While AI and machine learning are two technological prongs that work together to speed up financial processes with greater accuracy, there are key distinctions between their individual roles in accounting. AI encompasses everything from rule-based systems to advanced algorithms that can perform reasoning-based tasks, solve problems, and make decisions, generating human-like text and responding conversationally. On the other hand, machine learning, a special subset of AI, is mainly focused on creating systems that learn from data, improving their performance over time. Machine learning systems identify patterns in historical data and use these patterns to make predictions about new data.
For bank reconciliation, this is a specifically relevant distinction.
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Traditional non-AI approaches can use predefined rules and logic to match transactions
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Machine learning approaches actually learn from patterns observed in reconciliation processes from the past. These patterns are identified from specific sets of data from each client.
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Machine learning systems enhance their accuracy over a period of time, as they work on processing an increasing number of transactions and get feedback based on their performance.
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Machine learning delivers more predictable, statistically-founded outcomes than generative AI systems.
When considered practically in terms of bank reconciliation, machine learning is specifically well-suited for reconciliation, as it excels at pattern recognition in structured data. This is the exact capacity that comes in handy while matching bank transactions to accounting records. Broader AI applications can use varied sources of data and different approaches for reconciliation. However, machine learning particularly focuses on learning from the actual reconciliation history of the clients.
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