Few trends have received more attention among business leaders in the past decade than innovation. With buzz words like AI and machine learning so often thrown around, it can be hard to tell where advances in new technology and data meet realistic use cases and the resultant impact. Although the promise of a tech-induced business revolution can seem unrealistic, we are beginning to see real progress in the application of technology and the ability to harness new data sets. Increasingly finance and accounting functions will be able to take advantage of new, data-driven opportunities to get an edge. To do this, CFOs will need strategies for data, cloud computing, and talent.
AI is the broader encompassing category of using computer science to receive inputs and make decisions. Machine Learning is a subset of AI that allows the computer to learn from a distinct set of data inputs without explicit programming. Both are being used to develop more sophisticated financial models, including predictive models that enable businesses to better understand market and company signals and even probabilities of potential outcomes. As a result, “data is the new oil;”businesses not leveraging vast amounts of structured, semi-structured, and unstructured content will find themselves running out of fuel to make critical business decisions or advise clients around potential scenarios. With vendors now offering a wide range of datasets that were unavailable several years ago, and alternative data like geo-spatial and satellite imagery reemerging as viable resources to drive differentiable outcomes, executives have never had more information at their disposal. But the question remains: are companies using it effectively? For this reason, executives need to consider a data strategy as critical to the success of their business.
For businesses in highly-cyclical industries, particularly those impacted by fluctuating commodity prices, accurate forecasting of macroeconomic indicators like GDP, consumer price index, or housing starts, is critical. Given these leading indicators are published with a monthly or quarterly cadence, firms have limited reaction time to adjust strategic direction. As such, CFOs may seek analytical methodologies to get early warnings in anticipation of macroeconomic environmental changes. This enables proactive decision making around risk exposure, asset sales, capital investments, and activity forecasts. By combining internal, proprietary company data with external data sources, AI, and machine learning could generate alerts around potential macro changes.
Most finance and accounting functions will focus on machine learning techniques given the nature of the structured datasets they work with, including general ledgers, structured accounts, and labeled transactions. Finance teams could apply machine learning to develop mathematical formulae like algorithms and models which could leverage historical financial and market data to analyze and interpret data sets that could lead to predictions, recommendations, or clustering. Given the current intersection of financial services and technology, there is an opportunity for finance and accounting teams to harness technology to significantly generate revenue growth, reduce costs, and improve business operations. Specifically, teams could improve a business process through modernization, generate deeper business intelligence and data insights, and increase business influence.
"By combining internal, proprietary company data with external data sources, AI, and machine learning could generate alerts around potential macro changes"
Amidst these opportunities, it is easy to forget how we got here. Simply put, cloud technology is the bedrock of these gains; without it, we would not see as widespread a paradigm shift in business models, technology infrastructure, and data management to discuss. Widespread use of sophisticated technology like AI and machine learning is possible only because the cloud can accommodate large data sets securely and cost-effectively. The cloud offers several benefits, including enabling faster iterative innovation and more compute power for AI and machine learning opportunities. While there has been ample hype around the application of AI and machine learning to financial problems, demonstrable successes on a range of use cases including automation of order execution, pricing, risk management, pattern recognition, portfolio construction, bet sizing, and asset allocation mean financial executives are taking the opportunity seriously. The cloud also enables AI and machine learning to complement human subject matter experts around use cases like credit rating analyses, scoring, fraud detection, sentiment analysis, and recommendation systems.
As a result, more sophisticated cloud strategies are becoming necessary, and many financial services institutions are migrating away from on-premises solutions. Data volume scaling requirements, database corruption mitigation, and cost-savings are part of the rationale. Third parties offering more cost-effective outsourced hardware and software solutions are driving the adoption of products and services that used to be considered nascent but now are necessary. Technologically sophisticated institutions are going a step further, increasingly embracing multi-cloud strategies that involve more than one cloud provider to meet specific business-driven use cases. Tied to these moves are growing concerns around data privacy, which has increased the importance of data protection and governance. As a result, cybersecurity technology–including experimentation around cryptography–continues to develop and be applied at a rapid pace.
Lastly, no conversation about AI and machine learning would be complete without consideration of the human element. As new technologies rapidly evolve within the technology ecosystem, the competition for talent is fierce and the skills required for success have shifted. Not only are financial institutions competing to attract the best talent, but they are also competing with large technology firms for individuals with the technical subject matter expertise around data science, AI, machine learning, and developer skills. This is even more acute when identifying individuals who also have the necessary financial markets experience. Business acumen, financial markets knowledge, and diverse programming background are highly sought after skill sets.
We can all agree that advances in data and technology have come a long way. The question remains whether organizations and their executives can reorient themselves to take advantage of it.