AI is the game-changing technology of our time, and for finance teams, there’s one function in particular that’s the perfect use case for it – payment reconciliation.
For businesses handling a high volume of transactions and complex finance operations, reconciliation has never been more challenging, time-consuming, or labor-intensive.
That’s because finance teams today are inundated with a tsunami of unstructured data from a wide variety of sources including banks, PSPs, billing systems, and databases.
Exceptions are the norm, the matching scenarios are complex, and reporting is wildly inconsistent, all of which make simply closing the books and maintaining accurate ledgers a Herculean challenge – especially given the fact that most finance teams manage it all manually in Excel.
But dealing with massive amounts of unstructured data is exactly where AI shines.
Here are 7 specific ways AI can help finance teams manage complicated, high-volume reconciliation with simplicity and ease.
1. Harness LLMs to automate transaction matching
(Rather than spending hours manually cross-referencing transactions across multiple sources and platforms)
The challenge: When reconciling transactions, it’s par for the course that finance teams will encounter inconsistent transaction descriptions, missing references, and variations in data formats across different systems.
This can make reconciliation an absolute nightmare, requiring you to roll up your sleeves and dive into the weeds, manually searching for the unique identifiers or hidden clues that explain each discrepancy.
How AI can help: Large language models (LLMs) can revolutionize transaction matching by understanding and interpreting high volumes of complex, ambiguous, and unstructured financial data. AI relies on contextual clues and patterns to handle common scenarios such as:
- If a payment is labeled differently across different platforms, the LLM can understand abbreviations and contextual similarities and recognize that “Invoice 12345” and “INV12345” refer to the same invoice
- If a customer’s name is misspelled in the transaction description, the LLM can leverage its knowledge of common misspellings to identify that “John’s Supplies’ and “Jhon’s Supplies” are probably the same entity
- If a transaction lacks a unique identifier but includes partial information like date, amount, and customer name, the LLM can analyze all available data points and understand the relationships between them to infer matches
2. Streamline data ingestion, cleansing, and normalization with AI
(Instead of correcting endless formatting issues to standardize data from your PSPs, banks, and billing systems)
The challenge: Finance teams often deal with vast amounts of unstructured and inconsistent data from multiple sources such as banks, payment service providers, billing systems, and internal databases.
Before reconciliation even begins, teams are forced to manually clean, map, and normalize this messy data, which is filled with errors, inconsistencies, and varying formats, rendering ingestion and processing a time-consuming and error-prone process.
How AI can help: A reconciliation tool that leverages machine learning algorithms can automate data cleansing by identifying and correcting errors, filling in missing values, and converting data into consistent formats.
AI can also map disparate data fields from different sources to a unified schema, which makes data integration a much smoother and less painful process. For example:
- AI can parse through unstructured documents and fields like bank memos to extract relevant financial data automatically.
- Machine learning models can detect duplicate entries or outliers and correct them based on learned patterns.
- AI can standardize data formats, like converting dates from various formats to a single standard or normalizing currency values across different financial statements.
3. Unlock intelligent rule suggestions with AI to adapt to your ever-changing business needs
(Rather than trying to remember if a transaction should be split across cost centers or chasing complex rules for partial payments)
The challenge: Reconciliation often relies on a complex set of matching rules to identify corresponding transactions across different systems. Creating and maintaining these rules manually is labor-intensive and requires deep domain expertise.
Moreover, static rules may not adapt well to new transaction patterns or exceptions, leading to increased mismatches and exceptions that need manual review.
How AI can help: AI can analyze historical transaction data to learn patterns and suggest new or refined matching rules, and machine learning algorithms can identify which attributes are most effective for matching and can adapt to emerging patterns over time. For example:
- AI can propose matching rules based on combinations of transaction attributes like amount ranges, dates, customer names, or transaction types.
- As new transactions are processed, the AI system learns from any mismatches or manual corrections to improve its rule suggestions.
- AI can identify common causes of exceptions and suggest rules to automatically handle them in the future.
By leveraging AI for rule suggestions, finance teams can improve match rates, reduce manual interventions, and keep their reconciliation processes agile and up-to-date.
4. Uncover and troubleshoot anomalies with AI-powered detection
(Instead of trying to spot a one-off fraudulent charge or manually tracking down why a PSP suddenly charged you an unexpected fee)
The challenge: Discrepancies happen all the time, whether it’s one-off errors, fraud, or systemic issues like unexpected fees or tax discrepancies. While manual reconciliation processes can detect these issues, it’s still on finance teams to explain why the discrepancies occurred, leading to lengthy and often tedious investigations.
How AI can help: AI-powered anomaly detection systems can not only spot irregularities but also analyze and explain the underlying causes. By processing vast datasets, AI can uncover hidden patterns and correlations. For example:
- AI can detect that a series of unmatched transactions share common attributes, such as all being related to a specific customer or occurring on the same date.
- If reconciliation rates drop, AI can analyze exceptions and identify that many are due to new service fees introduced by a payment processor.
- AI can anticipate potential anomalies by learning from historical data, alerting teams to transactions that deviate from expected patterns.
5. Automate report generation with AI for audits and compliance
(Rather than painstakingly extracting data from multiple systems, formatting it into custom templates, and then triple-checking for errors or missing figures)
The challenge: Generating detailed financial reports and audit artifacts is a critical but labor-intensive task. Each stakeholder requires working papers in specific formats with precise data presentations.
Manually compiling these reports is time-consuming and increases the risk of errors or inconsistencies, which can lead to compliance issues or misinformed decision-making.
How AI can help: AI can automate the generation of customized reports by pulling data from various sources and presenting it in the desired format. Natural Language Generation (NLG) technologies can even create narrative explanations to accompany data tables and charts. For example:
- AI can use custom formatting to produce reports that match the specific templates and structures expected by different stakeholders.
- Automated reports can be refreshed instantly as new data becomes available, ensuring that all information is up-to-date.
- AI can generate visualizations and summaries that make complex financial data more accessible to non-financial stakeholders.
6. Forecast cash flow and simulate scenarios with AI insights
(Instead of scrambling to adjust cash flow projections after unexpected expenses hit, like surprise fees or a major customer delaying payment)
The challenge: Cash flow forecasting is a critical exercise but is constantly complicated by fluctuating revenues, changing expenses, and market volatility. When working in spreadsheets, as most finance teams do, this can lead to inaccurate projections, inefficient capital allocation, and even cash shortages.
How AI can help: AI can enhance forecasting by analyzing historical data alongside real-time market information to predict future cash flows with higher accuracy. Machine learning models can simulate various scenarios to assess potential impacts on cash positions and overall business performance. For example:
- AI can use predictive modeling to forecast cash inflows and outflows based on trends in sales, receivables, payables, and other financial activities.
- Finance teams can use AI to simulate the financial impact of different strategies, such as adjusting credit terms or altering investment plans.
- AI can identify potential cash flow risks, like delayed customer payments or increased supplier costs, allowing teams to plan accordingly.
7. Gain proactive insights and alerts with AI for strategic decision-making
(Rather than manually checking exchange rates every day, worrying that a sudden currency fluctuation will blow up your budget)
The challenge: In fast-paced business environments, staying ahead of potential financial challenges is crucial. However, manually monitoring all the variables that could impact financial health — such as currency fluctuations, market trends, or operational inefficiencies — is nearly impossible when you’re confined to Excel.
Without timely insights, companies may find themselves facing cash shortages, overexposure to risk, or declining returns on investments.
How AI can help: AI systems can continuously analyze internal financial data and external factors to provide real-time insights and proactive alerts. These systems can recommend actions to mitigate risks and capitalize on opportunities. For example:
- AI can notify you if there's a significant chance of a cash shortfall in the near future, suggesting actions like reducing discretionary spending or securing additional financing.
- If your expenses are in one currency (say, Euros) and revenues in another (such as US dollars), AI can monitor exchange rate trends and alert you to hedge against currency risk.
- AI can track the efficiency of spending in areas like marketing or payroll, highlighting when costs are increasing faster than revenue growth or when the return on investment is diminishing.
These insights enable finance teams to act swiftly, adjusting strategies before issues escalate, and ensuring the company's financial stability and growth.
Final thoughts
By leveraging an automated, AI-powered reconciliation tool like Ledge, finance teams can transform the reconciliation process from a tedious, manual chore into a streamlined, intelligent operation.
AI not only handles the heavy lifting of data processing and analysis but also provides valuable insights that drive strategic decision-making, ultimately contributing to a more agile and resilient financial function.
With AI taking over repetitive tasks and reducing human error, finance teams can focus on higher-value work like identifying growth opportunities, mitigating risks, and optimizing financial strategies. The result is not just faster reconciliations but smarter, data-driven decision-making that strengthens the entire organization.
