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Historically, the finance unit is one of the back-office operations and a cost centre for business. The unit reports financial transactions after the fact; it cost the business money, and don’t necessarily add monetary value to the bottom line. In the modern-day world of business, the finance function has transitioned from bean counters in the background; they are now required to transform corporate finance into a streamlined business unit that not only provide efficient and accurate financial information, but also leads the charge for a company’s transition to a data-driven organisation.
CFOs, Accountants, and other finance professionals are sitting on big data! Hence, business intelligence and analytics are already integral part of the finance function. This implies finance professionals are in a prime position of leveraging technology to drive strategic, organisational, and cultural transformation that is needed to succeed with data and analytics. The future of transformational and impactful analytics belongs to Finance.
The new age does not necessarily mean that finance professionals will be replaced by AI; instead, Old-school finance professionals will be replaced by New Age finance professionals who know how to work side-by-side with machine learning (ML), artificial intelligence (AI) and big financial data — Glenn Hopper.
At a time when everyone is talking about digital transformations, automation, machine learning (ML) and artificial intelligence (AI); the finance team is in a prime position of leveraging financial data to help CEO, the Board, senior management, and employees down the line to embrace data in making informed decisions. This starts by bringing visibility, transparency and leveraging digital technologies in dissecting and communicating monthly financial reports, spends with each vendors/supplier, spotlighting revenue generated from individual customers, region, sales channel amongst others.
The old-school narrative that the finance team are bean counters and don’t necessarily add to a company’s bottom line can be eradicated when professionals start expanding value and thinking like a data scientist.
The game charger for the finance team is the application of science and technology in how financial data are collected, analysed, visualised, and communicated to various stakeholders of a company. Digital transformation accelerates the transition from a cost-centre unit to value adding centre. This means finance professionals can spend team analysing and making strategic decisions by identifying Key performance indicators (KPIs) peculiar to their companies that let them predict the future with a high-level accuracy. The old-school narrative that the finance team are bean counters and don’t necessarily add to a company’s bottom line can be eradicated when professionals start expanding value and thinking like a data scientist.
Regardless of the size and stage of your business, the below data science framework (fig. 1) outlines how a product-led companycan build a modern-day finance team and put the company on a path of digital transformation reflecting a data-driven, automated, and streamlined finance operations.
The first approach to solving any problem is to understand and putting things in right context to achieve the right results. Business owners and start-ups founders need to understand and articulate what processes and why in the context of their business model? Attention should be placed in identifying key finance functions such as: expense management, invoicing clients, paying invoices, financial reporting, compliance, procurement etc. Leveraging data science approach for automating finance functions have several advantage including but not limited toreducing human errors, enhance efficiency, and provides deeper insights and patterns into a company financial transactions and activities. The applications of emerging technologies such as machine learning (ML), natural language processing (NLP), and statistical analysis, data science helps streamline processes in areas such as accounting, budgeting, auditing, and financial decision-making. The figure below highlights the data science framework to automating finance functions.
Figure 1: Data Science Methodology for Automating Finance Functions
It is worth mentioning that automation of the finance operations is a series on incremental steps. The little and single steps taken now are what eventually makes the full digital transformation a reality in the long run.
Building a modern-day finance team requires finding and hiring the right talent (the humans). To build a successful finance team, business owners and company leadership need to focus on finding finance professionals with relevant technical and soft skills. Essential soft skills for a modern-day finance team includes but not limited to strong communication skills — communicating financial information to non-finance audiences, problem-solving, interpersonal skills, business savvy. In addition, technical skills such as financial analytics for data-driven decisions using Tableau, Power BI etc, risk management, compliance, financial reporting, data science and forecasting using ML/AI could be added advantage. A business owner, founders or hiring manager can get help from experienced external finance experts when hiring for a skilled position outside of their own expertise.
In finance environment, financial data is the fuel that powers any automation or application of digital technologies such as ML/AI. No matter how good the automation or algorithm; ML does not work without a massive data set to train.The big data collected from the automated financial system can be useful opportunities for the application of data science, ML and AI including but not limited to: predictive analytics — forecasting and recommendation; anomaly detection — fraud prevention; business intelligence and process automation.
In summary, to remain relevant in the information age, finance professionals need to have a heart to learn, embrace continuous upskilling, critical thinking, digital literacy etc. Finance professionals can use financial data and analytics as a strategic force multiplier and value adding for their business.
Written by Abiodun Ajanaku | LinkedIn Profile
Abiodun is a CFO and AI/Machine Learning Expert with domain expertise in Finance. He is on a mission of supporting tech startups to be investment-ready, transition to a going concernand operate at scale. Outside of his day-to-day role as a CFO, Abiodun has built a novel cancer risk predictive system powered by AI/Machine learning techniques. Want to see his AI/ML work?
Know your cancer risk (high or low) HERE:https://cancer-risk-system-6b9a50c7dee8.herokuapp.com/
See his AI/ML publication HERE:https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4885481
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