Financial has risen in sophistication, precision and relevance throughout the last century – yet the operational paradigm of the finance function has surprisingly little changed. It is now time to adopt a more agile approach to decision-making, growth and profitability, as with many corporate operations. With the use of current technology, such as artificial intelligence, automation and machine learning, finance can drive digital change, drive the creation of value in companies and increase performance overall.
Research carried out by the McKinsey Global Institute has shown that 40% of key financial functions – including revenue management, cash disbursement, accounting and operations – are fully automated and another 17% are somewhat automated. Automation operations of this sort will allow financial teams to spend more time on valuable duties such as insight, liquidity and expenditure management, and investment tracking. All of these involve experience, human contact and a good range of tools, techniques and skills such as dynamic planning and prediction, cross-functional collaborating, low-latency dashboards and KPI monitoring.
Further automation and more AI and machine learning will give finance teams speed and flexibility, enhance company resilience and accelerate organisational decision making.
The recent development of the financing function was motivated by the need to improve data openness, transparency and accuracy – and the global pandemic has served as a stimulus for increasing digital tool usage by finance teams and the company as a whole.
Cutting-edge financing teams are moving to more data visualisation tools based on AI, along with automation capability to produce clear, fast and operational business reports. This provides faster user insights, improves productivity, decreases data collecting time and encourages more concentrated (and faster) business interactions. The financial system of AI drives the performance of businesses.
Building components that transform finance
The financing future is more digital and considerably more automated. Two items are needed: First of all, financial leaders should build a culture in which teams spend more time understanding data and making decisions easier than simply gathering data. Second, the implementation of an integrated AI system that leverages data aggregation, data viewing, process automation, KPI reporting, scenario simulation and advanced analytics should be considered.
An AI system can provide the possibility to monitor financial operations in real-time, for example budgeting and predicting, and managing working capital – and can automate the generation of insight-driven warning indicators. Changes in culture and technology should match the company’s strategic vision and long-term objectives.
Budgeting and forecasting: These processes are complex and are based on the heritage of corporate practises that respond to questions such as “How do we do so today?” Or “What is our anticipated cash flow from the following three years’ operations?” More digital and automated finance should be revised, with flexibility and intelligence as well as planning and forecasting. In order to accomplish so, the barriers between finance, operations and strategy must be breached. And it will enable the company to provide insights and answer queries like ‘How do we do today?’ and ‘What measures can we use now to track our investments?’
The financial team should help in holistic and ongoing planning, leading to an improvement in visibility and decision making. This planning should also contain modelling capability in order to get these results, which can test various assumptions that include revenue fluctuations and/or costs of products sold from a regional, product portfolio, customer and/or sales viewpoint.
Here is an example of financial automation in action: A worldwide clothing firm for $5+ billion sought a microscopy of cash flows, which would properly plan and assess one of its business units’ performance. DV Nation has produced an income-forming solution that reliably predicts the revenue of an industry leader for the next four quarters while delivering impartial and evidence-backed inferences and suggested interventions, simulated to show how the results would alter. It so enhanced the organization’s decision making that end-users accepted the model in the first six months of its financial planning and analysis. This initiative generated stakeholders’ confidence that an AI model could predict revenues with greater precision than professional financing experts with extensive experience in the garment sector.
Work Capital Management: Management of the cash required for day-to-day operations by local nuances and malaligned teams is complex and inefficient. In receivables, payables and inventory the working capital analysis should take into account. Working capital models of the next generation need a more effective cash management process driven by modern digital technologies which enhance machine learning and automation. DV Nation helps businesses develop AI enhanced working capital frameworks, combining a hybrid approach.
For example, a European manufacturer of automation and robotics had a fragmented invoicing and collecting procedure which proved to be a barrier to future growth. DV Nation helped build a working capital plan and adopted best industry standards for the order-to-cash process in order to tackle this difficulty. The collections of accounts receivable have greatly increased by the application of machine-learning algorithms to recommend ideal customer/transaction collection techniques. In addition to this, the firm reduced work capital by automatic recalls, more accurate invoices and quicker dispute management.
AI on the Journey of Finance
So how should financial leaders start AI as part of the transformation process? You can feel awful: A single-dach AI solution requires the combination of cognitive, Big Databases, machine learning and automation skills. It can be helpful to use a step by step method.
The first stage in using AI in order to transform the financial department and establish it for long term success is data consolidation. Most firms have at least one ERP system that tracks orders and transactions, invoices, payments, stock details, information on the cost centre, etc. In general, companies utilise several separate systems to track specialised data for which ERP systems do not provide an account, for example product line scrap. It is easy to collect considerable information from consolidating and harmonising data sources, which otherwise will be difficult to use for financial planning and analysis. Data might be unpleasant, slow and laboured during consolidation.
The next step is to develop the algorithm to find relationships between the data sets created by data consolidation. A trained algorithm allows financial managers to model numerous scenarios using predictive modelling and stress testing and allows them to understand the financial and operational impact of externalities on their firm.
In order to verify the results of an algorithm and its predictive capabilities, firms must also define clearly, detailed and quantitative key performance indicators (CPIs). In the end, KPIs increase the transparency of the algorithm and may guarantee cross-cutting collaboration, depending on the insight gained.
Finance management has the right and the responsibility of an organisation to ask how a company’s day-to-day business generates value. With using AI, financial executives cannot simply synthesise such information more quickly, but also support the use of AI-based predictive models to harness their potential in strategic decisions.
Real Insights – Artificial Intelligence
DV Nation, There will only be greater pressure for top-line growth, cost optimization and alignment with corporate strategy – and expectations that finance takes a technologically advanced, cutting-edge approach into consideration. This is the way automation and artificial intelligence are present now. Using AI as part of the process for financial transformation, businesses can create more precise and accurate reports in real-time, boost prediction accuracy, optimize resources and eliminate manual interventions. By adopting and implementing an AI-enhanced vision and creating a new culture based on insight, organize.
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