The Allure of AI in Reconciliation: Does AI Solve Recon Automation?

This is the first article in our series on AI Reconciliation! Subscribe for the rest of the series!

Banks are pouring billions into AI, yet up to 80% of a data scientist’s time is spent just preparing data (more on that later—either way, it’s a lot of time).

This contrast underscores the messy reality behind the AI hype in financial operations.

Reconciliation, a critical process in finance, is often touted as ripe for AI and machine learning (ML) transformation. The promises are grand: AI will automate matching, eliminate human effort, and revolutionize the workflow. But how much of this is achievable, and how much is mere hype?

So, let’s examine these questions. In this article, we’ll dig into the current state of AI in financial reconciliation, exploring its strengths, limitations, and the pragmatic steps firms can take to leverage AI effectively.

The Allure of AI in Reconciliation

Reconciliation involves matching financial records from different sources to ensure accuracy and consistency. Traditionally, this process has been manual, time-consuming, and prone to errors. Many firms still rely on Excel macros and manual checks; in fact, as of 10 years ago, about 90% of companies used Excel in reconciliation.

The explosion of data in finance has exacerbated these challenges. By 2025, IDC estimates there will be 175 zettabytes of data globally, with 80% of that data being unstructured. This deluge includes various transaction types and real-time demands, making manual reconciliation increasingly untenable.(Forbes)

AI offers a compelling solution. Its theoretical benefits for reconciliation include:

• Processing vast datasets rapidly, identifying patterns at scale.
• Learning from historical matches to improve accuracy over time.
• Handling unstructured data, such as reading documents and emails.
• Operating continuously without fatigue, potentially reducing backlogs. (I’m assuming you know all of the general AI selling points, but if not, take a look at this article from Oracle)

In a separate blog, Oracle notes that AI can "uncover deviations in large amounts of data quickly," highlighting its potential in anomaly detection (Oracle Blogs). Continuing this theme, a McKinsey study found that AI could cut finance operation costs by up to 30%.

Okay, so AI solves every problem? Not quite. Looking at the long list of advertiser-written selling points, it's easy to see why AI is hailed as a game-changer in financial reconciliation. However, the reality is more nuanced.

Where AI Delivers in Reconciliation

Advanced Matching and Pattern Recognition

AI and ML can transcend traditional rule-based matching by identifying complex patterns and relationships in data. For example, a machine learning model trained on past reconciliation data might recognize that "INV-12345" in one system and "Invoice 12345" in another refer to the same item, despite different formats. AI can also handle typos, abbreviations, and fuzzy matches by understanding context, thereby improving match rates on challenging data.

As part of our Intelligent Reconciliation platform, our users are successfully leveraging this exact type of flexible pattern matching on our platform—and it works.

These types of matches are hard to accomplish using rules. They generally involve cleaning data through transformation prior to matching. A survey by CrowdFlower found that data professionals spend 60% of their time cleaning data, with 76% finding it the least enjoyable part of their work. While the validity of this survey has been called into question, there is certainly a significant resource spend on data cleaning (at least 25% according to another survey by Figure Eight). AI can alleviate this burden by automating much of the data cleaning and matching processes. But is distance from data necessarily a good thing? An interesting blog by Leigh Dobbs in Lost Boy suggests that it is not. Something we strive for in building systems in Boagent is to save time, while exposing original data. Our view is that the best data automation process helps a human-in-the-loop, without obfuscating raw data (anything the Agent does is plainly visible to users, and raw files are viewable, filterable, and groupable right on screen).

Handling Unstructured Data

Unstructured data, such as trade confirmations, bank statements, and emails, poses significant challenges for reconciliation. AI “Computer Vision” techniques like Optical Character Recognition (OCR) and Natural Language Processing (NLP) can extract relevant information from these documents. For instance, an AI document parser can read a PDF confirmation, extract trade details, and feed them into the reconciliation system—tasks that previously required manual input.

Considering that 80% of data is unstructured, this capability is crucial. JPMorgan's COiN platform exemplifies this, reviewing legal documents in seconds and significantly reducing manual workload. This tech is mature in development but early in implementation. It truly does work, and implementing it can be a green mark on anybody’s career. Anecdotally, our users are delighted at how well it’s working for them.

Anomaly Detection & Exception Prioritization

AI can flag unusual discrepancies that traditional rules might miss. For example, AI-driven anomaly detection might identify that all unmatched trades on a particular day involve a specific counterparty, indicating a potential data feed issue. AI can also assign confidence scores to matches, allowing ambiguous cases to be reviewed by humans while automatically resolving straightforward ones. To me, this is one of the most interesting cases for Agentic automation, and it’s something we are actively working on at Boagent.

These strengths demonstrate that AI is already delivering tangible benefits in specific areas of reconciliation, particularly where patterns are complex or data volumes are high. But it’s not all sunshine and roses.

The Limitations and Pitfalls of AI in Reconciliation

Dependence on Data Quality

The adage "garbage in, garbage out" holds true for AI. AI models learn from historical data; if that data is inaccurate or biased, the AI will perpetuate errors or produce false matches. Data scientists spend around a significant portion of their time preparing and managing data, highlighting the importance of data quality. (The Banker, DATAVERSITY)

If you can’t aggregate good data, an AI won’t help. But code-free data connectors for databases, APIs, and FTPs might.

AI is Not Plug-and-Play

Implementing AI requires training, tuning, and ongoing maintenance. An ML model doesn't instantly understand your business rules; it must be trained on historical reconciliations. If your data history is limited or rapidly changing, the model might struggle.

Moreover, running AI on large datasets isn't free; it demands infrastructure or cloud services, adding cost and complexity. Dell's research revealed that "data overload and the inability to extract insights from data" is a significant barrier to digital transformation. (Dell)

For many midsize and smaller organizations, this makes onboarding AI workflows internally impossible, requiring outside specialized software providers.

Explainability and Trust

In finance, transparency is paramount. If an AI matches two records or clears an exception, stakeholders need to understand why. Black-box AI models can erode trust, especially if errors go unnoticed or are rubber-stamped by an overzealous algorithm. This is why many firms are cautious about fully automatic AI decisions in reconciliation, preferring AI-assisted processes with human oversight.

At Boagent, this is core to our philosophy. Nothing AI does is hidden or final. Every configuration or decision is logged and transparent.

Not a Complete Replacement for Rules

Certain straightforward reconciliation logic is often better handled with deterministic rules, which are 100% precise for known patterns. AI complements but doesn't replace rule engines in all cases. For example, if two systems have slight format differences, a simple script or regex might suffice, eliminating the need for ML. ML also takes time. In some cases, we find ML based matches are up to 6X slower than equivalent rule matches. These are extreme cases, but they do exist.

The best systems use a hybrid approach: rules for what rules do best, ML for the hard cases. In an ideal system, AI defines rules for user approval, while ML matches hard cases. This is essentially our two-prong definition of Intelligent Reconciliation: ML matching for extreme cases, AI rule definition for general matching and reconciliation—with a human-in-the-loop to validate everything before deploying to production use.

Case Study of Caution

Consider a large bank that implemented an AI reconciliation tool, expecting to eliminate manual work. After six months, they found only modest improvements in match rates due to underlying data quality issues. The AI was making educated guesses on flawed data, leading them to first consolidate data sources and standardize formats before reapplying ML.

Expert opinions echo this sentiment. Gartner reports that only 54% of AI projects make it from pilot to production, often due to data and integration challenges (LinkedIn). Whether you’re implementing a project from scratch internally or working with external tooling, always make sure that your team or your support is available to deal not just with algorithms but also with inputs and outputs. What else should you keep in focus? Let’s cover that next.

Bridging Hype and Reality: Leveraging AI Successfully

Start with Solid Foundations

Before applying AI, ensure your reconciliation process design and data quality are robust. AI should enhance, not repair, fundamentally broken processes. Strong data governance is essential, as emphasized by regulatory frameworks like BCBS 239.

Use AI in Targeted Ways

Start with the lowest hanging fruit: un-automatable one-off or low volume reconciliations where you might still be using Excel. Have the AI set up rules and iterate on them. Look into its setup and results—often our one-click recons succeed on the spot. Sometimes you might need to tweak the setup, but it still beats a fully manual setup.

Click to see how this looks like in Boagent.

Here you can get a feel for what the AI does well and what it does not. For big processes, focus AI efforts where they add the most value: high-volume, low-value-add tasks like parsing descriptions, unstructured data ingestion, and analyzing exception patterns. Apply AI like a scalpel, not a hammer. In a later article, we will discuss AI unstructured data injestion, Regex vs ML-match, and automated exceptions analysis.

Human-in-the-Loop as Safety Net

Keep humans in the review loop. Efficiency cannot replace humans, it can only enhance our conclusions and speed to conclusion. Allow AI to make suggestions, such as auto-matching, rule-construction, and labelling, but have analysts confirm critical outputs in production. This ensures oversight and builds trust in the system's recommendations, putting you on the path to a truly hybrid human-AI operations approach.

Measure and Iterate

Use metrics to evaluate AI's impact—match rate improvements, reduction in manual hours, etc. If progress stalls, investigate whether data issues or model limitations are to blame. An iterative mindset is key to successful AI integration.

Don't Neglect Process Re-engineering

Sometimes, the most significant gains come from rethinking the reconciliation process itself. Implementing earlier data validation to prevent breaks can be more effective than applying AI to fix them later.

Conclusion

AI holds significant promise for transforming financial reconciliation, offering efficiency gains and the ability to handle complex data. However, realizing these benefits requires careful planning, quality data, and a balanced approach that combines AI with human expertise and traditional methods. As the financial industry continues to evolve, organizations that thoughtfully integrate AI into their reconciliation processes will be better positioned to navigate the complexities of modern finance. If you have any questions, don’t hesitate to reach out to the team at Boagent. Our team regularly save clients a lot of time, money, and headache in both traditional and next-generation reconciliation automation.

Stay tuned for our next article in this series, where we'll dive into how AI solves unstructured data.