Autonomous Finance: Six Essential Digital Capabilities in Financial Sector
Updated: Mar 13
"Autonomous finance is the organic convergence of all the technology innovation we’ve been seeing over the years, from AI to unprecedented access to data, to finally deliver on self-driving finance." Rachid Molinary, senior vice president of digital strategy and innovation at Spain’s Banco Popular
Financial leaders have grand digital objectives but an obsolete understanding of what is feasible with modern technology. In order to assist finance professionals in resetting their view of digital potential early in their digital journeys, this articles highlights six essential digital capabilities.
Financial Leaders Will Continue Their Journey Toward Autonomous Finance in 2023
According to Gartner, in 2023 and beyond, financial leaders will continue their journey toward autonomous finance, particularly as the economy weakens. Industry leaders accelerate in downturns by boldly investing in initiatives that matter, according to lessons learnt from previous recessions. These investment choices currently revolve entirely around digital. These priorities are a reflection of how the function is evolving to provide enhanced real-time and predictive insights, easy compliance, and more flexibility in finance strategy. This will enable fresh opportunities to boost profitability while also preparing the company for economic turbulence.
Few CFOs are moving toward autonomous finance, despite the majority of respondents (64%) believing it would be a reality within the next six years. Only 21% of companies, for instance, are utilizing blockchain, 12% are employing natural language processing, and 19% are using prescriptive analytics. Finance leaders need to be aware of their investing opportunities if they want to speed up investments in digital technologies. In order to provide finance professionals with a comprehensive knowledge of their digital potential, we will discuss six essential digital capabilities in the financial sector.
Advantages Using Autonomous Finance
Autonomous finance offers several advantages, including:
Improved accuracy: Autonomous finance systems use advanced algorithms and machine learning techniques to analyze vast amounts of financial data, which can lead to more accurate predictions and better decision-making.
Increased efficiency: Automated financial processes can reduce the time and effort required to manage finances, freeing up more time for other activities.
Better personalization: Autonomous finance systems can analyze an individual's financial situation and provide personalized recommendations tailored to their specific needs and goals.
Cost-effective: Autonomous finance systems can be less expensive than traditional financial services, as they eliminate the need for human intermediaries and can be accessed through low-cost digital platforms.
Accessibility: Autonomous finance can expand access to financial services for underserved populations, as it can be accessed from anywhere with an internet connection.
Security: Autonomous finance systems can incorporate blockchain technology, which provides an immutable ledger of all financial transactions, making them more secure and transparent.
Six Essential Digital Capabilities in The Financial Sector
Without initially being aware of their investment opportunities, finance leaders cannot speed up the investment in digital technology; they must be up to date on what is now feasible. To assist finance professionals in better understanding their digital investment opportunities, here are the six essential digital capabilities to implement to achieve the autonomous finance.
Financial Process Automation Powered by Robotic Process Automation (RPA)
RPA is utilized in both transactional and judgment-based financial processes to automate repetitive, highly manual procedures. The time spent by financial employees on highly manual labor can be significantly decreased by using RPA to automate some manual operations. The following five procedures can be automated best with RPA: reporting, alerting, migration, validation, and computation. Each time, a software bot is built to execute operations that are extremely repetitious at a significantly faster and error-free pace. Due to the ability to monitor and assess RPA bots' behavior for compliance, automating such operations can also enhance control. The fact that only one or two robots are required for RPA to begin producing a return on investment makes it one of the most popular uses of digital technology in the banking industry.
According to Gartner, RPA-driven process automation is being deployed or has already been implemented in 45 percent of finance functions, while just 13 percent are not currently pursuing it.
Automating Everyday Business Decisions
Human subjectivity and bias are present in a large number of manual, everyday business decisions. This subjectivity is reduced when routine business choices are made using digital technologies. RPA bots are typically taught decision rules to automatically apply a certain action to a given item in the majority of scenarios of routine business decision automation. Routine business decision automation frees up time formerly spent on relatively basic decisions, which enables finance to swiftly advise on complicated issues as customers' expectations for shorter decision times rise. Of now, the following use cases are most common:
Automated credit decision-making. Set criteria for an automated rejection or acceptance based on the company's level of acceptable risk. These rules will regulate the credit decision-making process. Only judgments about credit are made by finance employees when the automated process is unclear.
Automatic cash allocation. Reduce days sales outstanding (DSO) and shorten the average collection period by automatically reading, matching, and recording open items against payments received in real time with accounts receivable (AR) software.
Automating intelligent (hyper)finance processes
In order to better execute complicated finance operations, intelligent (hyper)finance process automation uses a combination of automation methods (usually RPA and ML are combined). Machine Learning (ML) models should be used for business processes that have the potential to enhance and result in significant cost savings or revenue impacts because they are particularly good for forecasting a quantity or a category.
Developing an ML solution is easier than it seems, despite the fact that some finance chiefs might believe ML is excessively resource-intensive and requires strong computer science abilities beyond their finance employees. Choosing a precise business prediction, collecting historical data, and utilizing open-source ML tools are the main requirements. For the purpose of making predictions for the future, the ML model is trained using historical data.
Basic Machine Learning Pilots as an Example
Iron Mountain used machine learning to create a targeted customer treatment strategy when late customer payments hampered finance from generating accurate estimates and plans based on projected income. By estimating the likelihood that a client will pay an invoice late, it first discovered a chance to enhance its A/R process. Second, it gathered the client information necessary for an ML training set. The trained ML model was then deployed. Finally, it focused direct involvement on high-risk, high-value customers. As a result, the settlement time for accounts included in the ML pilot was cut by 40%. For high-risk consumers, the average time to settle dropped from 68 days to 40 days.
Business Decision Advancement
Business decision enhancement is being introduced or has already been deployed in 14% of financial functions. Business decision enhancement, like routine decision automation, lessens human bias in business judgments. Instead of RPA bots, it usually leverages AI to support more complicated decisions that demand high levels of judgment. For instance, while RPA bots may answer questions with a simple "yes" or "no," AI's self-teaching capabilities can produce more individualized responses based on vast amounts of data. Business decision enhancement is especially helpful when rapid or extensive categorization and prediction are required, and it is most likely to be beneficial when data are well-defined and of high quality.
A good example is the pricing simulation tool (Food and Beverage Company). An AI-enabled simulation tool was used by a brewery to help decision-makers assess the anticipated effects of different pricing strategies in various international markets. In order to comprehend the relationship between pricing and demand in various markets, the brewery first conducted an effect evaluation. Second, the brewery made use of a simulation tool powered by AI that applied discrete choice modeling strategies and data from economic and demographic indicator sources. This made it possible for decision-makers to experiment with and see how a proposed pricing plan would affect sales as well as sales that were lost to rivals. The brewery found opportunities to increase prices by 16%, which could increase revenue by $250 million.
Analysis and Decision Support with AI
Finance is now able to automatically generate analysis that is more complicated than the reports generally offered by RPA bots thanks to AI-assisted analysis and decision support capabilities. In order to achieve this capability, financial functions may need to use a variety of AI tools in a multistep procedure. For instance, a tool that can repair inconsistent and unstructured data, identify data trends by correlating and categorizing data, create suggested remedial measures, or do all of the above would be needed by the finance industry. By allowing financial experts to concentrate on more practical decision assistance and more individualized analysis, this capacity can considerably improve the self-service offers in finance.
A prime example is trend analysis powered by machine learning (UBS). To assess employee cost data and automate associated reporting, UBS employs AI and Natural language processing (NLP). First, ML automatically fixes inconsistencies found in a variety of often unstructured employee travel receipts. Second, more ML finds spending trends in the cleansed data and automatically adds them to a presentation that is visually appealing for stakeholders. In order to summarize the trends, NLP generates summary text. UBS is able to bargain better booking rates and travel arrangements with suppliers and agencies around the world because it has a more thorough and immediate grasp of its employees' travel activities and needs.
Nudges, originally developed as a behavioral economics concept, are subdued cues intended to influence people's behavior in a predictable manner without restricting choice or introducing new financial incentives. Nudges are a potent tool for finance operations that can be used to discreetly or indirectly urge staff to make "better" (more financially sensible) decisions. When finance staff is unable to give this level of oversight, this adds an additional layer of quality or compliance control.
In processes where human judgment cannot be completely eliminated but still has to be enhanced, this skill is especially helpful. For instance, AI-enabled nudges can detect the choice points with the highest risk (those more likely to result in human error in judgment) and provide digital prompts to convey either the risk of continuing with a certain decision or/and the advantage of pursuing an alternate decision.
A fund trained its analysts to spend more time analyzing dangerous anomalous transactions using AI capabilities. The AI first ranked potential anomalies according to danger and potential expense. The amount of time analysts spent on anomalies with different levels of risk was tracked using a time tracker tool, and it was discovered that the riskiest anomalies received far too little attention from analysts. The AI then utilized a chess-playing algorithm to "challenge" the analysts' real-time assessments of abnormalities. The AI advised the analyst to take another look if they came to a decision about a risky abnormality too quickly. The company was able to catch a higher percentage of high-risk transaction errors by merging artificial and human intelligence thanks to the fund's ability to hold down analysts' assessments of transaction faults.
Start Your Autonomous Finance Today!
Starting your journey into autonomous finance can be a great way to take control of your finances and improve your financial outcomes. Finance leaders should start by taking the following actions as they think about how and where to introduce these six digital capabilities within finance:
Think on how to enhance the targeted finance processes and/or set of activities, not merely whether they can be improved
Create a roadmap for financial digital technology investments to help determine investment priorities.
To determine whether a specific application of digital technology is appropriate, speak with the organization's IT support partners (such as finance IT, corporate IT, the chief digital officer, and the chief information officer), especially when thinking about more sophisticated digital capabilities that use AI.
With the right approach and mindset, autonomous finance can provide many benefits and help you achieve your financial goals more efficiently and effectively.
So why not start your autonomous finance journey today? Need help on your autonomous finance journey?
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Kitameraki (www.kitameraki.com) is a Technology & Operations boutique consulting firm that provides unique end-to-end solutions to companies throughout Southeast Asia. Kitameraki specializes in Cloud Solution, Analytics & Big Data, and Web & Mobile development.