Author: Ante Spittler, Co-founder and CEO of spend management platform Moss
The benefits of Artificial Intelligence (AI) extend across almost all business functions, but its value for the finance function is particularly striking. In particular, the application of AI in finance heralds a new age of automated workflows, advanced security and fraud detection, and improved compliance with legal and accounting regulations.
Finance professionals have traditionally taken a cautious and conservative approach to new technologies and processes, and a recent report by Gartner shows that over half (61%) of organisations’ finance functions either have no plans for AI implementation, or are still in the initial planning phase. This slower adoption makes sense given the fact that finance teams operate in a heavily regulated environment. The approvals process for adopting new technology can be lengthy, with concerns around cost and potential disruption from integrating new solutions.
However the capabilities and benefits that AI offers finance teams are now evident and indisputable. Those businesses which choose not to utilise AI risk exposing themselves to greater risk of fraud, reducing the operational efficiency of their finance teams, and ultimately limiting their company’s competitiveness in a cut-throat market.
Below are three tangible benefits of AI for finance teams:
Benefit one: Saving time by automating manual workflows
Finance departments are accustomed to high-stakes, repetitive, and process-driven workflows. This is precisely why finance teams would benefit from automating manual tasks. Imagine the time savings if tasks like extracting information from invoices, automating book keeping entries, and matching expenses to payments were supported by AI.
Finance teams would be elevated from ‘administrative work, and instead offer greater strategic, high-value work to the senior team – something more than a quarter of finance professionals would prefer, according to recent research.
Of course, automation has already started streamlining workflows and freeing up precious time for finance teams. Optical Character Recognition (OCR) automates data extraction from financial documents like invoices and receipts, transforming them into digital formats. The automated data extraction process not only eliminates the need for manual data entry, but also drastically reduces the likelihood of errors, ensuring accurate and efficient handling of financial information.
To date, more than two million receipts in the Moss platform have been digitised via OCR, with millions of fields being pre-filled automatically, rather than being manually inputted by hand. The precision rate is more than 90%, potentially freeing up hundreds of hours per quarter, per customer.
Machine Learning (ML) goes a step further in automating routine tasks. ML models in accounting can be trained on the specifics of individual customers, and therefore pre-fill accounting information with an accuracy comparable to humans. Moss has implemented ML models for customers which pre-fill 90% of fields with 99% accuracy. While the ML model handles the bulk of routine data entry with remarkable accuracy, the finance team can close books faster and focus on analysis, problem-solving, and the critical task of a final human review.
Benefit two: Advanced controlling and oversight
Recent advances in Large Language Models (LLMs) have opened new possibilities for AI applications in finance. LLMs are a transformative technology; they understand, interpret, and generate human language based on huge data sets. Insights from vast amounts of data can be extracted within a short time frame, which is enormously valuable in finance where timely and accurate information plays an important role in decision-making processes.
One clear application of LLMs is via AI-powered chatbots. These advanced AI systems can understand customers’ data contextually, and effectively act as a virtual assistant to finance professionals. LLMs are equipped to answer simple and complex queries quickly, and with ease. Questions like ‘How much did we spend on marketing last quarter?’ or ‘How many transactions deviated from our policy guidelines?’ can be promptly and accurately addressed, providing instant insights and facilitating more informed decision-making.
As organisations increasingly grapple with the complexities of compliance, regulatory frameworks, and internal policies, the integration of LLMs could significantly streamline processes. Imagine a scenario where companies could simply upload their spend policy documents. Advanced LLMs analyse the content, extract key information and nuances, then propose not just standardised rules, but rules tailored to the specific operational environment of the company. This level of automation and customisation saves enormous amounts of time whilst bolstering security, highlighting the stark benefits of AI when it comes to policy management.
Benefit three: Improving fraud detection and security
Fraudulent or malicious financial activity is a growing risk for businesses. Research shows that invoice fraud leads to losses of £300,000 per business in the UK each year. During the first half of 2022, there were more than 4.5 million compromised payment cards for sale on underground credit card markets.
AI and ML models are important tools that help finance teams analyse transactional data in real-time, and detect unusual patterns which may indicate fraudulent behaviour. This dynamic learning approach ensures that the systems are always equipped with the latest data and trends, significantly bolstering their accuracy and responsiveness in combating financial fraud.
This is particularly relevant in the realm of credit card fraud detection, where the stakes are high, and rapid response is essential. AI and ML models excel in this domain by continuously learning and adapting to evolving patterns in transactional data. By leveraging sophisticated algorithms, these systems can discern normal spending behaviour from anomalies that may indicate fraudulent activities.
The “Superman Rule” is a straightforward yet effective heuristic used in fraud detection, particularly in the context of monitoring financial transactions. This rule is based on the logical assumption that an individual cannot physically cover vast distances within an implausibly short timeframe, unless, of course, they possess superhuman abilities akin to Superman. Applied to corporate credit card transactions, the “Superman Rule” helps identify potentially fraudulent behaviour by flagging instances where transactions occur in geographically distant locations within an unrealistically brief time span.
While the “Superman Rule” is a simplistic example, it underscores the power of automated rule-based systems and their ability to quickly identify patterns indicative of fraudulent activity.
The enormous potential of AI for finance professionals is clear to see, with unprecedented opportunity to enhance decision-making processes, optimise operations, and mitigate risks in the complex world of finance. As finance professionals harness the power of AI, they stand to unlock new levels of efficiency, innovation, and competitiveness, all of which are crucial in the context of challenging economic circumstances. And while AI will undoubtedly revolutionise finance processes, it must be remembered it will remain a tool for humans; the human element will remain indispensable for the critical task of interpreting nuance, exercising judgement, and providing final sign-off and review.
