). A comprehensive approach ensures that from data gathering to model deployment, quality checks and balances are ingrained at each step. Training employees on data governance and AI ethics is also part of best practices – everyone from analysts to executives should understand the importance of data integrity in AI systems. By institutionalizing these practices, companies create a culture that values accuracy and trust in their AI initiatives.
Financial enterprises that follow these best practices are finding that their AI projects deliver more consistent, credible results. For instance, banks that unified their data sources and improved data lineage have seen significant boosts in model accuracy and reduction in reconciliation times. Likewise, organizations that invested in cleaning historical financial data before an automation rollout often report smoother implementations and faster achievement of ROI. The common thread is that clean, curated data combined with domain intelligence leads to AI that the business can rely on. As one finance executive quipped, “AI is only as smart as the data you feed it” – so feed it the cleanest and richest data possible.
Conclusion
In the pursuit of AI-powered automation and intelligence, enterprise decision-makers must remember that AI’s accuracy and value are only as good as the quality of the data and knowledge behind it. This is especially true in financial operations – a domain where precision, compliance, and trust are paramount. Poor data quality and lacking domain context can turn an AI from an asset to a liability, resulting in errors, inefficiencies, and business risks that negate the very benefits of automation. Conversely, high-quality data – accurate, complete, timely, and well-understood – is a force multiplier for AI, enabling reliable predictions and smooth automation of complex financial tasks.
From our experience at Itemize, where we focus on automating financial transaction processing, we have learned that investing in data quality up front pays huge dividends. It means our AI systems can accurately read invoices, reconcile accounts, detect fraud, and more without constantly hitting edge cases or mistakes. Our clients, in turn, gain confidence that the AI is doing the right thing, which accelerates adoption and impact. This echoes the industry findings: organizations that prioritize data quality and governance see far more success with AI than those that rush in with messy data. As Gartner and McKinsey have warned, the margin for error is shrinking in AI projects. In financial services, that margin for error is essentially zero – you simply cannot afford an AI that makes unreliable decisions with money or compliance.
The path forward is clear. Enterprise leaders should champion initiatives to clean and connect their data, enrich it with domain-specific knowledge, and enforce ongoing quality controls. Treat data as a strategic asset and ensure your AI teams and finance teams are in lockstep on data standards. Leverage external expertise and frameworks if needed to benchmark and improve your data practices. When deploying AI, start with use cases where you have strong, trustworthy data and build on early wins. By aligning AI projects with high-quality data and robust knowledge, you set the stage for AI that delivers accurate insights, streamlined operations, and informed decision-making – the very outcomes that justify the AI investment in the first place.
In summary, data quality and domain knowledge are the bedrock of AI accuracy in financial operations. Firms that get this right will unlock the full promise of AI – from cutting costs in accounts payable to strengthening risk management – with confidence that the results can be trusted. Those that neglect it will continue to struggle with unpredictable AI outputs and unrealized ROI. For any enterprise embarking on or scaling up AI in finance, the message is simple: focus on your data (and knowledge) quality now, and the AI accuracy will follow. As the saying goes, take care of your data, and your data will take care of you.
How Itemize Ensures AI Accuracy in Financial Operations
At Itemize, we built our platform with a simple conviction: AI in finance is only as good as the data behind it. That’s why our solutions go beyond surface-level automation. Instead of stopping at header-level capture or summary totals, Itemize AI Agents work at the line-item level, extracting, validating, and enriching transaction data with unmatched precision.
- Curated Financial Data Models: Itemize’s models are trained on billions of financial transactions, invoices, and remittances, ensuring accuracy that generic AI tools cannot achieve
- Domain-Specific Intelligence: Our AI is purpose-built for financial operations – from accounts payable and receivables, to treasury, deposit operations, and compliance
- Continuous Validation: Itemize applies advanced validation, anomaly detection, and enrichment logic so the data feeding your ERP or treasury system is reliable, consistent, and complete
- Trusted Outcomes: Whether it’s eliminating duplicate payments, accelerating reconciliations, or improving fraud detection, Itemize ensures your AI-powered automation delivers the accuracy enterprise finance leaders demand
The result? Higher straight-through processing, reduced operational risk, and AI outputs you can trust – because the data and knowledge foundation is rock solid.
Ready to see how Itemize delivers AI accuracy at scale for banks and enterprises? Request a Demo and discover how we can help transform your financial operations.
